Project - Alertness Detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b02061b1",
      "metadata": {
        "id": "b02061b1"
      },
      "source": [
        "# Predicting Alertness Of The Driver"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0xJmLimBJZ8b",
      "metadata": {
        "id": "0xJmLimBJZ8b"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ssjJ7iAi34jM",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssjJ7iAi34jM",
        "outputId": "b933ba00-ef5f-4eab-fb9c-69760c710ebc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: opendatasets in /usr/local/lib/python3.10/dist-packages (0.1.22)\n",
            "Requirement already satisfied: jovian in /usr/local/lib/python3.10/dist-packages (0.2.47)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.65.0)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.5.13)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from jovian) (2.27.1)\n",
            "Requirement already satisfied: uuid in /usr/local/lib/python3.10/dist-packages (from jovian) (1.30)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from jovian) (6.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2022.12.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->jovian) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->jovian) (3.4)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install opendatasets jovian"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fcbbcdab",
      "metadata": {
        "id": "fcbbcdab"
      },
      "source": [
        "The goal o this project is to predict whether a driver is alert or not on the basis of different physiological, environmental, and vechicular data. The columns p1 to p7 indicate physiological data, E1 to E11 indicate environmental data, and V1 to v11 indicate vehicular data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50b2ab39",
      "metadata": {
        "id": "50b2ab39"
      },
      "outputs": [],
      "source": [
        "# Let's import all our fundamental libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import jovian\n",
        "import os\n",
        "import opendatasets as od"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "02ef07a5",
      "metadata": {
        "id": "02ef07a5"
      },
      "source": [
        "## Downloading the Data\n",
        "\n",
        "Dataset source: https://www.kaggle.com/c/stayalert/data\n",
        "\n",
        "\n",
        "We'll use the [`opendatasets` library](https://github.com/JovianML/opendatasets) to download the data from Kaggle directly within Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c33a7da3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c33a7da3",
        "outputId": "3f91de92-81ae-40a4-959a-d4210fcfde1f",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Skipping, found downloaded files in \"./stayalert\" (use force=True to force download)\n"
          ]
        }
      ],
      "source": [
        "od.download('https://www.kaggle.com/c/stayalert/data')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "49b99f37",
      "metadata": {
        "id": "49b99f37"
      },
      "source": [
        "The dataset is downloaded and extracted to the folder `stayalert`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bf71b1d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bf71b1d",
        "outputId": "038f559c-eb6f-4931-b31a-6163617666fe",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['fordTrain.csv', 'fordTest.csv', 'Solution.csv', 'example_submission.csv']"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.listdir('stayalert')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9dccd1d3",
      "metadata": {
        "id": "9dccd1d3"
      },
      "source": [
        "Lets load `fordTrain.csv` file into pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "125dfbc5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "125dfbc5",
        "outputId": "7127cfc0-e425-458c-da3e-ea47e7fa8ef8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-80758698-91b6-4c1d-a9a1-0d413518d750\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>IsAlert</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>...</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34.7406</td>\n",
              "      <td>9.84593</td>\n",
              "      <td>1400</td>\n",
              "      <td>42.8571</td>\n",
              "      <td>0.290601</td>\n",
              "      <td>572</td>\n",
              "      <td>104.8950</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175</td>\n",
              "      <td>752</td>\n",
              "      <td>5.99375</td>\n",
              "      <td>0</td>\n",
              "      <td>2005</td>\n",
              "      <td>0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>14.8004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>34.4215</td>\n",
              "      <td>13.41120</td>\n",
              "      <td>1400</td>\n",
              "      <td>42.8571</td>\n",
              "      <td>0.290601</td>\n",
              "      <td>572</td>\n",
              "      <td>104.8950</td>\n",
              "      <td>...</td>\n",
              "      <td>0.455</td>\n",
              "      <td>752</td>\n",
              "      <td>5.99375</td>\n",
              "      <td>0</td>\n",
              "      <td>2007</td>\n",
              "      <td>0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>14.7729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>34.3447</td>\n",
              "      <td>15.18520</td>\n",
              "      <td>1400</td>\n",
              "      <td>42.8571</td>\n",
              "      <td>0.290601</td>\n",
              "      <td>576</td>\n",
              "      <td>104.1670</td>\n",
              "      <td>...</td>\n",
              "      <td>0.280</td>\n",
              "      <td>752</td>\n",
              "      <td>5.99375</td>\n",
              "      <td>0</td>\n",
              "      <td>2011</td>\n",
              "      <td>0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>14.7736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>34.3421</td>\n",
              "      <td>8.84696</td>\n",
              "      <td>1400</td>\n",
              "      <td>42.8571</td>\n",
              "      <td>0.290601</td>\n",
              "      <td>576</td>\n",
              "      <td>104.1670</td>\n",
              "      <td>...</td>\n",
              "      <td>0.070</td>\n",
              "      <td>752</td>\n",
              "      <td>5.99375</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>14.7667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>34.3322</td>\n",
              "      <td>14.69940</td>\n",
              "      <td>1400</td>\n",
              "      <td>42.8571</td>\n",
              "      <td>0.290601</td>\n",
              "      <td>576</td>\n",
              "      <td>104.1670</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175</td>\n",
              "      <td>752</td>\n",
              "      <td>5.99375</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>0</td>\n",
              "      <td>13.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>14.7757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604324</th>\n",
              "      <td>510</td>\n",
              "      <td>1194</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0051</td>\n",
              "      <td>10.13240</td>\n",
              "      <td>800</td>\n",
              "      <td>75.0000</td>\n",
              "      <td>0.081731</td>\n",
              "      <td>680</td>\n",
              "      <td>88.2353</td>\n",
              "      <td>...</td>\n",
              "      <td>0.280</td>\n",
              "      <td>240</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1801</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>15.8113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604325</th>\n",
              "      <td>510</td>\n",
              "      <td>1195</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0393</td>\n",
              "      <td>12.45040</td>\n",
              "      <td>800</td>\n",
              "      <td>75.0000</td>\n",
              "      <td>0.081731</td>\n",
              "      <td>680</td>\n",
              "      <td>88.2353</td>\n",
              "      <td>...</td>\n",
              "      <td>0.280</td>\n",
              "      <td>240</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1801</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>15.8018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604326</th>\n",
              "      <td>510</td>\n",
              "      <td>1196</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0762</td>\n",
              "      <td>10.06180</td>\n",
              "      <td>800</td>\n",
              "      <td>75.0000</td>\n",
              "      <td>0.081731</td>\n",
              "      <td>680</td>\n",
              "      <td>88.2353</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175</td>\n",
              "      <td>240</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1800</td>\n",
              "      <td>0</td>\n",
              "      <td>31.3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>15.8120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604327</th>\n",
              "      <td>510</td>\n",
              "      <td>1197</td>\n",
              "      <td>1</td>\n",
              "      <td>32.1154</td>\n",
              "      <td>17.84500</td>\n",
              "      <td>800</td>\n",
              "      <td>75.0000</td>\n",
              "      <td>0.081731</td>\n",
              "      <td>680</td>\n",
              "      <td>88.2353</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175</td>\n",
              "      <td>240</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1800</td>\n",
              "      <td>0</td>\n",
              "      <td>31.3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>15.8270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604328</th>\n",
              "      <td>510</td>\n",
              "      <td>1198</td>\n",
              "      <td>1</td>\n",
              "      <td>32.1154</td>\n",
              "      <td>17.84500</td>\n",
              "      <td>800</td>\n",
              "      <td>75.0000</td>\n",
              "      <td>0.081731</td>\n",
              "      <td>680</td>\n",
              "      <td>88.2353</td>\n",
              "      <td>...</td>\n",
              "      <td>0.175</td>\n",
              "      <td>240</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1800</td>\n",
              "      <td>0</td>\n",
              "      <td>31.3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>15.8270</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>604329 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80758698-91b6-4c1d-a9a1-0d413518d750')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80758698-91b6-4c1d-a9a1-0d413518d750 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80758698-91b6-4c1d-a9a1-0d413518d750');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        TrialID  ObsNum  IsAlert       P1        P2    P3       P4        P5  \\\n",
              "0             0       0        0  34.7406   9.84593  1400  42.8571  0.290601   \n",
              "1             0       1        0  34.4215  13.41120  1400  42.8571  0.290601   \n",
              "2             0       2        0  34.3447  15.18520  1400  42.8571  0.290601   \n",
              "3             0       3        0  34.3421   8.84696  1400  42.8571  0.290601   \n",
              "4             0       4        0  34.3322  14.69940  1400  42.8571  0.290601   \n",
              "...         ...     ...      ...      ...       ...   ...      ...       ...   \n",
              "604324      510    1194        1  32.0051  10.13240   800  75.0000  0.081731   \n",
              "604325      510    1195        1  32.0393  12.45040   800  75.0000  0.081731   \n",
              "604326      510    1196        1  32.0762  10.06180   800  75.0000  0.081731   \n",
              "604327      510    1197        1  32.1154  17.84500   800  75.0000  0.081731   \n",
              "604328      510    1198        1  32.1154  17.84500   800  75.0000  0.081731   \n",
              "\n",
              "         P6        P7  ...     V2   V3       V4  V5    V6  V7    V8  V9  V10  \\\n",
              "0       572  104.8950  ...  0.175  752  5.99375   0  2005   0  13.4   0    4   \n",
              "1       572  104.8950  ...  0.455  752  5.99375   0  2007   0  13.4   0    4   \n",
              "2       576  104.1670  ...  0.280  752  5.99375   0  2011   0  13.4   0    4   \n",
              "3       576  104.1670  ...  0.070  752  5.99375   0  2015   0  13.4   0    4   \n",
              "4       576  104.1670  ...  0.175  752  5.99375   0  2017   0  13.4   0    4   \n",
              "...     ...       ...  ...    ...  ...      ...  ..   ...  ..   ...  ..  ...   \n",
              "604324  680   88.2353  ...  0.280  240  3.01875   0  1801   0  30.4   0    4   \n",
              "604325  680   88.2353  ...  0.280  240  3.01875   0  1801   0  30.4   0    4   \n",
              "604326  680   88.2353  ...  0.175  240  3.01875   0  1800   0  31.3   0    4   \n",
              "604327  680   88.2353  ...  0.175  240  3.01875   0  1800   0  31.3   0    4   \n",
              "604328  680   88.2353  ...  0.175  240  3.01875   0  1800   0  31.3   0    4   \n",
              "\n",
              "            V11  \n",
              "0       14.8004  \n",
              "1       14.7729  \n",
              "2       14.7736  \n",
              "3       14.7667  \n",
              "4       14.7757  \n",
              "...         ...  \n",
              "604324  15.8113  \n",
              "604325  15.8018  \n",
              "604326  15.8120  \n",
              "604327  15.8270  \n",
              "604328  15.8270  \n",
              "\n",
              "[604329 rows x 33 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df = pd.read_csv('stayalert/fordTrain.csv')\n",
        "train_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fa55eaa9",
      "metadata": {
        "id": "fa55eaa9"
      },
      "source": [
        "Lets load `fordTest.csv` file into pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f0c8a738",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "f0c8a738",
        "outputId": "5c7a375d-c825-4360-dc21-e55ea41ab9a3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-140d332c-c04a-4b10-a847-33c158531c14\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>IsAlert</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>...</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>38.4294</td>\n",
              "      <td>10.94350</td>\n",
              "      <td>1000</td>\n",
              "      <td>60.0000</td>\n",
              "      <td>0.302277</td>\n",
              "      <td>508</td>\n",
              "      <td>118.1100</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>4.50625</td>\n",
              "      <td>0</td>\n",
              "      <td>2127</td>\n",
              "      <td>0</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>16.1937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>38.3609</td>\n",
              "      <td>15.32120</td>\n",
              "      <td>1000</td>\n",
              "      <td>60.0000</td>\n",
              "      <td>0.302277</td>\n",
              "      <td>508</td>\n",
              "      <td>118.1100</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>4.50625</td>\n",
              "      <td>0</td>\n",
              "      <td>2127</td>\n",
              "      <td>0</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>16.1744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>?</td>\n",
              "      <td>38.2342</td>\n",
              "      <td>11.51400</td>\n",
              "      <td>1000</td>\n",
              "      <td>60.0000</td>\n",
              "      <td>0.302277</td>\n",
              "      <td>508</td>\n",
              "      <td>118.1100</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>255</td>\n",
              "      <td>4.50625</td>\n",
              "      <td>0</td>\n",
              "      <td>2131</td>\n",
              "      <td>0</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>16.1602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>?</td>\n",
              "      <td>37.9304</td>\n",
              "      <td>12.26150</td>\n",
              "      <td>1000</td>\n",
              "      <td>60.0000</td>\n",
              "      <td>0.302277</td>\n",
              "      <td>508</td>\n",
              "      <td>118.1100</td>\n",
              "      <td>...</td>\n",
              "      <td>0.07</td>\n",
              "      <td>255</td>\n",
              "      <td>4.50625</td>\n",
              "      <td>0</td>\n",
              "      <td>2131</td>\n",
              "      <td>0</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>16.1725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>?</td>\n",
              "      <td>37.8085</td>\n",
              "      <td>12.36660</td>\n",
              "      <td>1000</td>\n",
              "      <td>60.0000</td>\n",
              "      <td>0.302277</td>\n",
              "      <td>504</td>\n",
              "      <td>119.0480</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>255</td>\n",
              "      <td>4.50625</td>\n",
              "      <td>0</td>\n",
              "      <td>2136</td>\n",
              "      <td>0</td>\n",
              "      <td>17.6</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>16.1459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120835</th>\n",
              "      <td>99</td>\n",
              "      <td>1206</td>\n",
              "      <td>?</td>\n",
              "      <td>37.3798</td>\n",
              "      <td>17.40260</td>\n",
              "      <td>892</td>\n",
              "      <td>67.2646</td>\n",
              "      <td>0.131030</td>\n",
              "      <td>752</td>\n",
              "      <td>79.7872</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>255</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>658</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.6850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120836</th>\n",
              "      <td>99</td>\n",
              "      <td>1207</td>\n",
              "      <td>?</td>\n",
              "      <td>37.1653</td>\n",
              "      <td>5.37419</td>\n",
              "      <td>892</td>\n",
              "      <td>67.2646</td>\n",
              "      <td>0.131030</td>\n",
              "      <td>752</td>\n",
              "      <td>79.7872</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>255</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>657</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.6840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120837</th>\n",
              "      <td>99</td>\n",
              "      <td>1208</td>\n",
              "      <td>?</td>\n",
              "      <td>36.9131</td>\n",
              "      <td>9.26657</td>\n",
              "      <td>892</td>\n",
              "      <td>67.2646</td>\n",
              "      <td>0.131030</td>\n",
              "      <td>752</td>\n",
              "      <td>79.7872</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>255</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>655</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.6487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120838</th>\n",
              "      <td>99</td>\n",
              "      <td>1209</td>\n",
              "      <td>?</td>\n",
              "      <td>36.6297</td>\n",
              "      <td>10.41710</td>\n",
              "      <td>892</td>\n",
              "      <td>67.2646</td>\n",
              "      <td>0.131030</td>\n",
              "      <td>752</td>\n",
              "      <td>79.7872</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>240</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>657</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.6314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120839</th>\n",
              "      <td>99</td>\n",
              "      <td>1210</td>\n",
              "      <td>?</td>\n",
              "      <td>36.6297</td>\n",
              "      <td>10.41710</td>\n",
              "      <td>892</td>\n",
              "      <td>67.2646</td>\n",
              "      <td>0.131030</td>\n",
              "      <td>752</td>\n",
              "      <td>79.7872</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>240</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>657</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.6314</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120840 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-140d332c-c04a-4b10-a847-33c158531c14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-140d332c-c04a-4b10-a847-33c158531c14 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-140d332c-c04a-4b10-a847-33c158531c14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        TrialID  ObsNum IsAlert       P1        P2    P3       P4        P5  \\\n",
              "0             0       0       ?  38.4294  10.94350  1000  60.0000  0.302277   \n",
              "1             0       1       ?  38.3609  15.32120  1000  60.0000  0.302277   \n",
              "2             0       2       ?  38.2342  11.51400  1000  60.0000  0.302277   \n",
              "3             0       3       ?  37.9304  12.26150  1000  60.0000  0.302277   \n",
              "4             0       4       ?  37.8085  12.36660  1000  60.0000  0.302277   \n",
              "...         ...     ...     ...      ...       ...   ...      ...       ...   \n",
              "120835       99    1206       ?  37.3798  17.40260   892  67.2646  0.131030   \n",
              "120836       99    1207       ?  37.1653   5.37419   892  67.2646  0.131030   \n",
              "120837       99    1208       ?  36.9131   9.26657   892  67.2646  0.131030   \n",
              "120838       99    1209       ?  36.6297  10.41710   892  67.2646  0.131030   \n",
              "120839       99    1210       ?  36.6297  10.41710   892  67.2646  0.131030   \n",
              "\n",
              "         P6        P7  ...    V2   V3       V4  V5    V6  V7    V8  V9  V10  \\\n",
              "0       508  118.1100  ...  0.00  255  4.50625   0  2127   0  17.6   0    4   \n",
              "1       508  118.1100  ...  0.00  255  4.50625   0  2127   0  17.6   0    4   \n",
              "2       508  118.1100  ...  0.07  255  4.50625   0  2131   0  17.6   0    4   \n",
              "3       508  118.1100  ...  0.07  255  4.50625   0  2131   0  17.6   0    4   \n",
              "4       504  119.0480  ...  0.00  255  4.50625   0  2136   0  17.6   0    4   \n",
              "...     ...       ...  ...   ...  ...      ...  ..   ...  ..   ...  ..  ...   \n",
              "120835  752   79.7872  ... -0.07  255  0.00000   0   658   0   0.0   0    1   \n",
              "120836  752   79.7872  ... -0.07  255  0.00000   0   657   0   0.0   0    1   \n",
              "120837  752   79.7872  ... -0.07  255  0.00000   0   655   0   0.0   0    1   \n",
              "120838  752   79.7872  ... -0.07  240  0.00000   0   657   0   0.0   0    1   \n",
              "120839  752   79.7872  ... -0.07  240  0.00000   0   657   0   0.0   0    1   \n",
              "\n",
              "            V11  \n",
              "0       16.1937  \n",
              "1       16.1744  \n",
              "2       16.1602  \n",
              "3       16.1725  \n",
              "4       16.1459  \n",
              "...         ...  \n",
              "120835  16.6850  \n",
              "120836  16.6840  \n",
              "120837  16.6487  \n",
              "120838  16.6314  \n",
              "120839  16.6314  \n",
              "\n",
              "[120840 rows x 33 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_df = pd.read_csv('stayalert/fordTest.csv')\n",
        "test_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57c6fdca",
      "metadata": {
        "id": "57c6fdca"
      },
      "source": [
        "Lets load `Solution.csv` file into pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e0878fe4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "e0878fe4",
        "outputId": "af3a9ebe-a63c-4191-f6c1-78489195477c",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0db05298-fcc2-49b4-8f56-5e1c8457bf9e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Public</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Public</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120835</th>\n",
              "      <td>99</td>\n",
              "      <td>1206</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120836</th>\n",
              "      <td>99</td>\n",
              "      <td>1207</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120837</th>\n",
              "      <td>99</td>\n",
              "      <td>1208</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120838</th>\n",
              "      <td>99</td>\n",
              "      <td>1209</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120839</th>\n",
              "      <td>99</td>\n",
              "      <td>1210</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120840 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0db05298-fcc2-49b4-8f56-5e1c8457bf9e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0db05298-fcc2-49b4-8f56-5e1c8457bf9e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0db05298-fcc2-49b4-8f56-5e1c8457bf9e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        TrialID  ObsNum  Prediction Indicator\n",
              "0             0       0           1    Public\n",
              "1             0       1           1    Public\n",
              "2             0       2           1   Private\n",
              "3             0       3           1   Private\n",
              "4             0       4           1   Private\n",
              "...         ...     ...         ...       ...\n",
              "120835       99    1206           1   Private\n",
              "120836       99    1207           1   Private\n",
              "120837       99    1208           1   Private\n",
              "120838       99    1209           1   Private\n",
              "120839       99    1210           1   Private\n",
              "\n",
              "[120840 rows x 4 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "solution_df = pd.read_csv('stayalert/Solution.csv')\n",
        "solution_df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ec741aa",
      "metadata": {
        "id": "7ec741aa"
      },
      "source": [
        "Here, our training dataset contains 604329  rows and 33 columns. The dataset contains mostly numeric columns. Our objective is to create a model to predict the value in the column `IsAlert`.\n",
        "\n",
        "Let's us check the data types and missing values in the various `columns` in the training dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d1dcf804",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1dcf804",
        "outputId": "c790beef-b045-4544-a13a-42a597cd79e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 604329 entries, 0 to 604328\n",
            "Data columns (total 33 columns):\n",
            " #   Column   Non-Null Count   Dtype  \n",
            "---  ------   --------------   -----  \n",
            " 0   TrialID  604329 non-null  int64  \n",
            " 1   ObsNum   604329 non-null  int64  \n",
            " 2   IsAlert  604329 non-null  int64  \n",
            " 3   P1       604329 non-null  float64\n",
            " 4   P2       604329 non-null  float64\n",
            " 5   P3       604329 non-null  int64  \n",
            " 6   P4       604329 non-null  float64\n",
            " 7   P5       604329 non-null  float64\n",
            " 8   P6       604329 non-null  int64  \n",
            " 9   P7       604329 non-null  float64\n",
            " 10  P8       604329 non-null  int64  \n",
            " 11  E1       604329 non-null  float64\n",
            " 12  E2       604329 non-null  float64\n",
            " 13  E3       604329 non-null  int64  \n",
            " 14  E4       604329 non-null  int64  \n",
            " 15  E5       604329 non-null  float64\n",
            " 16  E6       604329 non-null  int64  \n",
            " 17  E7       604329 non-null  int64  \n",
            " 18  E8       604329 non-null  int64  \n",
            " 19  E9       604329 non-null  int64  \n",
            " 20  E10      604329 non-null  int64  \n",
            " 21  E11      604329 non-null  float64\n",
            " 22  V1       604329 non-null  float64\n",
            " 23  V2       604329 non-null  float64\n",
            " 24  V3       604329 non-null  int64  \n",
            " 25  V4       604329 non-null  float64\n",
            " 26  V5       604329 non-null  int64  \n",
            " 27  V6       604329 non-null  int64  \n",
            " 28  V7       604329 non-null  int64  \n",
            " 29  V8       604329 non-null  float64\n",
            " 30  V9       604329 non-null  int64  \n",
            " 31  V10      604329 non-null  int64  \n",
            " 32  V11      604329 non-null  float64\n",
            "dtypes: float64(14), int64(19)\n",
            "memory usage: 152.2 MB\n"
          ]
        }
      ],
      "source": [
        "train_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2d90819",
      "metadata": {
        "id": "b2d90819"
      },
      "source": [
        "We can clearly see that the total rows are 604329, and none of the columns have missing value. Hence, We will not have to perform the section of Imputing null values for this dataset respectively.\n",
        "\n",
        "Furthermore, the different datatypes in the `training` datasets are mainly float64, and int64."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9508967c",
      "metadata": {
        "id": "9508967c"
      },
      "source": [
        "Lets check our `testing` set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "183c28ca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "183c28ca",
        "outputId": "08cd1d6f-1a9b-409f-f4d5-5b16275c805d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120840 entries, 0 to 120839\n",
            "Data columns (total 33 columns):\n",
            " #   Column   Non-Null Count   Dtype  \n",
            "---  ------   --------------   -----  \n",
            " 0   TrialID  120840 non-null  int64  \n",
            " 1   ObsNum   120840 non-null  int64  \n",
            " 2   IsAlert  120840 non-null  object \n",
            " 3   P1       120840 non-null  float64\n",
            " 4   P2       120840 non-null  float64\n",
            " 5   P3       120840 non-null  int64  \n",
            " 6   P4       120840 non-null  float64\n",
            " 7   P5       120840 non-null  float64\n",
            " 8   P6       120840 non-null  int64  \n",
            " 9   P7       120840 non-null  float64\n",
            " 10  P8       120840 non-null  int64  \n",
            " 11  E1       120840 non-null  float64\n",
            " 12  E2       120840 non-null  float64\n",
            " 13  E3       120840 non-null  int64  \n",
            " 14  E4       120840 non-null  int64  \n",
            " 15  E5       120840 non-null  float64\n",
            " 16  E6       120840 non-null  int64  \n",
            " 17  E7       120840 non-null  int64  \n",
            " 18  E8       120840 non-null  int64  \n",
            " 19  E9       120840 non-null  int64  \n",
            " 20  E10      120840 non-null  int64  \n",
            " 21  E11      120840 non-null  float64\n",
            " 22  V1       120840 non-null  float64\n",
            " 23  V2       120840 non-null  float64\n",
            " 24  V3       120840 non-null  int64  \n",
            " 25  V4       120840 non-null  float64\n",
            " 26  V5       120840 non-null  int64  \n",
            " 27  V6       120840 non-null  int64  \n",
            " 28  V7       120840 non-null  int64  \n",
            " 29  V8       120840 non-null  float64\n",
            " 30  V9       120840 non-null  int64  \n",
            " 31  V10      120840 non-null  int64  \n",
            " 32  V11      120840 non-null  float64\n",
            "dtypes: float64(14), int64(18), object(1)\n",
            "memory usage: 30.4+ MB\n"
          ]
        }
      ],
      "source": [
        "test_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52ef6fae",
      "metadata": {
        "id": "52ef6fae"
      },
      "source": [
        "We can also see that there are no missing values in any columns. Similarly, the datatypes are mainly float64 and int64."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f97016fe",
      "metadata": {
        "id": "f97016fe"
      },
      "source": [
        "# Exploratory Data Analysis and Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "28ec9d29",
      "metadata": {
        "id": "28ec9d29"
      },
      "source": [
        "It is always a good practice to  visually represent the relation between other feature columns and target columns. Lets explore the data from feature and target columns using different libraries like matplotlib, seaborn, and plotly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "166002f8",
      "metadata": {
        "id": "166002f8"
      },
      "outputs": [],
      "source": [
        "# Lets import our data visualization libraries\n",
        "\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "%matplotlib inline\n",
        "\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', 150)\n",
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 14\n",
        "matplotlib.rcParams['figure.figsize'] = (10, 6)\n",
        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Tdd_kYme6fPe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "Tdd_kYme6fPe",
        "outputId": "9cf6e69a-3e80-45b0-d517-d5361ae49004"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Presenting the total outcomes of IsAlert target column\n",
        "sns.displot(x = train_df.IsAlert,bins = 2, color = 'blue');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2yz-sIijjyc3",
      "metadata": {
        "id": "2yz-sIijjyc3"
      },
      "source": [
        "Here, we can clearly see that the total number of values 0 (when the driver is not alert) is close to 250,000 whereas the total number of values 1 ( when the driver is  alert) is about 350,000 respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5cdf220",
      "metadata": {
        "id": "d5cdf220"
      },
      "source": [
        "<b> According to the dataset provided in Kaggle, the models that make less use of physiological features are said to be of additional Interest. </b>\n",
        "\n",
        "So, from all the feature columns, lets check which columns has the highest average, and compare them with each other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "097db65e",
      "metadata": {
        "id": "097db65e"
      },
      "outputs": [],
      "source": [
        "# Using traditional for loop way to find out which Psychological feature column has the maximum mean\n",
        "count = 0\n",
        "coln = ''\n",
        "for i in train_df[train_df.columns.tolist()[3:10]]:\n",
        "  mean_value = train_df[i].mean()\n",
        "  if (mean_value > count):\n",
        "    count = mean_value\n",
        "    coln = i"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "abb57877",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "abb57877",
        "outputId": "0bfc66ac-0541-4b13-f423-0f6e76db52a1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'P3'"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "coln"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "s_7Khl0RKH8X",
      "metadata": {
        "id": "s_7Khl0RKH8X"
      },
      "source": [
        "Hence, the Psychological feature column with highest mean is P3. Thus, we will compare this particular feature column with the similar vechicular and environmental feature columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "M5fZ0nzfKFdN",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5fZ0nzfKFdN",
        "outputId": "3e972fc6-18a9-446e-d722-3b4f193b5bbb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "E1      10.512332\n",
              "E2     102.790045\n",
              "E3       0.290565\n",
              "E4      -4.230136\n",
              "E5       0.016262\n",
              "E6     358.674738\n",
              "E7       1.757296\n",
              "E8       1.383058\n",
              "E9       0.876787\n",
              "E10     63.311256\n",
              "E11      1.315265\n",
              "dtype: float64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets see which Environmental feature column has the highest mean\n",
        "train_df[train_df.columns.tolist()[11:22]].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-zKgsDvoLmg3",
      "metadata": {
        "id": "-zKgsDvoLmg3"
      },
      "source": [
        "So, from the Environmental feature columns, we will use column E6 for comparision."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "kaTrLtMyKHr_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaTrLtMyKHr_",
        "outputId": "e188619a-e208-46ce-a724-c07c0c31e755"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "V1       76.965412\n",
              "V2       -0.037710\n",
              "V3      573.786433\n",
              "V4       19.961030\n",
              "V5        0.179814\n",
              "V6     1715.688383\n",
              "V7        0.000000\n",
              "V8       12.710354\n",
              "V9        0.000000\n",
              "V10       3.312257\n",
              "V11      11.668277\n",
              "dtype: float64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets see which Vehicular feature column has the highest mean\n",
        "train_df[train_df.columns.tolist()[22:]].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c9a146c7",
      "metadata": {
        "id": "c9a146c7"
      },
      "source": [
        "So, from the Vehicular feature columns, we will use column V6 for comparision."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5214ba7f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "5214ba7f",
        "outputId": "d62bc8fa-7733-47c0-f258-ad9b940faf46"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 582.375x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.displot(data = train_df, x = 'E6', hue = 'IsAlert', bins = 50);\n",
        "plt.title('Environmental Feature Column (E6)');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QMTjm_tjlLuI",
      "metadata": {
        "id": "QMTjm_tjlLuI"
      },
      "source": [
        "In the E6 Feature column from the above histogram, we can see that 1112 times the value of E6 ranging from 260-269 have the IsAlert column's value to be 0.\n",
        "\n",
        "Similarly, we can also see that the above distribution is normal. In more than 120,000 rows when E6 columns value ranged from 360 -369, the driver was alert which is the highest number in the entire dataset for this particular feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "HGLLpVeSPd1P",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "HGLLpVeSPd1P",
        "outputId": "edbd1424-22d6-4e75-b0f4-f2298ecf6710"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# P3 vs V6 (using 2000 random samples out of more than 600,000 values)\n",
        "sns.scatterplot(x = 'P3', y = 'V6', data = train_df[['P3','V6']].sample(2000, random_state = 42), hue = train_df.IsAlert)\n",
        "plt.title('P3 v/s V6');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4b16fde7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "4b16fde7",
        "outputId": "c74ea68a-ba22-4e7b-9d6a-1debc7953396"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 582.375x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# visualizing the distribution of medical charges in connection with region\n",
        "fig = sns.displot(data = train_df,\n",
        "                   x='P3',\n",
        "                   hue='IsAlert',\n",
        "                   bins=50)\n",
        "plt.title('Psychological Feature Column (P3)');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Zsqnx1sKnuDI",
      "metadata": {
        "id": "Zsqnx1sKnuDI"
      },
      "source": [
        "From the above graph, we can make the following observations:\n",
        "\n",
        "1. Most of the time the psychological feature P3 value was less than 1500.\n",
        "2. For the feature P3, the total counts of  Psylogical value of the driver was higher when driver was alert."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "l__IMuX2uVy0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l__IMuX2uVy0",
        "outputId": "70aff499-1ea2-41c9-f2e9-b5870d787c27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.005167867220705532"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.IsAlert.corr(train_df.P3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "_QwM6L2uuuCj",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QwM6L2uuuCj",
        "outputId": "252fa932-e650-4a30-8157-3b73c19d8944"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "-0.18919786734413285"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.IsAlert.corr(train_df.E6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "qe6aH-KVut4I",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qe6aH-KVut4I",
        "outputId": "b2ffa3c1-1977-4858-d863-f3e655e7faa5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "-0.244150113935766"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.IsAlert.corr(train_df.V6)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "zwTUIqdEfHSP",
      "metadata": {
        "id": "zwTUIqdEfHSP"
      },
      "source": [
        "Lets explain the correlation of feature columns `P3`, `E6`, and `V6`.\n",
        "\n",
        "The absolute value of a correlation determines the strength. If a correlation between two variables is -1 or +1, the absolute value is 1, which indicates the perfect linear relationship.\n",
        "\n",
        "Similarly, the sign of correlation coefficient determines the direction of the relationship.  ([source](https://www.cuemath.com/data/how-to-calculate-correlation-coefficient/))\n",
        "\n",
        "Thus, among the three feature columns, `V6` column is the most strongly correlated with `IsAlert` target column. However, the relationship is negative."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9d5656a7",
      "metadata": {
        "id": "9d5656a7"
      },
      "source": [
        "# Preprocessing and Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50f92a81",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "50f92a81",
        "outputId": "847c48d9-0498-4fe0-9e01-483d6c53f7d5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-96cfce73-5cd4-48c3-911b-1a82e4570749\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>IsAlert</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.0</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.0</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.0</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>250.167657</td>\n",
              "      <td>603.841765</td>\n",
              "      <td>0.578799</td>\n",
              "      <td>35.449020</td>\n",
              "      <td>11.996525</td>\n",
              "      <td>1026.671035</td>\n",
              "      <td>64.061965</td>\n",
              "      <td>0.178923</td>\n",
              "      <td>845.384610</td>\n",
              "      <td>77.887628</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.512332</td>\n",
              "      <td>102.790045</td>\n",
              "      <td>0.290565</td>\n",
              "      <td>-4.230136</td>\n",
              "      <td>0.016262</td>\n",
              "      <td>358.674738</td>\n",
              "      <td>1.757296</td>\n",
              "      <td>1.383058</td>\n",
              "      <td>0.876787</td>\n",
              "      <td>63.311256</td>\n",
              "      <td>1.315265</td>\n",
              "      <td>76.965412</td>\n",
              "      <td>-0.037710</td>\n",
              "      <td>573.786433</td>\n",
              "      <td>19.961030</td>\n",
              "      <td>0.179814</td>\n",
              "      <td>1715.688383</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.710354</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.312257</td>\n",
              "      <td>11.668277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>145.446164</td>\n",
              "      <td>348.931601</td>\n",
              "      <td>0.493752</td>\n",
              "      <td>7.484629</td>\n",
              "      <td>3.760292</td>\n",
              "      <td>309.277877</td>\n",
              "      <td>19.755950</td>\n",
              "      <td>0.372309</td>\n",
              "      <td>2505.335141</td>\n",
              "      <td>18.577930</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.049071</td>\n",
              "      <td>127.258629</td>\n",
              "      <td>1.006162</td>\n",
              "      <td>35.508596</td>\n",
              "      <td>0.002304</td>\n",
              "      <td>27.399973</td>\n",
              "      <td>2.854852</td>\n",
              "      <td>1.608807</td>\n",
              "      <td>0.328681</td>\n",
              "      <td>18.891029</td>\n",
              "      <td>5.247204</td>\n",
              "      <td>44.387031</td>\n",
              "      <td>0.403896</td>\n",
              "      <td>298.412888</td>\n",
              "      <td>63.269456</td>\n",
              "      <td>0.384033</td>\n",
              "      <td>618.176470</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.532085</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.243586</td>\n",
              "      <td>9.934423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-22.481200</td>\n",
              "      <td>-45.629200</td>\n",
              "      <td>504.000000</td>\n",
              "      <td>23.885300</td>\n",
              "      <td>0.038920</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>0.262224</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-250.000000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-4.795000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.676730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>125.000000</td>\n",
              "      <td>302.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.758100</td>\n",
              "      <td>9.903540</td>\n",
              "      <td>792.000000</td>\n",
              "      <td>49.180300</td>\n",
              "      <td>0.092110</td>\n",
              "      <td>668.000000</td>\n",
              "      <td>66.666700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>0.015686</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>41.930000</td>\n",
              "      <td>-0.175000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.487500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1259.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.947680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>250.000000</td>\n",
              "      <td>604.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>34.145100</td>\n",
              "      <td>11.400400</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.105083</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016001</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>511.000000</td>\n",
              "      <td>3.018750</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.772600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>374.000000</td>\n",
              "      <td>906.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>37.311900</td>\n",
              "      <td>13.644200</td>\n",
              "      <td>1220.000000</td>\n",
              "      <td>75.757600</td>\n",
              "      <td>0.138814</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>89.820400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.240000</td>\n",
              "      <td>211.584000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.016694</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>108.500000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>7.481250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2146.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.900000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.270900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>510.000000</td>\n",
              "      <td>1210.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>101.351000</td>\n",
              "      <td>71.173700</td>\n",
              "      <td>2512.000000</td>\n",
              "      <td>119.048000</td>\n",
              "      <td>27.202200</td>\n",
              "      <td>228812.000000</td>\n",
              "      <td>468.750000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>243.991000</td>\n",
              "      <td>359.995000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>0.023939</td>\n",
              "      <td>513.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>52.400000</td>\n",
              "      <td>129.700000</td>\n",
              "      <td>3.990000</td>\n",
              "      <td>1023.000000</td>\n",
              "      <td>484.488000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4892.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>262.534000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96cfce73-5cd4-48c3-911b-1a82e4570749')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-96cfce73-5cd4-48c3-911b-1a82e4570749 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-96cfce73-5cd4-48c3-911b-1a82e4570749');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             TrialID         ObsNum        IsAlert             P1  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean      250.167657     603.841765       0.578799      35.449020   \n",
              "std       145.446164     348.931601       0.493752       7.484629   \n",
              "min         0.000000       0.000000       0.000000     -22.481200   \n",
              "25%       125.000000     302.000000       0.000000      31.758100   \n",
              "50%       250.000000     604.000000       1.000000      34.145100   \n",
              "75%       374.000000     906.000000       1.000000      37.311900   \n",
              "max       510.000000    1210.000000       1.000000     101.351000   \n",
              "\n",
              "                  P2             P3             P4             P5  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean       11.996525    1026.671035      64.061965       0.178923   \n",
              "std         3.760292     309.277877      19.755950       0.372309   \n",
              "min       -45.629200     504.000000      23.885300       0.038920   \n",
              "25%         9.903540     792.000000      49.180300       0.092110   \n",
              "50%        11.400400    1000.000000      60.000000       0.105083   \n",
              "75%        13.644200    1220.000000      75.757600       0.138814   \n",
              "max        71.173700    2512.000000     119.048000      27.202200   \n",
              "\n",
              "                  P6             P7        P8             E1             E2  \\\n",
              "count  604329.000000  604329.000000  604329.0  604329.000000  604329.000000   \n",
              "mean      845.384610      77.887628       0.0      10.512332     102.790045   \n",
              "std      2505.335141      18.577930       0.0      14.049071     127.258629   \n",
              "min       128.000000       0.262224       0.0       0.000000       0.000000   \n",
              "25%       668.000000      66.666700       0.0       0.000000       0.000000   \n",
              "50%       800.000000      75.000000       0.0       0.000000       0.000000   \n",
              "75%       900.000000      89.820400       0.0      28.240000     211.584000   \n",
              "max    228812.000000     468.750000       0.0     243.991000     359.995000   \n",
              "\n",
              "                  E3             E4             E5             E6  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean        0.290565      -4.230136       0.016262     358.674738   \n",
              "std         1.006162      35.508596       0.002304      27.399973   \n",
              "min         0.000000    -250.000000       0.008000     260.000000   \n",
              "25%         0.000000      -8.000000       0.015686     348.000000   \n",
              "50%         0.000000       0.000000       0.016001     365.000000   \n",
              "75%         0.000000       6.000000       0.016694     367.000000   \n",
              "max         4.000000     260.000000       0.023939     513.000000   \n",
              "\n",
              "                  E7             E8             E9            E10  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean        1.757296       1.383058       0.876787      63.311256   \n",
              "std         2.854852       1.608807       0.328681      18.891029   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.000000       1.000000      52.000000   \n",
              "50%         1.000000       1.000000       1.000000      67.000000   \n",
              "75%         2.000000       2.000000       1.000000      73.000000   \n",
              "max        25.000000       9.000000       1.000000     127.000000   \n",
              "\n",
              "                 E11             V1             V2             V3  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean        1.315265      76.965412      -0.037710     573.786433   \n",
              "std         5.247204      44.387031       0.403896     298.412888   \n",
              "min         0.000000       0.000000      -4.795000     240.000000   \n",
              "25%         0.000000      41.930000      -0.175000     255.000000   \n",
              "50%         0.000000     100.400000       0.000000     511.000000   \n",
              "75%         0.000000     108.500000       0.070000     767.000000   \n",
              "max        52.400000     129.700000       3.990000    1023.000000   \n",
              "\n",
              "                  V4             V5             V6        V7             V8  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.0  604329.000000   \n",
              "mean       19.961030       0.179814    1715.688383       0.0      12.710354   \n",
              "std        63.269456       0.384033     618.176470       0.0      11.532085   \n",
              "min         0.000000       0.000000       0.000000       0.0       0.000000   \n",
              "25%         1.487500       0.000000    1259.000000       0.0       0.000000   \n",
              "50%         3.018750       0.000000    1994.000000       0.0      12.800000   \n",
              "75%         7.481250       0.000000    2146.000000       0.0      21.900000   \n",
              "max       484.488000       1.000000    4892.000000       0.0      82.100000   \n",
              "\n",
              "             V9            V10            V11  \n",
              "count  604329.0  604329.000000  604329.000000  \n",
              "mean        0.0       3.312257      11.668277  \n",
              "std         0.0       1.243586       9.934423  \n",
              "min         0.0       1.000000       1.676730  \n",
              "25%         0.0       3.000000       7.947680  \n",
              "50%         0.0       4.000000      10.772600  \n",
              "75%         0.0       4.000000      15.270900  \n",
              "max         0.0       7.000000     262.534000  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ae8f37e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "0ae8f37e",
        "outputId": "ad95ccae-3a3c-4624-bfdc-7537c8856e1a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e2e43ab6-3fe9-4485-868d-a0487b2fabce\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>IsAlert</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>491832</th>\n",
              "      <td>406</td>\n",
              "      <td>1059</td>\n",
              "      <td>1</td>\n",
              "      <td>41.8120</td>\n",
              "      <td>6.27982</td>\n",
              "      <td>616</td>\n",
              "      <td>97.4026</td>\n",
              "      <td>0.107678</td>\n",
              "      <td>616</td>\n",
              "      <td>97.4026</td>\n",
              "      <td>0</td>\n",
              "      <td>0.043</td>\n",
              "      <td>219.045</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.015812</td>\n",
              "      <td>366</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>72</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>240</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0</td>\n",
              "      <td>648</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16.45860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479193</th>\n",
              "      <td>396</td>\n",
              "      <td>496</td>\n",
              "      <td>1</td>\n",
              "      <td>34.3353</td>\n",
              "      <td>11.36550</td>\n",
              "      <td>908</td>\n",
              "      <td>66.0793</td>\n",
              "      <td>0.092110</td>\n",
              "      <td>652</td>\n",
              "      <td>92.0245</td>\n",
              "      <td>0</td>\n",
              "      <td>6.617</td>\n",
              "      <td>95.508</td>\n",
              "      <td>0</td>\n",
              "      <td>-52</td>\n",
              "      <td>0.015875</td>\n",
              "      <td>392</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.50</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1008</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1656</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12.16280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63510</th>\n",
              "      <td>52</td>\n",
              "      <td>670</td>\n",
              "      <td>0</td>\n",
              "      <td>31.0747</td>\n",
              "      <td>10.10830</td>\n",
              "      <td>904</td>\n",
              "      <td>66.3717</td>\n",
              "      <td>0.083029</td>\n",
              "      <td>748</td>\n",
              "      <td>80.2139</td>\n",
              "      <td>0</td>\n",
              "      <td>29.262</td>\n",
              "      <td>186.513</td>\n",
              "      <td>0</td>\n",
              "      <td>-6</td>\n",
              "      <td>0.015875</td>\n",
              "      <td>342</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>63</td>\n",
              "      <td>0.0</td>\n",
              "      <td>103.14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>767</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>2009</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>9.90299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214783</th>\n",
              "      <td>177</td>\n",
              "      <td>860</td>\n",
              "      <td>1</td>\n",
              "      <td>42.8454</td>\n",
              "      <td>8.69336</td>\n",
              "      <td>1272</td>\n",
              "      <td>47.1698</td>\n",
              "      <td>0.112867</td>\n",
              "      <td>1008</td>\n",
              "      <td>59.5238</td>\n",
              "      <td>0</td>\n",
              "      <td>30.786</td>\n",
              "      <td>342.906</td>\n",
              "      <td>0</td>\n",
              "      <td>-14</td>\n",
              "      <td>0.015686</td>\n",
              "      <td>335</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>116.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>240</td>\n",
              "      <td>4.50625</td>\n",
              "      <td>0</td>\n",
              "      <td>2271</td>\n",
              "      <td>0</td>\n",
              "      <td>7.3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>9.59598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60877</th>\n",
              "      <td>50</td>\n",
              "      <td>447</td>\n",
              "      <td>0</td>\n",
              "      <td>46.8255</td>\n",
              "      <td>22.86100</td>\n",
              "      <td>1008</td>\n",
              "      <td>59.5238</td>\n",
              "      <td>0.079137</td>\n",
              "      <td>724</td>\n",
              "      <td>82.8729</td>\n",
              "      <td>0</td>\n",
              "      <td>28.186</td>\n",
              "      <td>141.681</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>0.015875</td>\n",
              "      <td>388</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>97</td>\n",
              "      <td>0.0</td>\n",
              "      <td>102.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1008</td>\n",
              "      <td>3.01875</td>\n",
              "      <td>0</td>\n",
              "      <td>1993</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>10.06040</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2e43ab6-3fe9-4485-868d-a0487b2fabce')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e2e43ab6-3fe9-4485-868d-a0487b2fabce button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e2e43ab6-3fe9-4485-868d-a0487b2fabce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        TrialID  ObsNum  IsAlert       P1        P2    P3       P4        P5  \\\n",
              "491832      406    1059        1  41.8120   6.27982   616  97.4026  0.107678   \n",
              "479193      396     496        1  34.3353  11.36550   908  66.0793  0.092110   \n",
              "63510        52     670        0  31.0747  10.10830   904  66.3717  0.083029   \n",
              "214783      177     860        1  42.8454   8.69336  1272  47.1698  0.112867   \n",
              "60877        50     447        0  46.8255  22.86100  1008  59.5238  0.079137   \n",
              "\n",
              "          P6       P7  P8      E1       E2  E3  E4        E5   E6  E7  E8  E9  \\\n",
              "491832   616  97.4026   0   0.043  219.045   0   0  0.015812  366   0   0   1   \n",
              "479193   652  92.0245   0   6.617   95.508   0 -52  0.015875  392   2   2   1   \n",
              "63510    748  80.2139   0  29.262  186.513   0  -6  0.015875  342   1   1   1   \n",
              "214783  1008  59.5238   0  30.786  342.906   0 -14  0.015686  335   1   2   1   \n",
              "60877    724  82.8729   0  28.186  141.681   0  28  0.015875  388   8   9   0   \n",
              "\n",
              "        E10  E11      V1    V2    V3       V4  V5    V6  V7    V8  V9  V10  \\\n",
              "491832   72  0.0    0.00 -0.07   240  0.00000   0   648   0   0.0   0    1   \n",
              "479193   53  0.0   20.50  0.07  1008  3.01875   0  1656   0   0.0   0    1   \n",
              "63510    63  0.0  103.14  0.00   767  3.01875   0  2009   0   0.0   0    4   \n",
              "214783   69  0.0  116.57  0.00   240  4.50625   0  2271   0   7.3   0    4   \n",
              "60877    97  0.0  102.43  0.00  1008  3.01875   0  1993   0  28.0   0    4   \n",
              "\n",
              "             V11  \n",
              "491832  16.45860  \n",
              "479193  12.16280  \n",
              "63510    9.90299  \n",
              "214783   9.59598  \n",
              "60877   10.06040  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets print a random sample of 5 rows from traiing dataframe\n",
        "train_df.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce7d227a",
      "metadata": {
        "id": "ce7d227a"
      },
      "outputs": [],
      "source": [
        "# Lets make a copy of our original train_df dataframe, and test_df dataframe\n",
        "train_new_df = train_df.copy()\n",
        "test_new_df = test_df.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "518317a9",
      "metadata": {
        "id": "518317a9"
      },
      "source": [
        "From our `training` and `testing` dataframes, the TrailID and ObsNum columns may not be very useful for predicting the drivers alertness. So, lets remove these columns from our new copies of tbe train_df and test_df."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "41ff9236",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41ff9236",
        "outputId": "11cbb0f5-4abf-4305-eb84-11631040a1b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['TrialID', 'ObsNum']"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets select the columns to be removed\n",
        "cols_tobe_removed = (train_df.columns.tolist())[0:2]\n",
        "cols_tobe_removed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a2e585b",
      "metadata": {
        "id": "3a2e585b"
      },
      "outputs": [],
      "source": [
        "train_new_df.drop(columns = cols_tobe_removed,axis = 1,inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c329d41",
      "metadata": {
        "id": "9c329d41"
      },
      "outputs": [],
      "source": [
        "test_new_df.drop(columns = cols_tobe_removed,axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2dfc3032",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dfc3032",
        "outputId": "cb69c1e4-c456-4447-8c3d-77f67361480e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(604329, 31)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b3cc930",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1b3cc930",
        "outputId": "14f02b5a-fe0e-4614-f3ec-231bf696f32b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(120840, 31)"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_new_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ma1sJtetsH3r",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "ma1sJtetsH3r",
        "outputId": "f0892a6f-5da5-4173-9aaa-d8cd9fbdadf8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ffc326f4-5d8a-46a5-b67f-0cd3d66f0189\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IsAlert</th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.0</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.0</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.0</td>\n",
              "      <td>604329.000000</td>\n",
              "      <td>604329.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.578799</td>\n",
              "      <td>35.449020</td>\n",
              "      <td>11.996525</td>\n",
              "      <td>1026.671035</td>\n",
              "      <td>64.061965</td>\n",
              "      <td>0.178923</td>\n",
              "      <td>845.384610</td>\n",
              "      <td>77.887628</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.512332</td>\n",
              "      <td>102.790045</td>\n",
              "      <td>0.290565</td>\n",
              "      <td>-4.230136</td>\n",
              "      <td>0.016262</td>\n",
              "      <td>358.674738</td>\n",
              "      <td>1.757296</td>\n",
              "      <td>1.383058</td>\n",
              "      <td>0.876787</td>\n",
              "      <td>63.311256</td>\n",
              "      <td>1.315265</td>\n",
              "      <td>76.965412</td>\n",
              "      <td>-0.037710</td>\n",
              "      <td>573.786433</td>\n",
              "      <td>19.961030</td>\n",
              "      <td>0.179814</td>\n",
              "      <td>1715.688383</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.710354</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.312257</td>\n",
              "      <td>11.668277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.493752</td>\n",
              "      <td>7.484629</td>\n",
              "      <td>3.760292</td>\n",
              "      <td>309.277877</td>\n",
              "      <td>19.755950</td>\n",
              "      <td>0.372309</td>\n",
              "      <td>2505.335141</td>\n",
              "      <td>18.577930</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.049071</td>\n",
              "      <td>127.258629</td>\n",
              "      <td>1.006162</td>\n",
              "      <td>35.508596</td>\n",
              "      <td>0.002304</td>\n",
              "      <td>27.399973</td>\n",
              "      <td>2.854852</td>\n",
              "      <td>1.608807</td>\n",
              "      <td>0.328681</td>\n",
              "      <td>18.891029</td>\n",
              "      <td>5.247204</td>\n",
              "      <td>44.387031</td>\n",
              "      <td>0.403896</td>\n",
              "      <td>298.412888</td>\n",
              "      <td>63.269456</td>\n",
              "      <td>0.384033</td>\n",
              "      <td>618.176470</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.532085</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.243586</td>\n",
              "      <td>9.934423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-22.481200</td>\n",
              "      <td>-45.629200</td>\n",
              "      <td>504.000000</td>\n",
              "      <td>23.885300</td>\n",
              "      <td>0.038920</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>0.262224</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-250.000000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-4.795000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.676730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.758100</td>\n",
              "      <td>9.903540</td>\n",
              "      <td>792.000000</td>\n",
              "      <td>49.180300</td>\n",
              "      <td>0.092110</td>\n",
              "      <td>668.000000</td>\n",
              "      <td>66.666700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-8.000000</td>\n",
              "      <td>0.015686</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>41.930000</td>\n",
              "      <td>-0.175000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.487500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1259.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.947680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>34.145100</td>\n",
              "      <td>11.400400</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.105083</td>\n",
              "      <td>800.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016001</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>511.000000</td>\n",
              "      <td>3.018750</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1994.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.772600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>37.311900</td>\n",
              "      <td>13.644200</td>\n",
              "      <td>1220.000000</td>\n",
              "      <td>75.757600</td>\n",
              "      <td>0.138814</td>\n",
              "      <td>900.000000</td>\n",
              "      <td>89.820400</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.240000</td>\n",
              "      <td>211.584000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.016694</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>108.500000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>7.481250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2146.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.900000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.270900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>101.351000</td>\n",
              "      <td>71.173700</td>\n",
              "      <td>2512.000000</td>\n",
              "      <td>119.048000</td>\n",
              "      <td>27.202200</td>\n",
              "      <td>228812.000000</td>\n",
              "      <td>468.750000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>243.991000</td>\n",
              "      <td>359.995000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>0.023939</td>\n",
              "      <td>513.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>52.400000</td>\n",
              "      <td>129.700000</td>\n",
              "      <td>3.990000</td>\n",
              "      <td>1023.000000</td>\n",
              "      <td>484.488000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4892.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>262.534000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ffc326f4-5d8a-46a5-b67f-0cd3d66f0189')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ffc326f4-5d8a-46a5-b67f-0cd3d66f0189 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ffc326f4-5d8a-46a5-b67f-0cd3d66f0189');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             IsAlert             P1             P2             P3  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean        0.578799      35.449020      11.996525    1026.671035   \n",
              "std         0.493752       7.484629       3.760292     309.277877   \n",
              "min         0.000000     -22.481200     -45.629200     504.000000   \n",
              "25%         0.000000      31.758100       9.903540     792.000000   \n",
              "50%         1.000000      34.145100      11.400400    1000.000000   \n",
              "75%         1.000000      37.311900      13.644200    1220.000000   \n",
              "max         1.000000     101.351000      71.173700    2512.000000   \n",
              "\n",
              "                  P4             P5             P6             P7        P8  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000  604329.0   \n",
              "mean       64.061965       0.178923     845.384610      77.887628       0.0   \n",
              "std        19.755950       0.372309    2505.335141      18.577930       0.0   \n",
              "min        23.885300       0.038920     128.000000       0.262224       0.0   \n",
              "25%        49.180300       0.092110     668.000000      66.666700       0.0   \n",
              "50%        60.000000       0.105083     800.000000      75.000000       0.0   \n",
              "75%        75.757600       0.138814     900.000000      89.820400       0.0   \n",
              "max       119.048000      27.202200  228812.000000     468.750000       0.0   \n",
              "\n",
              "                  E1             E2             E3             E4  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean       10.512332     102.790045       0.290565      -4.230136   \n",
              "std        14.049071     127.258629       1.006162      35.508596   \n",
              "min         0.000000       0.000000       0.000000    -250.000000   \n",
              "25%         0.000000       0.000000       0.000000      -8.000000   \n",
              "50%         0.000000       0.000000       0.000000       0.000000   \n",
              "75%        28.240000     211.584000       0.000000       6.000000   \n",
              "max       243.991000     359.995000       4.000000     260.000000   \n",
              "\n",
              "                  E5             E6             E7             E8  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean        0.016262     358.674738       1.757296       1.383058   \n",
              "std         0.002304      27.399973       2.854852       1.608807   \n",
              "min         0.008000     260.000000       0.000000       0.000000   \n",
              "25%         0.015686     348.000000       0.000000       0.000000   \n",
              "50%         0.016001     365.000000       1.000000       1.000000   \n",
              "75%         0.016694     367.000000       2.000000       2.000000   \n",
              "max         0.023939     513.000000      25.000000       9.000000   \n",
              "\n",
              "                  E9            E10            E11             V1  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean        0.876787      63.311256       1.315265      76.965412   \n",
              "std         0.328681      18.891029       5.247204      44.387031   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         1.000000      52.000000       0.000000      41.930000   \n",
              "50%         1.000000      67.000000       0.000000     100.400000   \n",
              "75%         1.000000      73.000000       0.000000     108.500000   \n",
              "max         1.000000     127.000000      52.400000     129.700000   \n",
              "\n",
              "                  V2             V3             V4             V5  \\\n",
              "count  604329.000000  604329.000000  604329.000000  604329.000000   \n",
              "mean       -0.037710     573.786433      19.961030       0.179814   \n",
              "std         0.403896     298.412888      63.269456       0.384033   \n",
              "min        -4.795000     240.000000       0.000000       0.000000   \n",
              "25%        -0.175000     255.000000       1.487500       0.000000   \n",
              "50%         0.000000     511.000000       3.018750       0.000000   \n",
              "75%         0.070000     767.000000       7.481250       0.000000   \n",
              "max         3.990000    1023.000000     484.488000       1.000000   \n",
              "\n",
              "                  V6        V7             V8        V9            V10  \\\n",
              "count  604329.000000  604329.0  604329.000000  604329.0  604329.000000   \n",
              "mean     1715.688383       0.0      12.710354       0.0       3.312257   \n",
              "std       618.176470       0.0      11.532085       0.0       1.243586   \n",
              "min         0.000000       0.0       0.000000       0.0       1.000000   \n",
              "25%      1259.000000       0.0       0.000000       0.0       3.000000   \n",
              "50%      1994.000000       0.0      12.800000       0.0       4.000000   \n",
              "75%      2146.000000       0.0      21.900000       0.0       4.000000   \n",
              "max      4892.000000       0.0      82.100000       0.0       7.000000   \n",
              "\n",
              "                 V11  \n",
              "count  604329.000000  \n",
              "mean       11.668277  \n",
              "std         9.934423  \n",
              "min         1.676730  \n",
              "25%         7.947680  \n",
              "50%        10.772600  \n",
              "75%        15.270900  \n",
              "max       262.534000  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_new_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Ybt23PXPskUM",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "Ybt23PXPskUM",
        "outputId": "2309910e-dcb4-4c71-faf8-37de5806fa5c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a7ba1058-0e33-4285-bb71-8c38aa17d6f5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P1</th>\n",
              "      <th>P2</th>\n",
              "      <th>P3</th>\n",
              "      <th>P4</th>\n",
              "      <th>P5</th>\n",
              "      <th>P6</th>\n",
              "      <th>P7</th>\n",
              "      <th>P8</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.0</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.0</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.0</td>\n",
              "      <td>120840.000000</td>\n",
              "      <td>120840.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.450222</td>\n",
              "      <td>12.008451</td>\n",
              "      <td>1026.668355</td>\n",
              "      <td>64.148812</td>\n",
              "      <td>0.122339</td>\n",
              "      <td>707.701456</td>\n",
              "      <td>86.174020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.648237</td>\n",
              "      <td>98.202840</td>\n",
              "      <td>0.191154</td>\n",
              "      <td>-0.572095</td>\n",
              "      <td>0.015950</td>\n",
              "      <td>347.686395</td>\n",
              "      <td>1.358284</td>\n",
              "      <td>1.614176</td>\n",
              "      <td>0.868032</td>\n",
              "      <td>68.655238</td>\n",
              "      <td>1.770682</td>\n",
              "      <td>78.696121</td>\n",
              "      <td>-0.036024</td>\n",
              "      <td>562.097443</td>\n",
              "      <td>32.215433</td>\n",
              "      <td>0.179394</td>\n",
              "      <td>1732.128037</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.838320</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.378633</td>\n",
              "      <td>13.899704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.303869</td>\n",
              "      <td>4.351161</td>\n",
              "      <td>310.874514</td>\n",
              "      <td>19.995102</td>\n",
              "      <td>0.113811</td>\n",
              "      <td>90.398496</td>\n",
              "      <td>11.024309</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.950893</td>\n",
              "      <td>125.644623</td>\n",
              "      <td>0.776454</td>\n",
              "      <td>26.663652</td>\n",
              "      <td>0.002584</td>\n",
              "      <td>47.971289</td>\n",
              "      <td>2.376400</td>\n",
              "      <td>1.537636</td>\n",
              "      <td>0.338457</td>\n",
              "      <td>17.456849</td>\n",
              "      <td>6.030855</td>\n",
              "      <td>42.534170</td>\n",
              "      <td>0.392249</td>\n",
              "      <td>300.358571</td>\n",
              "      <td>97.893439</td>\n",
              "      <td>0.383684</td>\n",
              "      <td>594.847283</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.315979</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.213857</td>\n",
              "      <td>1.504704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.776300</td>\n",
              "      <td>-25.911800</td>\n",
              "      <td>504.000000</td>\n",
              "      <td>25.996500</td>\n",
              "      <td>0.040217</td>\n",
              "      <td>468.000000</td>\n",
              "      <td>49.342100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-250.000000</td>\n",
              "      <td>0.008000</td>\n",
              "      <td>260.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.605000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.538220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.456300</td>\n",
              "      <td>9.600658</td>\n",
              "      <td>788.000000</td>\n",
              "      <td>49.180300</td>\n",
              "      <td>0.089515</td>\n",
              "      <td>636.000000</td>\n",
              "      <td>76.923100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>0.014804</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>56.540000</td>\n",
              "      <td>-0.175000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.487500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1489.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>12.775275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>34.877800</td>\n",
              "      <td>11.288900</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.101191</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>85.714300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015875</td>\n",
              "      <td>356.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>101.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>497.000000</td>\n",
              "      <td>3.018750</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.300000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>13.470900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36.862200</td>\n",
              "      <td>13.542925</td>\n",
              "      <td>1220.000000</td>\n",
              "      <td>76.142100</td>\n",
              "      <td>0.115462</td>\n",
              "      <td>780.000000</td>\n",
              "      <td>94.339600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.541000</td>\n",
              "      <td>211.328000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.017387</td>\n",
              "      <td>367.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>107.650000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>767.000000</td>\n",
              "      <td>5.993750</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2123.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.800000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>15.223900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81.819600</td>\n",
              "      <td>39.757300</td>\n",
              "      <td>2308.000000</td>\n",
              "      <td>119.048000</td>\n",
              "      <td>4.667770</td>\n",
              "      <td>1216.000000</td>\n",
              "      <td>128.205000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>64.693000</td>\n",
              "      <td>359.999000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>254.000000</td>\n",
              "      <td>0.023939</td>\n",
              "      <td>513.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>36.800000</td>\n",
              "      <td>122.650000</td>\n",
              "      <td>3.115000</td>\n",
              "      <td>1023.000000</td>\n",
              "      <td>478.494000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3408.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.100000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>18.322500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7ba1058-0e33-4285-bb71-8c38aa17d6f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a7ba1058-0e33-4285-bb71-8c38aa17d6f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7ba1058-0e33-4285-bb71-8c38aa17d6f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  P1             P2             P3             P4  \\\n",
              "count  120840.000000  120840.000000  120840.000000  120840.000000   \n",
              "mean       35.450222      12.008451    1026.668355      64.148812   \n",
              "std         3.303869       4.351161     310.874514      19.995102   \n",
              "min        17.776300     -25.911800     504.000000      25.996500   \n",
              "25%        33.456300       9.600658     788.000000      49.180300   \n",
              "50%        34.877800      11.288900    1000.000000      60.000000   \n",
              "75%        36.862200      13.542925    1220.000000      76.142100   \n",
              "max        81.819600      39.757300    2308.000000     119.048000   \n",
              "\n",
              "                  P5             P6             P7        P8             E1  \\\n",
              "count  120840.000000  120840.000000  120840.000000  120840.0  120840.000000   \n",
              "mean        0.122339     707.701456      86.174020       0.0      10.648237   \n",
              "std         0.113811      90.398496      11.024309       0.0      13.950893   \n",
              "min         0.040217     468.000000      49.342100       0.0       0.000000   \n",
              "25%         0.089515     636.000000      76.923100       0.0       0.000000   \n",
              "50%         0.101191     700.000000      85.714300       0.0       0.000000   \n",
              "75%         0.115462     780.000000      94.339600       0.0      28.541000   \n",
              "max         4.667770    1216.000000     128.205000       0.0      64.693000   \n",
              "\n",
              "                  E2             E3             E4             E5  \\\n",
              "count  120840.000000  120840.000000  120840.000000  120840.000000   \n",
              "mean       98.202840       0.191154      -0.572095       0.015950   \n",
              "std       125.644623       0.776454      26.663652       0.002584   \n",
              "min         0.000000       0.000000    -250.000000       0.008000   \n",
              "25%         0.000000       0.000000      -6.000000       0.014804   \n",
              "50%         0.000000       0.000000       0.000000       0.015875   \n",
              "75%       211.328000       0.000000       6.000000       0.017387   \n",
              "max       359.999000       4.000000     254.000000       0.023939   \n",
              "\n",
              "                  E6             E7             E8             E9  \\\n",
              "count  120840.000000  120840.000000  120840.000000  120840.000000   \n",
              "mean      347.686395       1.358284       1.614176       0.868032   \n",
              "std        47.971289       2.376400       1.537636       0.338457   \n",
              "min       260.000000       0.000000       0.000000       0.000000   \n",
              "25%       303.000000       0.000000       0.000000       1.000000   \n",
              "50%       356.000000       1.000000       2.000000       1.000000   \n",
              "75%       367.000000       2.000000       3.000000       1.000000   \n",
              "max       513.000000      25.000000       9.000000       1.000000   \n",
              "\n",
              "                 E10            E11             V1             V2  \\\n",
              "count  120840.000000  120840.000000  120840.000000  120840.000000   \n",
              "mean       68.655238       1.770682      78.696121      -0.036024   \n",
              "std        17.456849       6.030855      42.534170       0.392249   \n",
              "min         0.000000       0.000000       0.000000      -3.605000   \n",
              "25%        65.000000       0.000000      56.540000      -0.175000   \n",
              "50%        71.000000       0.000000     101.800000       0.000000   \n",
              "75%        77.000000       0.000000     107.650000       0.070000   \n",
              "max       127.000000      36.800000     122.650000       3.115000   \n",
              "\n",
              "                  V3             V4             V5             V6        V7  \\\n",
              "count  120840.000000  120840.000000  120840.000000  120840.000000  120840.0   \n",
              "mean      562.097443      32.215433       0.179394    1732.128037       0.0   \n",
              "std       300.358571      97.893439       0.383684     594.847283       0.0   \n",
              "min       240.000000       0.000000       0.000000       0.000000       0.0   \n",
              "25%       255.000000       1.487500       0.000000    1489.000000       0.0   \n",
              "50%       497.000000       3.018750       0.000000    2015.000000       0.0   \n",
              "75%       767.000000       5.993750       0.000000    2123.000000       0.0   \n",
              "max      1023.000000     478.494000       1.000000    3408.000000       0.0   \n",
              "\n",
              "                  V8        V9            V10            V11  \n",
              "count  120840.000000  120840.0  120840.000000  120840.000000  \n",
              "mean       12.838320       0.0       3.378633      13.899704  \n",
              "std        12.315979       0.0       1.213857       1.504704  \n",
              "min         0.000000       0.0       1.000000       9.538220  \n",
              "25%         0.000000       0.0       4.000000      12.775275  \n",
              "50%        12.300000       0.0       4.000000      13.470900  \n",
              "75%        22.800000       0.0       4.000000      15.223900  \n",
              "max        82.100000       0.0       7.000000      18.322500  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_new_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "TE0VRHWhsNFW",
      "metadata": {
        "id": "TE0VRHWhsNFW"
      },
      "source": [
        "In the `train_new_df` and `test_new_df` DataFrames, different feature columns have values in different ranges. In the training dataframe, columns P1 and P2 have minimum values and means to be 22.481200,\t-45.629200, 35.449020,\t11.996525. When compared to another feature column P3, its minimum value is 504 and mean is 1026.671035 which are way higher than the minimum value and mean of previous columns.\n",
        "\n",
        "This pattern also follows in different columns of both testing and training dataframes. When we pass the feature and target columns to models, the models ultimately get trained with numbers as scikitlearn only works with numeric values. This may result in columns with higher values to have higher importances or weights in descision making which may result in false predictions.\n",
        "\n",
        "Hence, It is the best practice to convert or bring the values of all feature columns in a same range like (0,1) or (-1,1) respectively. We can do so by using the process called `scaling`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "IzcTbhjqsMaT",
      "metadata": {
        "id": "IzcTbhjqsMaT"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "vuOldPgvv89Y",
      "metadata": {
        "id": "vuOldPgvv89Y"
      },
      "source": [
        "The `StandardScaler` scales or transforms the values in the numeric column such that they have a mean 0, and standard deviation 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "GyI-nUf3v4g-",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyI-nUf3v4g-",
        "outputId": "790def6d-1da8-44c4-e3d6-0a4cda9926d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['P1',\n",
              " 'P2',\n",
              " 'P3',\n",
              " 'P4',\n",
              " 'P5',\n",
              " 'P6',\n",
              " 'P7',\n",
              " 'P8',\n",
              " 'E1',\n",
              " 'E2',\n",
              " 'E3',\n",
              " 'E4',\n",
              " 'E5',\n",
              " 'E6',\n",
              " 'E7',\n",
              " 'E8',\n",
              " 'E9',\n",
              " 'E10',\n",
              " 'E11',\n",
              " 'V1',\n",
              " 'V2',\n",
              " 'V3',\n",
              " 'V4',\n",
              " 'V5',\n",
              " 'V6',\n",
              " 'V7',\n",
              " 'V8',\n",
              " 'V9',\n",
              " 'V10',\n",
              " 'V11']"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "numeric_feature_cols = train_new_df.columns.tolist()[1:]\n",
        "numeric_feature_cols"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "BnAXtzrGwQst",
      "metadata": {
        "id": "BnAXtzrGwQst"
      },
      "source": [
        "Since, the dataset provider in `kaggle` has mentioned that the models which make the use of least number of `Psychological` columns will be most favoured, we will drop all the `Psychological` columns other than the one that has the maximum mean that is `P3`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Cm_WG8nQwn_E",
      "metadata": {
        "id": "Cm_WG8nQwn_E"
      },
      "outputs": [],
      "source": [
        "del numeric_feature_cols[0:8]\n",
        "numeric_feature_cols.insert(0,'P3')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "JM-7b22Owo8L",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JM-7b22Owo8L",
        "outputId": "97abc2b1-baec-4003-be14-f7f78c708277"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['P3',\n",
              " 'E1',\n",
              " 'E2',\n",
              " 'E3',\n",
              " 'E4',\n",
              " 'E5',\n",
              " 'E6',\n",
              " 'E7',\n",
              " 'E8',\n",
              " 'E9',\n",
              " 'E10',\n",
              " 'E11',\n",
              " 'V1',\n",
              " 'V2',\n",
              " 'V3',\n",
              " 'V4',\n",
              " 'V5',\n",
              " 'V6',\n",
              " 'V7',\n",
              " 'V8',\n",
              " 'V9',\n",
              " 'V10',\n",
              " 'V11']"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "numeric_feature_cols"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "zdhiu29Xwo2l",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "zdhiu29Xwo2l",
        "outputId": "d19d8ae8-5ba0-4b23-e9aa-7aaafd7c7013"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "StandardScaler()"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets scale our numeric columns using StandardScaler\n",
        "scaler.fit(train_new_df[numeric_feature_cols])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "uSOHfW_jwotl",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSOHfW_jwotl",
        "outputId": "9efb417f-33af-4c9a-cb1d-c049bd2ef371"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[1026.6710351480733,\n",
              " 10.512332350425016,\n",
              " 102.79004483650462,\n",
              " 0.2905652384710977,\n",
              " -4.230136233740231,\n",
              " 0.016262422963319655,\n",
              " 358.6747384289021,\n",
              " 1.7572961085766197,\n",
              " 1.3830579038900996,\n",
              " 0.8767873128709693,\n",
              " 63.31125595495169,\n",
              " 1.3152650294789754,\n",
              " 76.96541183692989,\n",
              " -0.03770957541339237,\n",
              " 573.7864325557767,\n",
              " 19.96102967853602,\n",
              " 0.1798143064456612,\n",
              " 1715.6883833143868,\n",
              " 0.0,\n",
              " 12.710354459243227,\n",
              " 0.0,\n",
              " 3.3122570652740477,\n",
              " 11.66827714039869]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The calculated mean for each column after scaling using StandardScaler\n",
        "list(scaler.mean_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "AaVLZhT-g4V2",
      "metadata": {
        "id": "AaVLZhT-g4V2"
      },
      "outputs": [],
      "source": [
        "# lets scale and transform our columns in the training and testing set\n",
        "\n",
        "# for training set\n",
        "train_new_df[numeric_feature_cols] = scaler.transform(train_new_df[numeric_feature_cols])\n",
        "\n",
        "# for testing set\n",
        "test_new_df[numeric_feature_cols] = scaler.transform(test_new_df[numeric_feature_cols])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "TcJktzffhxAn",
      "metadata": {
        "id": "TcJktzffhxAn"
      },
      "source": [
        "<b> Now, Lets check our scaled training and testing set. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "RJ--cZA4h3WV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "RJ--cZA4h3WV",
        "outputId": "53347dd8-e987-4770-b8c1-7414ab352bf0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3a90304e-d1b2-45b3-b5ab-c1a3568582ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>137519</th>\n",
              "      <td>-0.784638</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.513401</td>\n",
              "      <td>-0.031432</td>\n",
              "      <td>-0.499079</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.657503</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.670216</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.189874</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.407717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22117</th>\n",
              "      <td>-0.758771</td>\n",
              "      <td>1.397009</td>\n",
              "      <td>0.886125</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.113459</td>\n",
              "      <td>0.924281</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.565811</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.719233</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.710658</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.606105</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.469512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>580114</th>\n",
              "      <td>1.465767</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.006481</td>\n",
              "      <td>-0.113459</td>\n",
              "      <td>1.362238</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.122347</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-1.033165</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>0.609025</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.746571</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.544665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a90304e-d1b2-45b3-b5ab-c1a3568582ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3a90304e-d1b2-45b3-b5ab-c1a3568582ae button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3a90304e-d1b2-45b3-b5ab-c1a3568582ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "137519 -0.784638 -0.748259 -0.807726 -0.288786  0.513401 -0.031432 -0.499079   \n",
              "22117  -0.758771  1.397009  0.886125 -0.288786  0.231779 -0.113459  0.924281   \n",
              "580114  1.465767 -0.748259 -0.807726 -0.288786  0.006481 -0.113459  1.362238   \n",
              "\n",
              "              E7        E8       E9       E10      E11        V1        V2  \\\n",
              "137519 -0.265267 -0.238101  0.37487  0.248200 -0.25066  0.657503  0.093365   \n",
              "22117  -0.265267 -0.238101  0.37487  0.565811 -0.25066  0.719233  0.266677   \n",
              "580114  0.085015 -0.859680  0.37487 -0.122347 -0.25066 -1.733963 -1.033165   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "137519  1.505344 -0.244269 -0.468227  0.670216  0.0  0.189874  0.0  0.553032   \n",
              "22117  -1.118540 -0.244269 -0.468227  0.710658  0.0  0.606105  0.0  0.553032   \n",
              "580114 -0.260667  0.609025 -0.468227 -1.746571  0.0 -1.102174  0.0 -1.859348   \n",
              "\n",
              "             V11  \n",
              "137519 -0.407717  \n",
              "22117   0.469512  \n",
              "580114  0.544665  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_new_df[numeric_feature_cols].sample(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "-I3wBJvmh4Xf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "-I3wBJvmh4Xf",
        "outputId": "23518af0-9f90-4efe-aedf-3d80dc70f8d2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8298ae45-f70c-44ab-b310-ed91d55d0baf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49295</th>\n",
              "      <td>0.896699</td>\n",
              "      <td>1.177920</td>\n",
              "      <td>0.427877</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.414224</td>\n",
              "      <td>-2.214411</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.319116</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.102417</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.291151</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.142396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25471</th>\n",
              "      <td>-0.319037</td>\n",
              "      <td>1.296504</td>\n",
              "      <td>1.050601</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>4.230814</td>\n",
              "      <td>-1.043097</td>\n",
              "      <td>0.376835</td>\n",
              "      <td>0.435296</td>\n",
              "      <td>4.112953</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.942127</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.820389</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>0.798012</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.165830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68199</th>\n",
              "      <td>-1.056239</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>0.705091</td>\n",
              "      <td>0.006481</td>\n",
              "      <td>0.652126</td>\n",
              "      <td>-3.601274</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.354070</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-0.674869</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-0.210401</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-0.064202</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.182999</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.064817</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8298ae45-f70c-44ab-b310-ed91d55d0baf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8298ae45-f70c-44ab-b310-ed91d55d0baf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8298ae45-f70c-44ab-b310-ed91d55d0baf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             P3        E1        E2        E3        E4        E5        E6  \\\n",
              "49295  0.896699  1.177920  0.427877 -0.288786  0.231779 -0.414224 -2.214411   \n",
              "25471 -0.319037  1.296504  1.050601 -0.288786  4.230814 -1.043097  0.376835   \n",
              "68199 -1.056239 -0.748259 -0.807726  0.705091  0.006481  0.652126 -3.601274   \n",
              "\n",
              "             E7        E8        E9       E10      E11        V1        V2  \\\n",
              "49295 -0.265267 -0.238101  0.374870  0.248200 -0.25066  0.319116 -0.079947   \n",
              "25471  0.435296  4.112953 -2.667592  1.942127 -0.25066  0.820389 -0.599884   \n",
              "68199 -0.615548 -0.859680  0.374870  0.354070 -0.25066 -0.674869  0.093365   \n",
              "\n",
              "             V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "49295 -1.068274 -0.291982 -0.468227  0.102417  0.0  1.291151  0.0  0.553032   \n",
              "25471  1.505344 -0.291982  2.135718  0.798012  0.0 -1.102174  0.0  0.553032   \n",
              "68199 -0.210401 -0.291982 -0.468227 -0.064202  0.0 -0.182999  0.0 -0.251094   \n",
              "\n",
              "            V11  \n",
              "49295  0.142396  \n",
              "25471  0.165830  \n",
              "68199  0.064817  "
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_new_df[numeric_feature_cols].sample(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WbZvIxyu0Asx",
      "metadata": {
        "id": "WbZvIxyu0Asx"
      },
      "source": [
        "Now, the next step  is `Preparing the Training and Validation Set` where we will use the `TrainTestSplit` to split the training set into `Training` and `Validation` set respectively.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84688de0",
      "metadata": {
        "id": "84688de0"
      },
      "source": [
        "# Preparing the Training and Validation Set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e4b2781f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "e4b2781f",
        "outputId": "e2c9a750-da57-48bf-9d51-7c178b3e0f1c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8153324d-a8af-4100-822a-c99ba407f1a9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.207100</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>0.705091</td>\n",
              "      <td>-0.444114</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>-1.265504</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.334088</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.563106</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.468009</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.315280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.207100</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>0.705091</td>\n",
              "      <td>-0.444114</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>-1.265504</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.334088</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.563557</td>\n",
              "      <td>1.219894</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.471244</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.312512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.207100</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>0.705091</td>\n",
              "      <td>-0.444114</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>-1.265504</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.334088</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.563331</td>\n",
              "      <td>0.786613</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.477714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.312582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.207100</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>0.705091</td>\n",
              "      <td>-0.444114</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>-1.265504</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.334088</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.563782</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.484185</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.311888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.207100</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>0.705091</td>\n",
              "      <td>-0.444114</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>-1.265504</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.334088</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.565584</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.487420</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.312794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604324</th>\n",
              "      <td>-0.732905</td>\n",
              "      <td>0.519228</td>\n",
              "      <td>0.937619</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>-1.338497</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.327677</td>\n",
              "      <td>0.786613</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.138005</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.533952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.417037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604325</th>\n",
              "      <td>-0.732905</td>\n",
              "      <td>0.519228</td>\n",
              "      <td>0.937619</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>-1.338497</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.327677</td>\n",
              "      <td>0.786613</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.138005</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.533952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.416081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604326</th>\n",
              "      <td>-0.732905</td>\n",
              "      <td>0.519228</td>\n",
              "      <td>0.937619</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>-1.338497</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.328803</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.136388</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.611995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.417108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604327</th>\n",
              "      <td>-0.732905</td>\n",
              "      <td>0.519228</td>\n",
              "      <td>0.937619</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>-1.338497</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.328803</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.136388</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.611995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.418618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604328</th>\n",
              "      <td>-0.732905</td>\n",
              "      <td>0.519228</td>\n",
              "      <td>0.937619</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>-1.338497</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.328803</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.136388</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.611995</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.418618</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>604329 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8153324d-a8af-4100-822a-c99ba407f1a9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8153324d-a8af-4100-822a-c99ba407f1a9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8153324d-a8af-4100-822a-c99ba407f1a9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "0       1.207100 -0.748259 -0.807726  0.705091 -0.444114 -0.168143 -1.265504   \n",
              "1       1.207100 -0.748259 -0.807726  0.705091 -0.444114 -0.168143 -1.265504   \n",
              "2       1.207100 -0.748259 -0.807726  0.705091 -0.444114 -0.168143 -1.265504   \n",
              "3       1.207100 -0.748259 -0.807726  0.705091 -0.444114 -0.168143 -1.265504   \n",
              "4       1.207100 -0.748259 -0.807726  0.705091 -0.444114 -0.168143 -1.265504   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "604324 -0.732905  0.519228  0.937619 -0.288786  0.119130  0.050595 -1.338497   \n",
              "604325 -0.732905  0.519228  0.937619 -0.288786  0.119130  0.050595 -1.338497   \n",
              "604326 -0.732905  0.519228  0.937619 -0.288786  0.119130  0.050595 -1.338497   \n",
              "604327 -0.732905  0.519228  0.937619 -0.288786  0.119130  0.050595 -1.338497   \n",
              "604328 -0.732905  0.519228  0.937619 -0.288786  0.119130  0.050595 -1.338497   \n",
              "\n",
              "              E7        E8       E9       E10      E11        V1        V2  \\\n",
              "0      -0.265267 -0.238101  0.37487 -0.334088 -0.25066  0.563106  0.526645   \n",
              "1      -0.265267 -0.238101  0.37487 -0.334088 -0.25066  0.563557  1.219894   \n",
              "2      -0.265267 -0.238101  0.37487 -0.334088 -0.25066  0.563331  0.786613   \n",
              "3      -0.265267 -0.238101  0.37487 -0.334088 -0.25066  0.563782  0.266677   \n",
              "4      -0.265267 -0.238101  0.37487 -0.334088 -0.25066  0.565584  0.526645   \n",
              "...          ...       ...      ...       ...      ...       ...       ...   \n",
              "604324  0.085015  0.383478  0.37487  0.036459 -0.25066  0.327677  0.786613   \n",
              "604325  0.085015  0.383478  0.37487  0.036459 -0.25066  0.327677  0.786613   \n",
              "604326  0.085015  0.383478  0.37487  0.036459 -0.25066  0.328803  0.526645   \n",
              "604327  0.085015  0.383478  0.37487  0.036459 -0.25066  0.328803  0.526645   \n",
              "604328  0.085015  0.383478  0.37487  0.036459 -0.25066  0.328803  0.526645   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "0       0.597205 -0.220759 -0.468227  0.468009  0.0  0.059802  0.0  0.553032   \n",
              "1       0.597205 -0.220759 -0.468227  0.471244  0.0  0.059802  0.0  0.553032   \n",
              "2       0.597205 -0.220759 -0.468227  0.477714  0.0  0.059802  0.0  0.553032   \n",
              "3       0.597205 -0.220759 -0.468227  0.484185  0.0  0.059802  0.0  0.553032   \n",
              "4       0.597205 -0.220759 -0.468227  0.487420  0.0  0.059802  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "604324 -1.118540 -0.267780 -0.468227  0.138005  0.0  1.533952  0.0  0.553032   \n",
              "604325 -1.118540 -0.267780 -0.468227  0.138005  0.0  1.533952  0.0  0.553032   \n",
              "604326 -1.118540 -0.267780 -0.468227  0.136388  0.0  1.611995  0.0  0.553032   \n",
              "604327 -1.118540 -0.267780 -0.468227  0.136388  0.0  1.611995  0.0  0.553032   \n",
              "604328 -1.118540 -0.267780 -0.468227  0.136388  0.0  1.611995  0.0  0.553032   \n",
              "\n",
              "             V11  \n",
              "0       0.315280  \n",
              "1       0.312512  \n",
              "2       0.312582  \n",
              "3       0.311888  \n",
              "4       0.312794  \n",
              "...          ...  \n",
              "604324  0.417037  \n",
              "604325  0.416081  \n",
              "604326  0.417108  \n",
              "604327  0.418618  \n",
              "604328  0.418618  \n",
              "\n",
              "[604329 rows x 23 columns]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# lets access our training dataframe\n",
        "train_new_df[numeric_feature_cols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7690f0e9",
      "metadata": {
        "id": "7690f0e9"
      },
      "outputs": [],
      "source": [
        "# Using train test split to split the scaled training set into training and validation set\n",
        "\n",
        "#importing TrainTestSplit\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(train_new_df[numeric_feature_cols], train_new_df['IsAlert'], random_state = 42, test_size = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06f65c63",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06f65c63",
        "outputId": "6c6bd7a1-7e64-4ac4-f70f-d48efb2c314f",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((483463, 23), (120866, 23), (483463,), (120866,))"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape, X_val.shape, y_train.shape, y_val.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "31507bb5",
      "metadata": {
        "id": "31507bb5"
      },
      "source": [
        "<b>Here, our training dataset is extremely large and my system is taking very long to fit and predict through the dataset. So,\n",
        "I have randomly selected 50,000 rows for the testing set and 10,000 for validation set. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b7b1639",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "7b7b1639",
        "outputId": "1c61a51d-1a18-4e82-fb44-b9a5d354a13b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cc41c46d-516e-48ed-a86b-0fcbf12e08bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>IsAlert</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>517921</th>\n",
              "      <td>1.026033</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.980233</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>4.399439</td>\n",
              "      <td>-0.974732</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.592893</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.503847</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540287</th>\n",
              "      <td>-1.082106</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>2.101267</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.830487</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.394588</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.220506</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.103160</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.006108</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244957</th>\n",
              "      <td>1.853768</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.277513</td>\n",
              "      <td>-0.499079</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.761362</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.743011</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.681367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.270053</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374229</th>\n",
              "      <td>1.543367</td>\n",
              "      <td>1.334087</td>\n",
              "      <td>1.923399</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.106168</td>\n",
              "      <td>1.034918</td>\n",
              "      <td>0.121360</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>1.005057</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.142329</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.716529</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-0.210401</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.662128</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.189874</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.435234</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471795</th>\n",
              "      <td>0.185364</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.550936</td>\n",
              "      <td>-3.309303</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.016476</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.718332</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.655657</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.063448</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110268</th>\n",
              "      <td>-1.004505</td>\n",
              "      <td>-0.304101</td>\n",
              "      <td>0.702900</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.725737</td>\n",
              "      <td>-0.058774</td>\n",
              "      <td>2.019174</td>\n",
              "      <td>2.186701</td>\n",
              "      <td>1.005057</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.942127</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-0.666308</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.372891</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.443651</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259178</th>\n",
              "      <td>-0.034503</td>\n",
              "      <td>1.648343</td>\n",
              "      <td>1.752904</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.218817</td>\n",
              "      <td>-0.113459</td>\n",
              "      <td>-0.207108</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.877838</td>\n",
              "      <td>0.786613</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.872424</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.362862</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365838</th>\n",
              "      <td>-0.357837</td>\n",
              "      <td>1.344052</td>\n",
              "      <td>-0.762440</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.387790</td>\n",
              "      <td>-0.004090</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.075181</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.456318</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.303007</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.814220</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.422174</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131932</th>\n",
              "      <td>-0.319037</td>\n",
              "      <td>1.336934</td>\n",
              "      <td>0.824353</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.006481</td>\n",
              "      <td>-0.222828</td>\n",
              "      <td>-0.280100</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.512876</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.542605</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.419479</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.554076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.305589</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121958</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-3.039085</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.710404</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-0.680051</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.078312</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.338596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285260</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.410837</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>483463 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc41c46d-516e-48ed-a86b-0fcbf12e08bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc41c46d-516e-48ed-a86b-0fcbf12e08bd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc41c46d-516e-48ed-a86b-0fcbf12e08bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "517921  1.026033 -0.748259 -0.807726 -0.288786  0.119130  0.980233  0.303842   \n",
              "540287 -1.082106 -0.748259 -0.807726 -0.288786  0.119130  2.101267  0.267346   \n",
              "244957  1.853768 -0.748259 -0.807726 -0.288786  0.119130 -0.277513 -0.499079   \n",
              "374229  1.543367  1.334087  1.923399 -0.288786 -0.106168  1.034918  0.121360   \n",
              "471795  0.185364 -0.748259 -0.807726 -0.288786  0.231779 -0.550936 -3.309303   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "110268 -1.004505 -0.304101  0.702900 -0.288786 -0.725737 -0.058774  2.019174   \n",
              "259178 -0.034503  1.648343  1.752904 -0.288786 -0.218817 -0.113459 -0.207108   \n",
              "365838 -0.357837  1.344052 -0.762440 -0.288786 -0.387790 -0.004090  0.267346   \n",
              "131932 -0.319037  1.336934  0.824353 -0.288786  0.006481 -0.222828 -0.280100   \n",
              "121958 -0.086237 -0.748259 -0.807726  3.686720  0.119130 -3.039085  0.303842   \n",
              "\n",
              "              E7        E8        E9       E10       E11        V1        V2  \\\n",
              "517921 -0.615548 -0.859680  0.374870  0.036459  4.399439 -0.974732  0.526645   \n",
              "540287 -0.615548 -0.859680  0.374870  0.830487 -0.250660  0.394588  0.526645   \n",
              "244957  0.085015 -0.238101  0.374870  0.089394 -0.250660  0.761362 -0.079947   \n",
              "374229  0.085015  1.005057  0.374870  0.142329 -0.250660  0.716529  0.093365   \n",
              "471795 -0.265267 -0.238101  0.374870 -0.016476 -0.250660  0.718332  0.526645   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "110268  2.186701  1.005057 -2.667592  1.942127 -0.250660 -0.666308  0.526645   \n",
              "259178  0.085015 -0.238101  0.374870  0.248200 -0.250660  0.877838  0.786613   \n",
              "365838  0.085015 -0.859680  0.374870 -1.075181 -0.250660  0.456318  0.266677   \n",
              "131932 -0.265267 -0.238101  0.374870  0.512876 -0.250660  0.542605  0.093365   \n",
              "121958 -0.615548 -0.859680  0.374870 -1.710404 -0.250660 -0.680051  0.266677   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "517921 -1.068274 -0.291982 -0.468227 -1.592893  0.0 -1.102174  0.0 -0.251094   \n",
              "540287  1.455078 -0.244269 -0.468227  0.220506  0.0  0.103160  0.0  0.553032   \n",
              "244957 -0.260667 -0.291982 -0.468227  0.743011  0.0  1.681367  0.0  0.553032   \n",
              "374229 -0.210401 -0.291982 -0.468227  0.662128  0.0  0.189874  0.0  0.553032   \n",
              "471795 -1.118540 -0.291982 -0.468227  0.655657  0.0 -1.102174  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "110268  0.597205 -0.220759 -0.468227 -1.372891  0.0 -1.102174  0.0  0.553032   \n",
              "259178  1.455078 -0.220759 -0.468227  0.872424  0.0 -1.102174  0.0  0.553032   \n",
              "365838 -1.118540 -0.315493 -0.468227  0.303007  0.0  0.814220  0.0  0.553032   \n",
              "131932  0.597205 -0.220759 -0.468227  0.419479  0.0  0.554076  0.0  0.553032   \n",
              "121958  1.455078 -0.078312 -0.468227  0.338596  0.0  0.285260  0.0 -0.251094   \n",
              "\n",
              "             V11  IsAlert  \n",
              "517921  0.503847        1  \n",
              "540287 -0.006108        0  \n",
              "244957  0.270053        1  \n",
              "374229 -0.435234        0  \n",
              "471795  0.063448        1  \n",
              "...          ...      ...  \n",
              "110268 -0.443651        0  \n",
              "259178  0.362862        1  \n",
              "365838 -0.422174        0  \n",
              "131932 -0.305589        0  \n",
              "121958  0.410837        1  \n",
              "\n",
              "[483463 rows x 24 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets join the X_train dataframe and y_train series\n",
        "trainingSet = X_train.join(y_train)\n",
        "trainingSet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e6247abf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "e6247abf",
        "outputId": "0df83170-bf33-4d89-b55c-6680eb9138ea"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-83cf7ead-2401-4c2b-8879-a7906acf2f68\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>191725</th>\n",
              "      <td>0.844966</td>\n",
              "      <td>1.449468</td>\n",
              "      <td>0.923246</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>-0.462583</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.648717</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.608745</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458690</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.168321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590126</th>\n",
              "      <td>-0.719971</td>\n",
              "      <td>0.298502</td>\n",
              "      <td>-0.062047</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.387790</td>\n",
              "      <td>2.948879</td>\n",
              "      <td>-0.389590</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.459941</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-0.528880</td>\n",
              "      <td>1.219894</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.031292</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-0.363470</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.059802</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.498099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268311</th>\n",
              "      <td>-0.409571</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.457077</td>\n",
              "      <td>-0.468909</td>\n",
              "      <td>-1.812950</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>1.005057</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.942127</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.575948</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.500362</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.391114</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.484077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110357</th>\n",
              "      <td>1.077766</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.613088</td>\n",
              "      <td>0.023253</td>\n",
              "      <td>1.070267</td>\n",
              "      <td>2.186701</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.730386</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-0.272048</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.193006</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.345961</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.456535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573149</th>\n",
              "      <td>0.288831</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.457077</td>\n",
              "      <td>-0.004090</td>\n",
              "      <td>0.084864</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.565811</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.810025</td>\n",
              "      <td>-0.339916</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.781835</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458690</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.507289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291241</th>\n",
              "      <td>-0.474237</td>\n",
              "      <td>1.615316</td>\n",
              "      <td>0.461093</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.569726</td>\n",
              "      <td>-0.004090</td>\n",
              "      <td>0.048367</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.122347</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>1.034866</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>1.084338</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.278385</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.250283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154381</th>\n",
              "      <td>-0.655305</td>\n",
              "      <td>-0.747761</td>\n",
              "      <td>0.734952</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.460729</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.181051</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.859852</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.149536</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.728777</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>-0.203161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>515172</th>\n",
              "      <td>-0.991572</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.513401</td>\n",
              "      <td>-0.086117</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>1.005057</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.069412</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.232153</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-0.025379</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.779534</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.456033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476038</th>\n",
              "      <td>-0.047436</td>\n",
              "      <td>1.550614</td>\n",
              "      <td>-0.183697</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.682374</td>\n",
              "      <td>0.324018</td>\n",
              "      <td>-3.126821</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.459941</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.783216</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.747864</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.117721</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.138621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>584962</th>\n",
              "      <td>-1.340773</td>\n",
              "      <td>-0.746835</td>\n",
              "      <td>0.649504</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.175454</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-3.090325</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.122347</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.859852</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>0.609025</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.728777</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.536410</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83cf7ead-2401-4c2b-8879-a7906acf2f68')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-83cf7ead-2401-4c2b-8879-a7906acf2f68 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-83cf7ead-2401-4c2b-8879-a7906acf2f68');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "191725  0.844966  1.449468  0.923246 -0.288786  0.119130 -0.168143 -0.462583   \n",
              "590126 -0.719971  0.298502 -0.062047 -0.288786 -0.387790  2.948879 -0.389590   \n",
              "268311 -0.409571 -0.748259 -0.807726  3.686720  0.457077 -0.468909 -1.812950   \n",
              "110357  1.077766 -0.748259 -0.807726 -0.288786 -0.613088  0.023253  1.070267   \n",
              "573149  0.288831 -0.748259 -0.807726 -0.288786  0.457077 -0.004090  0.084864   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "291241 -0.474237  1.615316  0.461093 -0.288786  0.569726 -0.004090  0.048367   \n",
              "154381 -0.655305 -0.747761  0.734952 -0.288786  0.119130  0.460729  0.267346   \n",
              "515172 -0.991572 -0.748259 -0.807726 -0.288786  0.513401 -0.086117  0.303842   \n",
              "476038 -0.047436  1.550614 -0.183697 -0.288786  0.682374  0.324018 -3.126821   \n",
              "584962 -1.340773 -0.746835  0.649504 -0.288786  0.175454 -0.140801 -3.090325   \n",
              "\n",
              "              E7        E8        E9       E10      E11        V1        V2  \\\n",
              "191725 -0.265267 -0.238101  0.374870  0.407005 -0.25066  0.648717 -0.079947   \n",
              "590126  0.085015  0.383478  0.374870  0.459941 -0.25066 -0.528880  1.219894   \n",
              "268311 -0.265267  1.005057 -2.667592  1.942127 -0.25066  0.575948 -0.079947   \n",
              "110357  2.186701  0.383478 -2.667592  1.730386 -0.25066 -0.272048  0.526645   \n",
              "573149 -0.265267 -0.238101  0.374870  0.565811 -0.25066  0.810025 -0.339916   \n",
              "...          ...       ...       ...       ...      ...       ...       ...   \n",
              "291241  0.085015 -0.238101  0.374870 -0.122347 -0.25066  1.034866  0.266677   \n",
              "154381 -0.615548 -0.859680  0.374870 -1.181051 -0.25066 -1.733963 -0.859852   \n",
              "515172 -0.615548  1.005057  0.374870 -0.069412 -0.25066  0.232153  0.093365   \n",
              "476038 -0.265267 -0.238101  0.374870  0.459941 -0.25066  0.783216  0.526645   \n",
              "584962  0.085015  0.383478  0.374870 -0.122347 -0.25066 -1.733963 -0.859852   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "191725 -1.068274 -0.244269 -0.468227  0.608745  0.0  0.458690  0.0  0.553032   \n",
              "590126  1.455078 -0.031292 -0.468227 -0.363470  0.0  0.059802  0.0  0.553032   \n",
              "268311 -1.118540 -0.244269 -0.468227  0.500362  0.0 -0.391114  0.0  0.553032   \n",
              "110357 -0.260667 -0.220759 -0.468227  0.193006  0.0  0.345961  0.0  0.553032   \n",
              "573149 -1.068274 -0.315493 -0.468227  0.781835  0.0  0.458690  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "291241 -1.068274 -0.291982 -0.468227  1.084338  0.0 -0.278385  0.0  0.553032   \n",
              "154381 -1.118540 -0.149536 -0.468227 -1.728777  0.0 -1.102174  0.0 -1.859348   \n",
              "515172 -1.068274 -0.267780 -0.468227 -0.025379  0.0  0.779534  0.0  0.553032   \n",
              "476038 -0.260667 -0.220759 -0.468227  0.747864  0.0  1.117721  0.0  0.553032   \n",
              "584962 -0.260667  0.609025 -0.468227 -1.728777  0.0 -1.102174  0.0 -1.859348   \n",
              "\n",
              "             V11  \n",
              "191725 -0.168321  \n",
              "590126  0.498099  \n",
              "268311  0.484077  \n",
              "110357 -0.456535  \n",
              "573149  0.507289  \n",
              "...          ...  \n",
              "291241 -0.250283  \n",
              "154381 -0.203161  \n",
              "515172  0.456033  \n",
              "476038  0.138621  \n",
              "584962  0.536410  \n",
              "\n",
              "[50000 rows x 23 columns]"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train = trainingSet.sample(50000, random_state = 42)[trainingSet.columns.tolist()[0:-1]]\n",
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfe85f24",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfe85f24",
        "outputId": "22072c86-35b7-4eea-fa07-91af0db3fe2c",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "191725    1\n",
              "590126    1\n",
              "268311    0\n",
              "110357    0\n",
              "573149    0\n",
              "         ..\n",
              "291241    1\n",
              "154381    1\n",
              "515172    1\n",
              "476038    1\n",
              "584962    1\n",
              "Name: IsAlert, Length: 50000, dtype: int64"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train = trainingSet.sample(50000, random_state=42)['IsAlert']\n",
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40a99f88",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "40a99f88",
        "outputId": "56ef54ec-1927-4cc0-ecb1-06223420fc1d",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c2597388-725b-435c-90e5-d87de4311915\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>IsAlert</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>551187</th>\n",
              "      <td>0.262964</td>\n",
              "      <td>1.551468</td>\n",
              "      <td>0.548411</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.004090</td>\n",
              "      <td>1.216253</td>\n",
              "      <td>0.435296</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.142329</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.704589</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.705805</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.831563</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.063343</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155822</th>\n",
              "      <td>-0.409571</td>\n",
              "      <td>-0.742066</td>\n",
              "      <td>-0.652994</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.870864</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.651699</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.149536</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.706130</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>-0.199969</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186872</th>\n",
              "      <td>-0.306104</td>\n",
              "      <td>1.284048</td>\n",
              "      <td>0.935568</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.556763</td>\n",
              "      <td>-0.250170</td>\n",
              "      <td>-0.061122</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.301135</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.509487</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>0.647471</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.443744</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.729301</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.145542</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66220</th>\n",
              "      <td>0.159497</td>\n",
              "      <td>1.538229</td>\n",
              "      <td>0.526251</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.058774</td>\n",
              "      <td>-0.791050</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.016476</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.771275</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.728452</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.408457</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.238162</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541241</th>\n",
              "      <td>0.857899</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.386882</td>\n",
              "      <td>1.909685</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.699182</td>\n",
              "      <td>-0.339916</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.642716</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571419</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.082187</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70728</th>\n",
              "      <td>0.185364</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.288103</td>\n",
              "      <td>-0.222828</td>\n",
              "      <td>-0.718057</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.459941</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.958943</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.940366</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.503843</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.268538</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438945</th>\n",
              "      <td>-0.991572</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-1.120008</td>\n",
              "      <td>1.636448</td>\n",
              "      <td>-1.849446</td>\n",
              "      <td>0.435296</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.936358</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.667867</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.584480</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.408457</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.166816</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557129</th>\n",
              "      <td>-0.461304</td>\n",
              "      <td>1.577306</td>\n",
              "      <td>0.209738</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.331465</td>\n",
              "      <td>0.788837</td>\n",
              "      <td>0.632310</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.301135</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.925600</td>\n",
              "      <td>-1.293133</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>1.115073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458690</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.022510</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449348</th>\n",
              "      <td>-1.107972</td>\n",
              "      <td>-0.747191</td>\n",
              "      <td>1.521092</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.113459</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.916375</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>1.486532</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>-1.725542</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.141309</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493164</th>\n",
              "      <td>-0.396637</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.195486</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.459941</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.741718</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.386205</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120866 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2597388-725b-435c-90e5-d87de4311915')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c2597388-725b-435c-90e5-d87de4311915 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c2597388-725b-435c-90e5-d87de4311915');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "551187  0.262964  1.551468  0.548411 -0.288786  0.344428 -0.004090  1.216253   \n",
              "155822 -0.409571 -0.742066 -0.652994 -0.288786  0.119130  0.870864  0.267346   \n",
              "186872 -0.306104  1.284048  0.935568 -0.288786 -0.556763 -0.250170 -0.061122   \n",
              "66220   0.159497  1.538229  0.526251 -0.288786  0.231779 -0.058774 -0.791050   \n",
              "541241  0.857899 -0.748259 -0.807726 -0.288786  0.119130 -0.386882  1.909685   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "70728   0.185364 -0.748259 -0.807726 -0.288786  0.288103 -0.222828 -0.718057   \n",
              "438945 -0.991572 -0.748259 -0.807726 -0.288786 -1.120008  1.636448 -1.849446   \n",
              "557129 -0.461304  1.577306  0.209738 -0.288786 -0.331465  0.788837  0.632310   \n",
              "449348 -1.107972 -0.747191  1.521092 -0.288786  0.119130 -0.113459  0.303842   \n",
              "493164 -0.396637 -0.748259 -0.807726 -0.288786  0.119130 -0.195486  0.303842   \n",
              "\n",
              "              E7        E8       E9       E10      E11        V1        V2  \\\n",
              "551187  0.435296  0.383478  0.37487  0.142329 -0.25066  0.704589 -0.079947   \n",
              "155822 -0.615548  0.383478  0.37487 -0.651699 -0.25066 -1.733963 -0.599884   \n",
              "186872 -0.265267 -0.238101  0.37487  0.301135 -0.25066  0.509487 -0.599884   \n",
              "66220  -0.265267 -0.238101  0.37487 -0.016476 -0.25066  0.771275  0.266677   \n",
              "541241 -0.615548 -0.859680  0.37487  0.248200 -0.25066  0.699182 -0.339916   \n",
              "...          ...       ...      ...       ...      ...       ...       ...   \n",
              "70728  -0.265267 -0.238101  0.37487  0.459941 -0.25066  0.958943  0.093365   \n",
              "438945  0.435296 -0.238101  0.37487  0.936358 -0.25066  0.667867 -0.079947   \n",
              "557129  0.085015 -0.859680  0.37487  0.301135 -0.25066  0.925600 -1.293133   \n",
              "449348 -0.615548 -0.859680  0.37487 -0.916375 -0.25066 -1.733963 -0.599884   \n",
              "493164 -0.615548 -0.859680  0.37487  0.459941 -0.25066 -1.733963 -0.079947   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "551187 -1.068274 -0.291982 -0.468227  0.705805  0.0  0.831563  0.0  0.553032   \n",
              "155822 -1.118540 -0.149536 -0.468227 -1.706130  0.0 -1.102174  0.0 -1.859348   \n",
              "186872  0.647471 -0.267780 -0.468227  0.443744  0.0 -0.729301  0.0  0.553032   \n",
              "66220  -0.260667 -0.267780 -0.468227  0.728452  0.0 -0.408457  0.0  0.553032   \n",
              "541241  1.505344 -0.315493 -0.468227  0.642716  0.0  0.571419  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "70728  -0.260667 -0.244269 -0.468227  0.940366  0.0 -0.503843  0.0  0.553032   \n",
              "438945  0.597205 -0.267780 -0.468227  0.584480  0.0 -0.408457  0.0  0.553032   \n",
              "557129  1.505344 -0.291982  2.135718  1.115073  0.0  0.458690  0.0  0.553032   \n",
              "449348 -0.260667  1.486532  2.135718 -1.725542  0.0 -1.102174  0.0 -0.251094   \n",
              "493164 -1.068274 -0.315493 -0.468227 -1.741718  0.0 -1.102174  0.0 -1.859348   \n",
              "\n",
              "             V11  IsAlert  \n",
              "551187 -0.063343        0  \n",
              "155822 -0.199969        1  \n",
              "186872 -0.145542        1  \n",
              "66220  -0.238162        1  \n",
              "541241 -0.082187        0  \n",
              "...          ...      ...  \n",
              "70728  -0.268538        1  \n",
              "438945  0.166816        1  \n",
              "557129  0.022510        0  \n",
              "449348  0.141309        1  \n",
              "493164  0.386205        1  \n",
              "\n",
              "[120866 rows x 24 columns]"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "validatingSet = X_val.join(y_val)\n",
        "validatingSet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0a65d33",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "c0a65d33",
        "outputId": "e063ec79-3a9a-40cd-a6d5-1afdf6011855",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ee14ddc2-06c6-4ea8-ba7d-9f2241a56573\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>417774</th>\n",
              "      <td>1.802035</td>\n",
              "      <td>0.495027</td>\n",
              "      <td>-0.271440</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.175454</td>\n",
              "      <td>-0.250170</td>\n",
              "      <td>-2.469885</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.942127</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-0.276554</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-0.210401</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.198184</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.055784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15331</th>\n",
              "      <td>0.043097</td>\n",
              "      <td>1.239775</td>\n",
              "      <td>1.023797</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.795023</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>1.471728</td>\n",
              "      <td>4.288388</td>\n",
              "      <td>2.869795</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.942127</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.445955</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.325654</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.117721</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.450135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396348</th>\n",
              "      <td>-0.150903</td>\n",
              "      <td>-0.747405</td>\n",
              "      <td>1.629281</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>0.378703</td>\n",
              "      <td>0.449828</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.286922</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.126025</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.502304</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>-0.621359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141203</th>\n",
              "      <td>1.000166</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.457077</td>\n",
              "      <td>-0.086117</td>\n",
              "      <td>0.011871</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.536522</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.403302</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.555872</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.453373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>512975</th>\n",
              "      <td>-0.150903</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>1.062260</td>\n",
              "      <td>-0.280100</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.618746</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-0.286918</td>\n",
              "      <td>2.693047</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.054803</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>1.634343</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.548513</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.529777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450748</th>\n",
              "      <td>-0.978639</td>\n",
              "      <td>-0.747547</td>\n",
              "      <td>0.593618</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.058774</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.810505</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>1.486532</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>-1.725542</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>-0.640820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75267</th>\n",
              "      <td>0.469898</td>\n",
              "      <td>-0.747761</td>\n",
              "      <td>-0.210116</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>3.216974</td>\n",
              "      <td>0.378703</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>0.740344</td>\n",
              "      <td>-0.847667</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>-0.210401</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.748189</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>-0.062115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339692</th>\n",
              "      <td>-1.030372</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-1.176332</td>\n",
              "      <td>0.159964</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.392792</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.615148</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.545656</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.230450</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.662810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>315885</th>\n",
              "      <td>-0.784638</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.378703</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.075181</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>-1.761130</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>-0.473697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263249</th>\n",
              "      <td>1.905501</td>\n",
              "      <td>1.427759</td>\n",
              "      <td>1.675754</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-2.021199</td>\n",
              "      <td>-0.332197</td>\n",
              "      <td>0.230849</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.016476</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.680032</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.683158</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.195764</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.326192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20000 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee14ddc2-06c6-4ea8-ba7d-9f2241a56573')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee14ddc2-06c6-4ea8-ba7d-9f2241a56573 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee14ddc2-06c6-4ea8-ba7d-9f2241a56573');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "417774  1.802035  0.495027 -0.271440 -0.288786  0.175454 -0.250170 -2.469885   \n",
              "15331   0.043097  1.239775  1.023797 -0.288786  0.795023  0.050595  1.471728   \n",
              "396348 -0.150903 -0.747405  1.629281 -0.288786  0.231779  0.378703  0.449828   \n",
              "141203  1.000166 -0.748259 -0.807726 -0.288786  0.457077 -0.086117  0.011871   \n",
              "512975 -0.150903 -0.748259 -0.807726 -0.288786  0.119130  1.062260 -0.280100   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "450748 -0.978639 -0.747547  0.593618 -0.288786  0.119130 -0.058774  0.303842   \n",
              "75267   0.469898 -0.747761 -0.210116 -0.288786  3.216974  0.378703  0.267346   \n",
              "339692 -1.030372 -0.748259 -0.807726 -0.288786 -1.176332  0.159964  0.267346   \n",
              "315885 -0.784638 -0.748259 -0.807726 -0.288786  0.119130  0.378703  0.267346   \n",
              "263249  1.905501  1.427759  1.675754 -0.288786 -2.021199 -0.332197  0.230849   \n",
              "\n",
              "              E7        E8        E9       E10       E11        V1        V2  \\\n",
              "417774  0.085015 -0.859680 -2.667592  1.942127 -0.250660 -0.276554  0.093365   \n",
              "15331   4.288388  2.869795 -2.667592  1.942127 -0.250660  0.445955  0.266677   \n",
              "396348  0.085015  0.383478  0.374870 -1.286922 -0.250660 -1.733963  0.266677   \n",
              "141203 -0.265267  0.383478  0.374870  0.248200 -0.250660  0.536522  0.266677   \n",
              "512975 -0.615548 -0.859680  0.374870  0.618746 -0.250660 -0.286918  2.693047   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "450748 -0.615548 -0.859680  0.374870 -0.810505 -0.250660 -1.733963 -0.599884   \n",
              "75267   0.085015  0.383478  0.374870  0.036459  0.740344 -0.847667 -0.599884   \n",
              "339692  0.085015 -0.859680  0.374870 -1.392792 -0.250660  0.615148  0.093365   \n",
              "315885 -0.615548 -0.859680  0.374870 -1.075181 -0.250660 -1.733963  0.266677   \n",
              "263249 -0.265267  0.383478  0.374870 -0.016476 -0.250660  0.680032  0.093365   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "417774 -0.210401 -0.291982 -0.468227 -1.198184  0.0 -1.102174  0.0  0.553032   \n",
              "15331  -1.118540 -0.267780 -0.468227  0.325654  0.0  1.117721  0.0  0.553032   \n",
              "396348 -1.118540 -0.126025 -0.468227 -1.502304  0.0 -1.102174  0.0 -1.859348   \n",
              "141203  1.455078 -0.244269 -0.468227  0.403302  0.0 -0.555872  0.0  0.553032   \n",
              "512975  1.455078 -0.054803 -0.468227  1.634343  0.0  2.548513  0.0 -0.251094   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "450748 -0.260667  1.486532  2.135718 -1.725542  0.0 -1.102174  0.0 -0.251094   \n",
              "75267  -0.210401 -0.315493 -0.468227 -1.748189  0.0 -1.102174  0.0 -0.251094   \n",
              "339692  1.455078 -0.315493 -0.468227  0.545656  0.0  1.230450  0.0  0.553032   \n",
              "315885 -1.068274 -0.315493  2.135718 -1.761130  0.0 -1.102174  0.0 -1.859348   \n",
              "263249  1.505344 -0.267780 -0.468227  0.683158  0.0  1.195764  0.0  0.553032   \n",
              "\n",
              "             V11  \n",
              "417774 -0.055784  \n",
              "15331   0.450135  \n",
              "396348 -0.621359  \n",
              "141203 -0.453373  \n",
              "512975  0.529777  \n",
              "...          ...  \n",
              "450748 -0.640820  \n",
              "75267  -0.062115  \n",
              "339692 -0.662810  \n",
              "315885 -0.473697  \n",
              "263249  0.326192  \n",
              "\n",
              "[20000 rows x 23 columns]"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_val = validatingSet.sample(20000, random_state = 42)[validatingSet.columns.tolist()[0:-1]]\n",
        "X_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0f6a2d78",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0f6a2d78",
        "outputId": "0e4a3a1c-9411-4204-9df2-3538a7c47caa",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "417774    0\n",
              "15331     0\n",
              "396348    1\n",
              "141203    1\n",
              "512975    1\n",
              "         ..\n",
              "450748    1\n",
              "75267     1\n",
              "339692    0\n",
              "315885    1\n",
              "263249    1\n",
              "Name: IsAlert, Length: 20000, dtype: int64"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_val = validatingSet.sample(20000, random_state = 42)['IsAlert']\n",
        "y_val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8605ad6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8605ad6",
        "outputId": "c5b43d73-f05c-47da-dd13-2a2b7215a8a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((50000, 23), (20000, 23), (50000,), (20000,))"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train.shape, X_val.shape, y_train.shape, y_val.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bce81b3",
      "metadata": {
        "id": "5bce81b3"
      },
      "source": [
        "# ---------------------------------------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac519fd9",
      "metadata": {
        "id": "ac519fd9"
      },
      "source": [
        "# Training with KNN model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51a16be1",
      "metadata": {
        "id": "51a16be1"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "Knn = KNeighborsClassifier(n_jobs = -1) # by default, n_neighbors parameter is set to 5"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc446870",
      "metadata": {
        "id": "fc446870"
      },
      "source": [
        "Here, `X` is capital and `y` is small. This is because `X` represents feature matrix whereas `y` represents target or response vector."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b38c91e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "3b38c91e",
        "outputId": "3482c7b4-e712-4996-d926-ea6d9dfdbfbc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 11.4 ms, sys: 62 µs, total: 11.4 ms\n",
            "Wall time: 11.4 ms\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_jobs=-1)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(n_jobs=-1)"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "Knn.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "16xoYOgQdnZX",
      "metadata": {
        "id": "16xoYOgQdnZX"
      },
      "outputs": [],
      "source": [
        "X_predictions = Knn.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ee76df2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ee76df2",
        "outputId": "88cf4329-3e49-4ea0-e9ed-78e46c2031c1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.91228"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Lets check the accuracy for training set\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_train, X_predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c7924f75",
      "metadata": {
        "id": "c7924f75"
      },
      "source": [
        "The accuracy on the training set is calculated to be approximately 91.3 %. Lets calculate this for the validation set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "da5cb17c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "da5cb17c",
        "outputId": "28362933-68e0-43af-e2a4-769555ffa1ff",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10.1 s, sys: 24.8 ms, total: 10.1 s\n",
            "Wall time: 6.3 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "# calculating predictions for validation set\n",
        "val_pred = Knn.predict(X_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "11533597",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11533597",
        "outputId": "006f9a16-2009-429c-bc47-f82f8d382b4f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "86.91499999999999"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# lets check accuracy for validation set\n",
        "n_5_accuracy = accuracy_score(y_val, val_pred)*100\n",
        "n_5_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f7aa59f1",
      "metadata": {
        "id": "f7aa59f1"
      },
      "source": [
        "The accuracy measured for the validation set is 87 % approximately, which is very good. This also indicated that our model hasnot `overfitted` with the training set.\n",
        "\n",
        "Lets tune the hyperparameters for our knn model and see if there is any improvement on our model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e83b70c1",
      "metadata": {
        "id": "e83b70c1"
      },
      "source": [
        "## Hyperparameter Tuning for the KNN model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83bb9330",
      "metadata": {
        "id": "83bb9330"
      },
      "source": [
        "By default, our n_neighbors parameter is set to 5 in our KNN model. Let's check for which particular value of n_neighbors, our model will have maximum accuracy for both training set and validation set."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2434f29a",
      "metadata": {
        "id": "2434f29a"
      },
      "source": [
        "### Tuning the value of n_neighbors"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dfcabea6",
      "metadata": {
        "id": "dfcabea6"
      },
      "source": [
        "<b><u> What if n_neighbors is set to 3, 4, 6, and 7? </u></b>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b82c84f",
      "metadata": {
        "id": "7b82c84f"
      },
      "source": [
        "<b> When n_neighbors = 3? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d260f0c2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "d260f0c2",
        "outputId": "88b8a5f4-f8e7-4c4f-ee4d-81ed54c1406c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 11.9 ms, sys: 0 ns, total: 11.9 ms\n",
            "Wall time: 12.8 ms\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_jobs=-1, n_neighbors=3)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsClassifier(n_jobs=-1, n_neighbors=3)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "Knn = KNeighborsClassifier(n_jobs = -1, n_neighbors = 3)\n",
        "Knn.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3bdc8fb7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bdc8fb7",
        "outputId": "fe3a3c5e-b319-45af-aa9c-59e167bed451"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10 s, sys: 28 ms, total: 10.1 s\n",
            "Wall time: 5.52 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 1, 1])"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "n_3_pred = Knn.predict(X_val)\n",
        "n_3_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b9106446",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9106446",
        "outputId": "844d7e74-7ae1-4635-abf7-2dfbd3271e75"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "87.41499999999999"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "n_3_accuracy = accuracy_score(y_val,n_3_pred)*100\n",
        "n_3_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f3393eb",
      "metadata": {
        "id": "1f3393eb"
      },
      "source": [
        "<b> When n_neighbors = 4? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "35baaaac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35baaaac",
        "outputId": "b165e741-64ea-4e13-f961-b832efb2290f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10.1 s, sys: 17.4 ms, total: 10.1 s\n",
            "Wall time: 7.04 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "86.185"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "Knn = KNeighborsClassifier(n_jobs = -1, n_neighbors = 4)\n",
        "Knn.fit(X_train,y_train)\n",
        "n_4_pred = Knn.predict(X_val)\n",
        "n_4_accuracy = accuracy_score(y_val,n_4_pred)*100\n",
        "n_4_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3f2925c1",
      "metadata": {
        "id": "3f2925c1"
      },
      "source": [
        "<b> When n_neighbors = 6? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ce9a4f2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ce9a4f2",
        "outputId": "724b1c60-b840-4719-dd8a-b5944f46e16d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10.2 s, sys: 56.5 ms, total: 10.2 s\n",
            "Wall time: 5.56 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "86.435"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "Knn = KNeighborsClassifier(n_jobs = -1, n_neighbors = 6)\n",
        "Knn.fit(X_train,y_train)\n",
        "n_6_pred = Knn.predict(X_val)\n",
        "n_6_accuracy = accuracy_score(y_val,n_6_pred)*100\n",
        "n_6_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc6d1c87",
      "metadata": {
        "id": "fc6d1c87"
      },
      "source": [
        "<b> When n_neighbors = 7? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9c4e230",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9c4e230",
        "outputId": "8f92ddf2-fac8-4e4d-cd59-557c2bc441ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10.1 s, sys: 33.5 ms, total: 10.2 s\n",
            "Wall time: 7.08 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "86.435"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "Knn = KNeighborsClassifier(n_jobs = -1, n_neighbors = 7)\n",
        "Knn.fit(X_train,y_train)\n",
        "n_7_pred = Knn.predict(X_val)\n",
        "n_7_accuracy = accuracy_score(y_val,n_6_pred)*100\n",
        "n_7_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "55061668",
      "metadata": {
        "id": "55061668"
      },
      "source": [
        "<b> When n_neighbors = 8? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ec1c996",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ec1c996",
        "outputId": "eca92a63-a870-4db3-d0e6-88ec27f1a411"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10.1 s, sys: 21.7 ms, total: 10.2 s\n",
            "Wall time: 5.55 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "86.435"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "Knn = KNeighborsClassifier(n_jobs = -1, n_neighbors = 8)\n",
        "Knn.fit(X_train,y_train)\n",
        "n_8_pred = Knn.predict(X_val)\n",
        "n_8_accuracy = accuracy_score(y_val,n_6_pred)*100\n",
        "n_8_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b302911",
      "metadata": {
        "id": "7b302911"
      },
      "source": [
        "I also tried to calculate both training and validation prediction score for value of n_neighbors ranging from 1 to 30. I let my laptop run the predictions , and It was still running for 1 hour. So, I decided to do predictions for only n_neighbors = 3,4,5,6,7, and 8."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff841c91",
      "metadata": {
        "id": "ff841c91"
      },
      "source": [
        "Lets vizualize the calculated validation accuracy for different n_neighbors values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2620c4dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2620c4dd",
        "outputId": "aefbbe4f-ad30-41e2-b8df-3cdcec4b8a66"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[87.41499999999999, 86.185, 86.91499999999999, 86.435, 86.435, 86.435]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "score = [n_3_accuracy,n_4_accuracy,n_5_accuracy,n_6_accuracy,n_7_accuracy,n_8_accuracy]\n",
        "score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "90dd60a6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "90dd60a6",
        "outputId": "5fe319bf-d581-4fc1-db2f-de728054e467",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2gAAAIyCAYAAAC+dOkYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACgN0lEQVR4nOzddXycVfbH8c/Em6Rpi7vDQRdb3F1r6OJFlxVYYGHxUtzlt8Cyi/vipcFdiy3OLnDQFmmRFmpJO0km8/vjPtOGkDSZyaTPJPm+X6+8pjOPnUlu0ufMvffcRDqdRkREREREROJXFHcAIiIiIiIiEihBExERERERKRBK0ERERERERAqEEjQREREREZECoQRNRERERESkQChBExERERERKRBK0ERERERERAqEEjQREREREZECoQRNRERERESkQJTEHYCI9F5mNgK4CXjB3beMN5rCYmYDgbOBwcDihL/H+j5Jl5jZlsBzwHh3X6bVtpuBg4Az3X1Ulud9HtgCONjdb+56pJ2+7jhgaWArd39+Xl1XRCROStBE+gAzuwkYAbzu7ht28pjXgA2A69398G4Mr696kHDDOxN4P3r8oDMHmlk6+mebN61mVgzcQLgZbwQOdPe7uh6ydJaZnQGMAiYAS7l7qhPH3AXsDTzt7tt1b4Txij6gOAYg22SxpzCzM4GR0dPT3f2cOOMRkZ5DQxxF+oaboscNzGzljnY2s1UIyRnAjd0WVR9lZqszJzlb1d3XdfdN3f2oPJy7DLiHkJzNBIYpOYvFzUAaWAzoMNkys0HAsOhpd/3OTQQcmNRN58/GQOCM6GtuPifEXN/dAeWTmRURfgczRphZIq54RKRnUQ+aSB/g7i+a2WfACoSetJM6OOTg6PEjd3+1O2Pro1aNHv/r7uPydVIzqwRGA9sD04DB7v5ivs4vnefu483sWWAbwu/c4x0csg9QDvxM+Bl2R0wnAyd3x7m7i7tvE3cMOdqGMDSznnCvtTzhQ5nnY4xJRHoI9aCJ9B2ZXrQDoiFwbYq2HdDqGMmvftFj3noFzGwA8CQhOZsEbK3kLHaZnrBh0ZC+ucl8KPJvd5/VfSHJPHJI9PgA8FCr10RE5ko9aCJ9xy3AWYQhV9sDj7Wz307AIkATcCuAmS0B7B5tWzE6RyPwKeHT/v9z9+nZBNNiHtWybfUidVRgJEokDwT2B9YEaoAfCZ9QX+Tu77VxTBHhRvhAYA2gPzAV+AF4DbjN3Z/L8n3MD/wVGAIsSxjW9gXh+3KZu09t4z1lbNHi+wA5FkIwswWBJ4C1gW+B7dz9oyzP8QywNXCqu5/Xzj5FwFeEoiZ7uvt9rbavA/yF0FOwCDCLML/uRuBWd29utf+WtChoYWb7AEcSfjaDaPH9MLPdgcOBdaJtMwg/77eAe9x9dIvzjqCD4jRzK3phZlsDRxGG+S5ISKQnRe/lIXfv7BDEB4AphOF8+wDXtBPL6sBvo6c3Rq8tAAwHdiH0uC4ebf8SeBS4xN1/6GQcmevczFyKhJjZmoR5c5sTPkT4nPB34/K5nLMM2DX6Wi+KsxL4DngxivP9duLIPG/5OwAtfiYdFQkxsw2BY4FNgQWA6YQ2ca2739/G/ssQvoe4e8LMNiX0Km4Yxf0pcC1wtbu3jqtTWg1XvYXwvdwd2N3M/uzu0+Zy7EDgz4TiQStFx04ktL373f3WNo5ZlPB7tyOwHOHe7lvC9+EOd3+oxb4300GhmPb+Nrc8ltAmTone51LA95miNGa2ErAbsEMUz8KEvwUfAncB/3T3hrl8D9Ym/P5tQfi/Jgl8TfhbcaO7v2tm5YT5nfMB27v7U+2ca3nCzzRFmAs6sb3rihQS9aCJ9BHu/i2hhwXmfFrflsy2R939++jfxwBXEG7cEoRiFj8SEqOzgdeim5J5IrrWc4Sb2a0J/4H/l5Bw7Qv8x8x+18ahtwDXE97HTOBdYDLhBuNg4NAs41gVeI9wg7cy8Bnh5m81QnGAd81shRaHfA+MJdwwQBiGOLbF11SyFCXPLxGSs8+BTbNNziKZG78D5rLPNoQb8J+Z0yuQieME4E1C8jsf8DHhZnkzQrJ0fwc9t5cDdwJG+D5OaLHtbOA+wg0fhJvVCYQbv98REuS8MLPDgGcIN579gP8B4whJ4TDChxydEvWE/Tt62pnfuffd/a3o378jJAo7EYY+fki46V4JOAF4x8yW7WwsHTGznYA3CO+xIrpef+BiwpzG9qwE3E+4cV+A8L36DJif0Jb+Y2aDWx3zCaGtZIxt9fU9nWBmxwKvAHsRflbvE/4WbA/cZ2a3RB8qtHf8COAFQnL2BaG9rgFcSXjfudqX8D38BniW8GHYD4QEsK2/S5l41iV8388G1id8KPBBdNwQwt+v1sfsSPhdOzGK/RvgI8Lv4N7Re8m3+Qk/vxMIic+H/HI0wHnA+YQPHRoIP5cpwEbA/wFPRon9r5jZqYTE8mBCcvYx4T0tS0jajgFw9yRzvh+HzSXWwwj/Zz2i5Ex6EiVoIn1L5pP/IW0lVNGn9oNb7QvhBmNLoL+7L+fu67v78oT/NGsJn/Cf321R/9odhBv/l4E13H1xd1+H0FNxLFAM3BR9kguAma1F6G2bRvg0fnF3X8/djXAjukX0Xjol+gT3AULC8jqwnLuv6e5rEG5a3wOWIdwoFgO4+2PuvinhBgbgnag4SObrnSy/DytE3wMj3Mht2oU5bfcDdcDKZrZ+O/tkkre7oxskAMxsb+AiQoJ5EDDQ3ddy9yUJN5qfEW78T2nnvEsAfyD8fBZ19/Wj116N2uTJhB7dvYCFo6Iqq7n7AMJNYF6G4kY/pwuip0cDC0bvY013nw9YJXqf2cj8Hq0XJfStr1lCeN8t94WQLO0M1Lj70lFbXYlw03pd9Nhmj1y2oh7Y24Ey4F7Cz+C3UY/IUEKSuHE7h/9IaBcLuvui7r6Ou69OSNb+TPhdvDmaHwlA1EO7Z4vnm7b6aq93v2XMWwGXEm6+zyK0i/XcfXFgP0JicCBw3FxO809Ccr9QdOxCzGmjx0W9L7nIDGW8zd2b3b2J8Der5bbW72dh4BFgUcIogJXcfcUoroUJf0vObnXMqoTf2xrCBxhLunum6ND8hA+K8tJGWvkD4W+FZa4HrNti+22E3uea6D2sH7WlVQgjFbagjZ9LlDCfQxiFMBKY393XjtpTf0KxnWdaHHJt9DgsGsnQ+nwlzOmpvS7H9yoSCyVoIn1LLeFT2XLCkKvW9gNKCZ9gP5J50d2fcfcXWpcKd/evo/M0AvvNrYckX8xsW8IN41eEIhj/bRFPs7tfAVxN+AT7mBaHZqpXPtt6qJS7p939RXefW09Ba3sREqMGYA93/6rF+T6PtqcIvYzDsjhvNq4hDP/6D7Clu3+X64ncfQZzilMc2Hq7mVURhi3BnN62zE3QhdHTQ9z9F0MZ3f0/hF6DNOGmt61PzouBs9z9jsywsuhnkiQkocWEgir3th525u5vufsN2b/jNi1I6B2Y4u5XRjfWLa/1sbv/PZsTuvubhB4ECMVCWtsFWIjQjm5vcdwbUUKfbLmzu09y9yMIvWnbm9ki2cTTjiMJPS7fEZZkmD0Ez91rCTfNpW0d6O7fu/vt7v5Tq9eT7n41YUjbfMz54CdfTiMkZ4+6+xnu3tji2ncyJ5E+MfowpS23u/sVLf+uufv5hA87EoSfTVbM7DeEYbjwyx6vm6PHDdpK1IG/EXqEHdjZ3T9tudHdx7v7yFbHnE3oXXsB2NvdJ7Q65kN3v5D8SxGqw86O0d1ntvj3mKj9tv5d/Zg5H/K0rHCZGSqb+eDqHHc/293rWxybdven3f22Vud7gfDBwq/+ZhF+fosSeuA6KtIjUlA0B02kD3H3BjO7gzBf4WDgH612yQy1uq31zamZ1RCGzGxM+PS+knATA9AMVBPmp33cPdHPtnf0+G93n9LOPvcThsO0rAA3Pnrc0MyWc/cvuhhH5ubtHnf/pvVGd//EzGqZM4/oV/Nh8mh+oAr4qaMdO3AroTfnd2Z2bMubXkJyVgV84r+s7LkBIUn8zlvMA2vJ3d8ys/GEXoB1gbYqg17fTkyZn9tKZvbbKOHpLj8Qhr4OMLOd3f3RPJ33JsKcnQPM7ORWH3Rkfudq3X1yy4PMrILwfd+C8D2uYs7vXP/o32vR9ZvPnaPHa73tAiVXE+YdtXvPYGbbROdZidCjk/mwZqnocW3g7i7GmblWFeF7AnBZO7tdTuh5XYDQRtsqmHN1O8e+ShguuEI72+cm00P2urt75kV3f9/M3iX8vA4mDA9saffo8bKWyU57oraR+Rt0nrea39nNnvEOeurNbCHCBzPrExLPCua03WgX69fivW5MSKaShJ7RzvoXoS0cyq/nSmaGPt7U+sNFkUKnBE2k77mJkKD91sxWc/f/weyJ2Wu22Gc2M9ucMPRpoQ7O/athJt0gE+Nu0QT/tlREj0u2eO01wqetWwCfmNlLhJu2V4GXox6kbFj0+N+57PMBIUHrcO25HB1FWEdqOeB5M9sy6tXM1TOEnpnFCTd/D7bYlvmE+rZWx2R+Hv3M7OW5nDvTNpbk1wnaJG+n4IW7TzSz2wifvL9hZm8QhoC9Crzo7j/P5ZpZcfdmM7sEOB14xMw+IHxPMtfKtYfyDkKPziKEQg6PwOyb2MxNduvfuVWi/TqaZ5aP37lM+/xfWxvdfaqZfUNIsH/BzKoJQ307Wustn38bMr2q0M7vn7v/ZGbfEhLElWk7QfuknfNn5sD1zyaoqBdov+jpr+aLEXrRrmBOot4UHdefkIBDmFPXGSsSRkJkc0y+tNlOMsxsD0J7rp7LbglCz+q30fM1osf/+lyKqLThAcKokNXMbEN3fy2KYXHCSItmIF897CLzjIY4ivQxHqobvh09bVm4IPPv1939w8yLUc/ZfYTk7BnCjdjCQJm7J9w9QaiwBe0Mg8qzzNy5FYFN2vnKzIfIlLMnGm6zK3AuoSraloR5Do8BP5jZDdF8p87K3LzN7aY9Myk9qxu9LHwMbBXFkEnSlpz7Ie2LPoXPDLObPWQoutnZmjBMsXWClvl5DKD9n8cmzPkeVPJrdR2EdihhCNhnhN6QEwnJ4w9mdn9UmS9fziB88v4e4abxGELPzwQzeyqqdJgVd/+ROUVVWv7O7c+cintPZF6MClvcT0jO3iEUiFgcKG/xO/dStHs+fucyP5u5Fedob9slhL8JkwjftxWAyhZxZuZN5fNvQybeZkKvZ3vm+vvn7u21u0xvVLYLSw8h9Ng1EIZ2tnYnYTj4wvxy+GRNi39P6eS1Msekcvhwqava/X2NfhdvJyRn9zCnumZp1B5aDoNv2SYy72dKNoFEQ4Bvjp62LBZycHStp9x9fOvjRAqdetBE+qYbCfMk9jezkwgf1uzbYltLOxPm5nxNmPP1i+E3ZpZgzk16Ltq7Capq5/XMzcgh7p5VcYjoRuY04LSouuImwLaEYWSHEApkbN7J4TCZZQXmNgdo0Vb75p27fxwVTHiOkKQ9Z2ZbdaEn7VZCArSLmc0XzS3aj9BGnm/jZifz83jR3begG0RDLS8GLo6qVm5CSEz3Ivzs1jSztVrcqGbmvsztBrvN9hUl8jcAN0TFMzYh9LruTWgrz5rZbzxURc3GjVGsg81s/mg4YyZZu7VVm1ufUFBhJqGE+KQ2zpfPHqnphAI7C89ln19ti+YfZnqMRrj7I633oXt61TO/T0WED47aSx67/fevlczwxjLgJzPraN8x0b9b9hgNJMyZ6kjmmGIzq84iSZvr70Y0fLQrfkfo2XsD2KeNoZfttYfM+xmYwzWvJRR72dvMjiEkkJmfhYqDSI+kHjSRvulOwro0CxOGgQwh/MdZz68/+c0MsfpPO3MjVmfuQ1nak/kUtr2bwpXaef2D6PE3OVxzNnf/zN1vcfcDCGW204R5EGt18hSZuXarz2WfzLCdXMred1o0WT7Tk7Y8IUlbIsdzfUgoc13GnJLgmYn9bQ3byvw8VptbSfN8cfdv3P1ud8+slzaN8J53aLFbR20LQg9sR9f60d0fdPdjCUNavyQMy2q3VPpcPE5YGqAM2NfMfsucttP6g4bM79xHbSVnUQXW9n4/cpFpy20Vr8gsgt5We1qQOb/77S2KvmE7r+e0xljkM0JVT2jn9y/6HmXWjuvW37/oeosTyvtD6E38vp2vzM9z56hyIx7WkBwXvd5etczWPiH8Dc/mGMj9725nZdruy+3Mi2uvPWQK6aweDfnstKhYyXOEtvg7wtzjZQm9q52uzCtSSJSgifRB0bydB6OnI5jzSf79bYz/z1TSWpS2tZ7s3lmZCmAbtd5gYbHWtqpMwpw1mQ7M3OB0lbt/wJw1yBaf274tZHoL9morGYp66Ia02rfbtJGkPZ9rkkaLNdGiuYmrE9rBfW3s+zIh8ZifLNeR66qoF+vL6GnLn1umbS3bVhsxs/0IQzKzudZ05txEdraNtDw+xZzv6wjm/M691LpiH3N+5xaOeqhbO5b8joDJlLU/op2Kh39s53ot17761d8HCwt+r9P69dbHtizB3xnR0MQXoqftldE/hjDEbRKhN6e7HdTieou5+yJtfRG+Tz8Qvp8tKw9mfreOjQqAzFU0tO/h6OlJ7bSTtmTaWnuJ0h87eZ72tPv/RRTj8e0c9wrh70g5c18aoT3/ih4PY85Qx1taFToS6TGUoIn0XZmhjLsyp/eh9fBGmPPJ+EZmdkTmRTMrs7CAcGbNoWxlPtk8oeW8nqhs+J20M9TF3R8mLLg9H6Gn6FeFQsxsOTP7m4VFhzOv7W9mZ5nZyq32LbWwyPJAQvnozq5Fdg+hJHYZcK+ZZarVYWbLRduLCXOZxrR5hjzLY5L2b8JcmQ2ZszDzA20No3L3BuYk6Vea2TFm1q/lPmZWbWa7m1l7lRrbZWbbmtkVZrZOy5tQMyuKEq1MD8p/Whz2AaFHogy4umUCEFUbvCJ6f62vtWo0F3HT1r2BZrYdc6qC/qf1sZ2U+f1ahzkJWlu/c69G8S0OnGXR8hXRe/4TYa2utqot5uqfhLk/ixLWLJvdg2FmuxKGBf/q++XuUwntG+CK6IOVzHFbEnrj24tzEnM+FNk2h5jPJfTC7Wxmo8xs9nwmC+vynRg9vcBbLVXQTUZEj3fMLSmICoNk5nm2nI94MaGHbWVCgZpfVJA0s6XN7MxWpxtJSIi2Av5tZou2OmZVMzux1TEPE75va0Z/9zL7FpvZUcxZky9XmcR5TzObPc8ualPXE4bv/kr0PTs5ejrSzE5p+XfEzBJmto2ZtRffaELiuwFzKmJm/fdGpFAoQRPpu54hrCVWRkgkPmfOf66zeVg8OVMY4l9m9q2Z/Yfwn+FphJuEia2P64TLgC8IN4Vvm9nHFspQf01YYLX1zUhLewNPE+bpvGRm35vZG2b2lpn9EL2XC/nlsKwFCNX5PjKzSdG+b0fvY/aaSZ2duxUlJrsTPvXdEPjCzN4zs/cJn1KvTUgS9piXJZ7zMdwxKmqRKd2+a/R4azu7Z9adOobQji4nzL9518xeM7PPCDfi95HbjXg1oeroW8AUM3vHzN4kvL/bo2te2bL0fzS06lhCsYfdge+jn/d4Qruppe3Kd2WEuSsvAdOjn+cbFqoBPhnFMoYcy8VHPWWZSpf9CHOj7m1jvx+Ys2D2acDE6HfuO+AqwpDI13OJoZ24fiAMY20kDBH7zsz+Y2ZfEoqbPEH7lQL/RvhgYyfgGzN728y+IAw5+zaKt61rtiw4M9rM3jez56OvHTsR83OE3pg0obBL5m/AN4TEsDw6f+vS63lnocptZshsWwl3a5l9VjGzjWD2z2BXws94a+BTM/sk+jl8R/hb8ot10Nz9I0L7nk74m/iNmf0vauuTCNUW/9DqmM+Z8z25yMx+aPH3/IrW++egllBltQR42My+aPH7OgI4or0D3f1Wwt/oBCEBnxy1pw+i9/g07fwNiRK8zFDhEuAFd2+vSqdIwVOCJtJHRTexN7d46WZvtbBoCwcTPpF2wryT5QmVIIe7+7k5Xn8qoQDDdYRPjpcjFBv5J6GHod0iDB7WP9sB2JNww5wizB1bhTAn6d+EIZIt10i6n3BD90i0jxESwemEG+7N3T2b9XeIlij4DXA+4XuzIuF78yGhet3a7v5ZNufMh1ZJ2grkNiet5XyzbwkJ/dyu+X+EOWFXE4YdrkCopllN6IU9kY5LsbflJeBPhHLamXayJmEO0kPAEHc/uo14HiS0keejl1YGfgQOc/f2hmJ+QhimeSfhw4slCYl2GeHm8EBgt3bm1nRWyxv4e9qrJOhhUeLDgHcJFe5WInzwcJi7H96F67cp6pnegPD7lCT0TNYRekf3nMtxTxLa2tOEZGnl6PhzCL/fc6vQeQLhRvwTwu/OFtFXpxbfdvfLCPOv7iX01K1FSHyfAvZ09wO7+LPqrExP2Fvu/v5c92T2343MsMtDWrz+JnM+nHqH8OFVZnjxg7TRu+XujxP+7l1GmEu4DOFv28+EdtzWkMXjgT8ThuzWEH5X/wNs7e43dxR/B++tmVBY6gLC34ElCEsdPBedv3UV2NbHn0Noh7cTfl9XI/QkfwH8H+2vewe/LAii4iDSoyXS6a7M0xURERERiZeZbQyMJSSni3nbi66L9AjqQRMRERGRnu730eNtSs6kp1OCJiIiIiI9lpmtRxjWnqKdeY8iPYkWqhYRERGRHsfMngcqCXNFS4Cr21i2QqTHUYImIiIiIj3RFoTiNN8AdxAqeor0eCoSIiIiIiIiUiA0B01ERERERKRAaIhj90oAixHWWRIRERERkb6tPzCBMDy3TUrQutdihHHRIiIiIiIiEBZx/7a9jUrQutd0gMmTpxP3VL9EAuafv39BxCI9g9qMZEttRrKlNiPZUpuRbBRae8nEQwej65SgzQPpNAXRKKCwYpGeQW1GsqU2I9lSm5Fsqc1INnpae1GREBERERERkQKhBE1ERERERKRAKEETEREREREpEErQRERERERECoQSNBERERERkQKhBE1ERERERKRAKEETEREREREpEErQRERERERECoQSNBERERERkQKhBE1ERERERKRAKEETEREREREpEErQRERERERECoQSNBERERERkQJREncAMg+kUpS+/grUT6W0cgANG2wMxcVxRyUiIiIiIq0oQevlyh6upfq0v1E8YQIAA4DUYosx45yLaNh1SLzBiYiIiIjIL2iIYy9W9nAtNYceQFGUnGUUTZxIzaEHUPZwbUyRiYiIiIhIW5Sg9VapFNWn/Q3SaRKtNiXSaQCqTzsRUql5H5uIiIiIiLSpIIc4mlkCGA4cBaxMGJn3NfA8cKG7f9Fi33QnTrmUu3+dQxwnAhdETzdy99eyPUdcSl97ZfawxrYk0mmKJ3xL6Wuv0LjJZvMwMhERERERaU9BJmjAJcBxwETgQWAasCZwOLCPmW3s7v+N9j2znXOsAOwHfJhjcrZ6dO46oCrb4+NW9P13ed1PRERERES6X8ElaGa2CHAMMB5Y092ntth2LHAZIXk7BMDdR7Vzniujf96QQwylwC3Au8CnwP7ZniNuzQsvktf9RERERESk+xXiHLRlCHGNbZmcRR6OHhec2wnMrILQe9YA3JZDDKcCqxGSwB45Satxw41JLbYY6UTrGWhBOpEgtdjiNG648TyOTERERERE2lOICdqnhMRqEzOrabVt1+jxmQ7OsRswCKh19x+zubiZrUNI0M509w+zObagFBcz45yLAH6VpGUm7c0450KthyYiIiIiUkAKboiju082s5OAS4GPzWwMc+agbQ38A7iqg9McGj1en821zawcuJUwtPGibI6dm3Y6sbpd4+AhTL/xNqpO/dsvCoakq6qYcdW/aNx1yK8qPIpkZNptXO1Xeh61GcmW2oxkS21GslFo7aWzcSTS6c4UQZz3zGwvQoLVv8XLLwMnufvYuRy3LPA5oerjsu7enMU1LyTMf1s3U4TEzG4GDiK3Ko41QOthmvNeKgUvvQSPPQYXXQQLLQQTJqj3TERERERk3htA6IBqU8H1oAGY2UjgNGAkcDswBVgLuBx43sx2d/f2Vlk+BEgAN2WZnG0EHA+MalEhMi8mT55O3HlwYo11mX/jjWm+9lqKfviBqQ8/ofL6MleJBMw/f/+CaL/SM6jNSLbUZiRbajOSjUJrL5l4OlJwCZqZbUsob3+5u1/QYtPLZjYY+IIw/PFXCZqZFQEjgGbgxiyuWUKo2vg+c9Y9y5t0moJoFJSV0bDzYCruvI2yBx+gYWMlaNKxgmm/0mOozUi21GYkW2ozko2e1l4KsUjITtHjc603uPt3wMfACmZW3caxOwJLAE+5+1dZXLMaWJHQS9dgZunMF2F4I8Cr0WvDsjhvwUkOHQ5A+cO10NQUczQiIiIiItJSwfWgAWXRY3ul9Bck9JA1trEtp+IgQJL210vbnJC81QI/AuOyPHdBadxsC5oHDaJo0o+UvjqWxs22iDskERERERGJFGKCNhb4M3Ccmd3faqHqIwk9ZGPdPdnyIDNbEBhMSKLam5+WWYR6eaDR3T8HcPeZwGHt7H8zIUE7P4ciIYWntJTkLkPod/stlI8ZrQRNRERERKSAFOIQx3uBFwkLRX9iZteZ2cVm9gxwDTATOK6N4w4ESoHb3L1hLudfHPiIjtdS67WSg4cBUP7IGA1zFBEREREpIAWXoLl7CtgeOBn4FtiXUPreCBUd13X3N9o4NNfhjX1O46ab0zzffBRNnkzpKy/HHY6IiIiIiEQKcYgj0fDFC8iioqK7r9rJ/cZB59dndvcRhMqQvUdmmONtN4dhjptvGXdEIiIiIiJCAfagybyRHBJVc9QwRxERERGRgqEErY9q3GQzmuefn6KffqL05RfjDkdERERERFCC1neVlJDcZSgA5bWjYw5GRERERERACVqfNnvR6kdqobGtZeVERERERGReUoLWhzVutAnNCyxI0c8/U/rSC3GHIyIiIiLS5ylB68tKSkjuOgTQMEcRERERkUKgBK2Pm13N8dGHNMxRRERERCRmStD6uNnDHKdMofSl5+MOR0RERESkT1OC1tcVF5McHFVzHKNhjiIiIiIicVKCJiSH7gZA+aMPQ0NDzNGIiIiIiPRdStCExg02IrXQwhRNnULZi8/FHY6IiIiISJ+lBE2guJgGDXMUEREREYmdEjQB5gxzLHvsEUgmY45GRERERKRvUoImADSuvyGphRehaNpUyl54Nu5wRERERET6JCVoEhQVkRwyDNAwRxERERGRuChBk9mSg8Oi1WWPP6phjiIiIiIiMVCCJrM1rb8BqUUWpWj6NMqe1zBHEREREZF5TQmazPGLYY4PxBuLiIiIiEgfpARNfiE5JKrm+PijMGtWzNGIiIiIiPQtStDkF5p+ux6pxRanaMZ0yp57Ju5wRERERET6FCVo8ktFRSQHDwM0zFFEREREZF5Tgia/khwaVXN84jGYOTPmaERERERE+g4laPIrTeuuR2qJJSmqm0HZs0/HHY6IiIiISJ+hBE1+LZGYM8yxVsMcRURERETmFSVo0qbZ5fafeFzDHEVERERE5hElaNKmpnV+S2qJJUnU11H2zFNxhyMiIiIi0icoQZO2JRIkh4RiIRrmKCIiIiIybyhBk3ZlqjmWP/k41NfHHI2IiIiISO+nBE3a1bTWOqSWWppEfT1lzzwZdzgiIiIiIr2eEjRpX8thjmNGxxyMiIiIiEjvpwRN5mr2MMenHoe6upijERERERHp3ZSgyVw1/WYtUksvQ2LmTMqffiLucEREREREejUlaDJ3iQTJobsBGuYoIiIiItLdlKBJhzKLVpc98yTMmBFvMCIiIiIivZgSNOlQ0xprklpmWQ1zFBERERHpZkrQpGMa5igiIiIiMk8oQZNOmRWV2y975kkSM6bHHI2IiIiISO+kBE06JbX6GjQttzyJWbMoe/LxuMMREREREemVlKBJ5yQSc9ZE0zBHEREREZFuoQRNOi05JMxDK3v2KRLTp8UcjYiIiIhI76METTottepqNK2wIolkkrInHos7HBERERGRXkcJmnReIkEyKhZSXqthjiIiIiIi+aYETbKSSdDKnn1awxxFRERERPJMCZpkJbXKqjStuBKJhgbKHn807nBERERERHoVJWiSHQ1zFBERERHpNkrQJGvJoVE1x+eeITF1SrzBiIiIiIj0IkrQJGuplVehyVbWMEcRERERkTxTgiY50TBHEREREZH8U4ImOZldzfH5Z0lM+TnmaEREREREegclaJKTlK1M0yqrkmhs1DBHEREREZE8UYImOZs9zHHMAzFHIiIiIiLSOyhBk5zNHub4wnMa5igiIiIikgdK0CRnqRVXommV1Ug0NVH22CNxhyMiIiIi0uOV5HKQmT0GXAvUunsqvyGBmSWA4cBRwMrAAOBr4HngQnf/osW+6U6ccil3/7qDay4O7AnsHF1zEeAnYCxwkbu/nv076f2SQ4dT8tH/qBjzAMl99o87HBERERGRHi3XHrQdgPuAb8zsfDNbIY8xAVwC3A8Y8CBwJfAlcDjwrpmt3mLfM9v5uiPa/mFHyVnkKOByYDngSeBS4GVgKPCKme3dtbfUO2WGOZa++DyJnybHHI2IiIiISM+WUw8asAIhWToQOBH4m5k9D1wHPODuDbkGZGaLAMcA44E13X1qi23HApcBxwGHALj7qHbOc2X0zxs6eek3gC3d/YVW59kMeAa4xswedPdkp99MH5BaYUWaVluDkv99QPljjzBrvwPjDklEREREpMfKqQfN3b9w95OBpQhDER8FNif0Wk0ws8vMbNUcY1omimtsy+Qs8nD0uODcTmBmFcB+QANwW2cu6u4PtE7OotdfAp4DBgFrdOZcfU1yqKo5ioiIiIjkQ5eKhLh7yt3HuPtgQrI2EpgC/AX4wMxeNrODooSpsz4lJFabmFlNq227Ro/PdHCO3QgJVa27/5jFtdvTGD025eFcvU5yyDAASl96gcRkDXMUEREREclVrkMcf8XdJ5rZhcAnhLlciwEbAxsBl0bbLnX35g7OM9nMTiLMAfvYzMYA04A1ga2BfwBXdRDOodHj9bm+nwwzWwrYFpgIfJDLORKJrkbRdZkYuiOW5uVXoGmNNSn54D3KH3uI5AEj8n8Rmee6s81I76Q2I9lSm5Fsqc1INgqtvXQ2jkQ63ZkiiHNnZisBhxHmpC0IJIF7gVuAdYA/A0sCl7n7CZ08516EBKt/i5dfBk5y97FzOW5Z4HNC1cdlO0oIO4ihFHiaMHzzQHfv1HDJFmqA1sM0e6cLLoCTT4Ztt4Wnnoo7GhERERGRQjWA0AHVppwTtGjY4p6ExGxTIAF8TCi/f4u7/9xi33LgKcDcfeFOnHskcBphyOTthGGTaxF65tYCdnf32naOPTs69sz2Coh0hpkVEeav7Qtc5+5H5HCaGmDq5MnTyUMe3CWJBMw/f3+6K5aiL79gvvXXIl1czE///ZT0Agvk/yIyT3V3m5HeR21GsqU2I9lSm5FsFFp7ycRDBwlaruugXUVIXAYQ5mfdDfyrrSIbAO6eNLMngE06ce5tCWXyL3f3C1psetnMBgNfEIY//ipBi5KqEUAzcGM276mN89xIeI+3A0fmei6AdJqCaBTQfbGkllmOxt+sRen771L2yEPMOvDg/F9EYlFI7Vd6BrUZyZbajGRLbUay0dPaS65FQv4ITAZOApZw933bS85aeB44qxPn3il6fK71Bnf/jtBLt4KZVbdx7I7AEsBT7v5VJ671K1FydhNwEPBvYERXhkn2JZk10crHjI45EhERERGRninXIiHbuXtHlRR/IZo31u7csRbKosf2SukvSOgha2xjW5eKg7RIzg4k9Aoe4O6pXM7VFyWHDKP6nDMoHfsiiR9/JL3gXFdDEBERERGRVnJdBy2r5CxLmSTuODMb0HKDmR1J6CF7tfWC0Wa2IDAY+JE2hj+22K/UzFY2s+VbvZ4Z1nggocDJ/krOstO8zLI0rrU2ieZmyh9p90cgIiIiIiLtyHUO2kHA0cBgd5/QxvbFgIcIZfXvzPL09wJ/IFRO/MTMaglFQtYhlNmfCRzXxnEHAqXAbe7eMJfzLw58BIwnLIqdMZIwrHEGYamA08ys9bEPuvu7Wb2bPiY5ZDdK332H8trRzBpxaMcHiIiIiIjIbLnOQRsBNLSVnAFEr89kzpDDTot6rbYHTga+JRTqOAYwQsGOdd39jTYO7eraZ8tEj9XAqcAZbXytleO5+4zZi1a/8jKJ77+PNxgRERERkR4m1zloqwL3d7DPu8DuuZw8Gr54QfTV2WNW7eR+4whLArR+fQQh8ZQuaF5qaRrXWZfSt9+i/JFaZh1yeNwhiYiIiIj0GLn2oA0Afu5gn2nAoBzPLz1YcshuAJTXqpqjiIiIiEg2ck3QJtDxcL81AY1x64OSg4cCUPrqWA1zFBERERHJQq4J2tPADma2XVsbzWx7wppkT+QamPRczUsuReO6vyWRTlP+8Ji4wxERERER6TFynYN2PrA38KiZ3QY8RSjosTihwMf+hCGO5+cjSOl5kkN2o/StN0M1x0OPiDscEREREZEeIdd10L4EdiEMYRxBqK74XPR4EDAR2CnaT/qg2cMcX3uFou8mxhyNiIiIiEjPkOsQR9z9ZWA5Qk/aZcANwKXAXsAK7v5aXiKUHql5iSVp/O36JNJpyjTMUURERESkU3Id4ghAtCD0vdGXyC8khw6n9M03qBgzmlmHHRl3OCIiIiIiBS/nHjSRjiQHDwOg9PVXKZrY5prmIiIiIiLSQpd60MxsCWArYDGgvI1d0u5+dleuIT1X82KL07j+hpS+8RrlDz3IzCP+GHdIIiIiIiIFLecEzcwuBv4CFLd4OQGkW/1bCVoflhw6PCRotUrQREREREQ6ktMQRzM7HPgroXLjHoRk7BZgH+CfQBNhXtrW+QlTeqrkrlE1xzdeo2jCtzFHIyIiIiJS2HKdg3YEMI5QSn909No4d7/b3f9EWAttOLBg10OUnqx50cVo3GAjAMofejDeYEREREREClyuCdrKwOPu3tzitdnDJd39BeAR4PguxCa9xKyhwwEoHzO6gz1FRERERPq2rlRxnNLi33XA/K22O7BaF84vvUTDrkNJJxKUvvkGRd98HXc4IiIiIiIFK9cE7VtgiRbPPwc2aLXP6oTETfq45kUWpXHDjQEof0iLVouIiIiItCfXBG0ssGGL52OAtc3sX2a2i5mdD+wEvNjVAKV3SA6JhjnWPhBzJCIiIiIihSvXBO024HMzWzp6fjHwLnA4UAucCIwHTuhqgNI7JDPDHN96k6KvxscdjoiIiIhIQcopQXP35919J3cfHz2fQehR2ws4BdgXWCOzXSS98MI0brwpoGGOIiIiIiLtyWmhajPbHJjm7u9mXnP3RuC+PMUlvVByyHDKxr5E+UOjmfmno+MOR0RERESk4OQ6xPE5wlpoIp2W3GUI6aIiSt9+S8McRURERETakGuC9gMwK5+BSO+XXmihOcMcax+MNxgRERERkQKUa4L2FLClmSXyGYz0fqrmKCIiIiLSvlwTtJMIC1Nfa2bz5TEe6eVmD3N89x2Kxn0ZdzgiIiIiIgUlpyIhwO3AFOAQYH8z+xL4Hki32i/t7tvkHp70NukFF6Rxk80pe+l5ymsfZObRx8YdkoiIiIhIwcg1Qduyxb/LgZWjr9ZaJ2wiJIcOjxK00UrQRERERERayClBc/dch0aKkNx5MNUnHkfp++9S9MXnNC+3fNwhiYiIiIgUBCVaMs+lF1iAxk03B6D8oQfjDUZEREREpIAoQZNYzKnm+GC8gYiIiIiIFJCchjia2chO7pp297NzuYb0bsmdB1P9t2Mp/eA9ir/4jNRyK8QdkoiIiIhI7HItEjKqg+1pIBE9KkGTX0nPPz+Nm21B2fPPUl77IPXHHB93SCIiIiIisct1iONW7XwNA84CpgL3AVt3PUTprZJDdwOgfMzomCMRERERESkMuVZxfGEum2vN7A7gbeD+nKKSPiG50y5Un3AMJf/7gOLPPiW1wopxhyQiIiIiEqtuKRLi7p8Co4GTuuP80juk55ufxs23BKC8Vr1oIiIiIiLdWcXxB8C68fzSC8zSMEcRERERkdm6JUEzs3JgR2BKd5xfeo+GnXYhXVpKyUf/o/gTjzscEREREZFY5Vpm/8C5nG9x4HfAysDfc4xL+oj0wEE0bLEV5U8/SXntaOqP16hYEREREem7ci2zfzOhhH5riegxDfwbzUGTTkgOGR4StIceVIImIiIiIn1argnawe283gz8DLzl7hNzPLf0MQ077hwNc/yQYv+YlK0cd0giIiIiIrHItcz+LfkORPqu9MBBNGy5NeVPPRGGOZ5wctwhiYiIiIjEojurOIp0WnLIcEDl9kVERESkb8spQTOzg8zsLTNbrJ3ti0Xb9+1aeNJXNOy4M+myMkr8Y4o//ijucEREREREYpFrD9oIoMHdJ7S1MXp9JnBojueXPiY9YCANW20DQPmYB2KORkREREQkHrkmaKsC73Swz7vRfiKd8othjum2ioSKiIiIiPRuuSZoAwjVGudmGjAox/NLH9Sw486ky8sp+fQTij/6MO5wRERERETmuVwTtAnAWh3ssybwfY7nlz4o3b+Ghq22BaC8VsMcRURERKTvyTVBexrYwcy2a2ujmW0P7Ag8kWtg0jclhwwDoLz2QQ1zFBEREZE+J9eFqs8H9gYeNbPbgKeAb4HFge2B/QlDHM/PR5DSdzTssFMY5vjZpxR/+D9Sq60ed0giIiIiIvNMTj1o7v4lsAthCOMI4HbguejxIGAisFO0n0inpfvX0LB16JjVMEcRERER6WtyXqja3V8GliP0pF0G3ABcCuwFrODur+UlQulzkkOjao5jVM1RRERERPqWXIc4AuDuDcC90ZdIXjRsvyPpigpKvvic4v9+QGqN38QdkoiIiIjIPJFTD5qZFZtZjZm1eXyL7cVdC0/6onR1fxq22R6AitrRMUcjIiIiIjLv5DrE8QzgB2D+drbPR5ifdmqO55c+bs4wxwc0zFFERERE+oxcE7RdgWfc/ce2NkavPw0MzTUw6duS2+5Aul8/isd9SckH78UdjoiIiIjIPJFrgrYc8HEH+ziwbI7nl76uupqGbXcAojXRRERERET6gFyLhJQCzR3skwYqcjm5mSWA4cBRwMrAAOBr4HngQnf/osW+nRn/tpS7f93Ja68HnAlsTHifHwCXufs92bwH6brkkGGUP/Qg5WMeoO7UMyCRiDskEREREZFulWuC9hmwdQf7bA3kug7aJcBxhPXUHiQser0mcDiwj5lt7O7/jfY9s51zrADsB3yYRXK2FfAEMAu4C5gO7A7cbWZLuvulub0dycXsYY7jx1Hy/rs0rbl23CGJiIiIiHSrXBO0B4CRZnYWcKa7pzIbosqNo4C1gLOzPbGZLQIcA4wH1nT3qS22HUtYc+044BAAdx/VznmujP55QyevWwJcR+gZ3Nzd341ePwt4AzjPzO5z9/HZvifJUVUVye12pKJ2NOVjRitBExEREZFeL9c5aJcCnxCqNH5kZv8ys5Fm9i/gI+CU6PGSHM69TBTX2JbJWeTh6HHBuZ3AzCoIvWcNwG2dvO7WwPLAnZnkDCCK4TygDDiok+eSPJldzbFWi1aLiIiISO+XUw+au88ws82BawhzxVZosbkZuA/4o7vPyOH0nxISq03MrMbdp7XYtmv0+EwH59gNGATc116lyTZsGT0+2ca2J6LHLTp5rl8ohKlTmRgKIZZsNG67PenKSoq/Gk/pe2/TtPa6cYfUZ/TUNiPxUZuRbKnNSLbUZiQbhdZeOhtHrkMcM6X09zCzhYHfEgp5TAHedPcfunDeyWZ2EqGX7mMzG8OcOWhbA/8ArurgNIdGj9dncekVo8dP24jpOzOb0WKfrMw/f/9cDusWhRRL5/SHwYPh7rsZ+OQjsN2WcQfU5/S8NiNxU5uRbKnNSLbUZiQbPa295JygZbj798AjrV+P5pKNcPcLcjjn5Wb2LSHBOrLFppcJQxCb2jvWzJYFtgK+Ap7K4rIDosfWwyozprXYJyuTJ0+PfXReIhEaZyHEkq2yHQdTc/fdpO66m5//dnrhfAzSy/XkNiPxUJuRbKnNSLbUZiQbhdZeMvF0pMsJWktRgZBdCT1YOwLFQNYJmpmNBE4DRgK3E3rm1gIuB543s93dvbadww8BEsBN7t7RUgDzRDpdONOnCimWzkpuvR3pyiqKv/ma4rfepGnd9eIOqU/piW1G4qU2I9lSm5Fsqc1INnpae8lLgmZmRkiMDgQWIiRI3wC35HCubQml8y9v1fv2spkNBr4gDH/8VYJmZkXACMI8uBuzvHSm56y9XrIa4Ocszyn50K8fyR13ouKB+yivfVAJmoiIiIj0WrlWccTMKs3sYDN7GfgQOAFYGHgf2AFY2t1Pz+HUO0WPz7Xe4O7fAR8DK5hZdRvH7ggsATzl7l9led3M3LNfzTOLhmtW08b8NJk3koNVzVFEREREer+sEzQz29DMriMsIn09sDHwKnPmiv3H3Z9y91zvosuix/ZK6S9I6CFrbGNbLsVBMl6IHrdvY9sOrfaReaxh621prqqm+NtvKHnrP3GHIyIiIiLSLTqdoJnZX83sf8BYQiL0I2Eh6hXcfVN3vzZPMY2NHo8zs18MNzSzIwk9ZK+6e7LVtgWBwVFc7c1Pw8xKzWxlM1u+1aZnCMMn9zWztVrsP4CwrlsDcGtO70i6rl8/GnYInavlY0bHHIyIiIiISPfIZg7axYSeq5uBG9197Nx3z9m9wB+AzYFPzKyWUCRkHUKZ/ZnAcW0cdyBQCtzm7g1zOf/ihEW0xxMWxQbA3ZvM7DDCmmcvmtldwHRgd2Bp4Hh3H9eVNyZdkxy6GxUP3Ev5Qw9Sd+a5UJTzCF0RERERkYKUzR1uOtp/V2BPM1unOwJy9xRhmOHJwLfAvsAxgBEqOq7r7m+0cWhXhjdmrv0csCmhF29vQqL4PfA7d7801/NKfjRstQ3N1f0pnvAtJW9qmKOIiIiI9D7Z9KAtS0iCDgKOBo4ys48Jw/5ud/dv8xVUNHzxArIo0e/uq3Zyv3GEKpPtbX+DOYVKpJBUVNCw485U3Hc35bUP0LT+BnFHJCIiIiKSV53uQXP3r9z9DEKithNwP7A8cD4w3syeJvSyiXSb5NDdACivfRCaC2KZOxERERGRvMl6Eo+7p939CXffC1iMMB/sI8L8sASwj5ndaGab5zdUEWjYcmua+9dQ/N1ESt54Pe5wRERERETyqktVFtz9J3e/wt3XADYkzP9qJiwW/ZyZad0wya/ychp22iX88yFVcxQRERGR3iVvZfDc/Q13PwJYFDgEeIUwBFIkr5JDhgEa5igiIiIivU/e65S7e7273+zumwEr5/v8Ig1bbE1zzQCKv/+O0jdeizscEREREZG86daFpNz9k+48v/RRLYc5jnkg5mBERERERPJHK/1Kj5QcOhyAsofGQCoVczQiIiIiIvmhBE16pIbNt6J5wECKf/ie0tdfjTscEREREZG8UIImPVNZGcmddwU0zFFEREREeg8laNJjZYY5lmuYo4iIiIj0EkrQpMdq3GxLmgcOpGjSj5S+OjbucEREREREukwJmvRcpaUkdxkCQHmtFq0WERERkZ6vJNcDzawMGAasBwwEitvYLe3uh+Z6DZGOJAcPo98dt1L+cC0zzrsYSnJu0iIiIiIiscvpbtbMlgaeApYHEnPZNQ0oQZNu07jZFjQPGjR7mGPjZlvEHZKIiIiISM5yHeJ4ObACcDuwFbAisGwbX8vlIUaR9rUc5jhGwxxFYpNKUTr2Jfj3v8OjCveIiIjkJNfxYFsDz7j7QfkMRiQXySHD6Xf7LZQ/MoYZF1yiYY4i81jZw7VUn/Y3iidMAGAAkFpsMWaccxENuw6JNzgREZEeJtcetCLgnXwGIpKrxk03p3m++SiaPDl8ci8i80zZw7XUHHoARVFyllE0cSI1hx5A2cO1MUUmIiLSM+WaoL0OrJLPQERyVlJCcpehgKo5isxTqRTVp/0N0ulfTUZOpNMAVJ92ooY7ioiIZCHXBO0kYGsz2yOfwYjkavai1Y/UQmNjzNGI9A2lr71C8YQJ7VaKSqTTFE/4ltLXXpmncYmIiPRkuU7W2QV4DrjbzF4A3gamtbFf2t3PzjU4kc5q3HhTmhdYgKJJkygd+xKNW24dd0givV7R99/ldT8RERHJPUEb1eLfW0ZfbUkDStCk+0XDHPvdcgPltaOVoInMA80LL5LX/URERCT3BG2rvEYhkgfJIcNCgvZILTMuvAxKS+MOSaRXa9xwY1KLLUZRO8Mc04kEzYsuRuOGG8/z2ERERHqqnBI0d38h34GIdFXjRpvQvMCCYdHql16gcett4w5JpHcrLmbmiMOoPu8s0vDrJC2dZsZZ50NxcQzBiYiI9Ey5FgkRKTwlJSSjNZdUzVFkHmhupvzxRwBIV1b+YlMmYSt98415H5eIiEgP1qUVfc1sE2AEsBZQQygU8g5wq7u/3NXgRLKVHLob/W6+gfJHH2LGRZdDWVncIYn0WuWj76P07bdorqrmp7FvUjrucwbUT2Vq5QASEyZQ88fDqfznVaSWWZZZhxwed7giIiI9Qs4JmpldDhzNnFEtmQ9M1wUONbP/c/fjuh6iSOc1brgxzQsuRNGPP1D20vM0bLN93CGJ9E719VSdMwqAmX85jvRii9G4+GKwQH8aJ00nnYa6r7+i6vyzqT7lBJqXWoqGbXeIN2YREZEeIKchjmZ2EPAX4FNgP2AxQrK3KLAv8AnwFzM7ME9xinROcTHJwdGi1WM0zFGku1T+8yqKv/2G1BJLUv/7P7W5T/0xxzPrd/uRaG6m/+EHU/zfD+ZxlCIiIj1PrnPQ/gB8A2zg7v929+/cPe3u37v7XcCGwLfAH/MVqEhnJYfuBkDZY49AQ0PM0Yj0PkXff0fl3y8HoO60UdCvX9s7JhJMv+T/aNh0c4rqZjBgvz0pmjhh3gUqIiLSA+WaoK0G3O/uU9vaGL1+f7SfyDzVuP6GpBZehKKpUyh78bm4wxHpdSrPP5tEfR2N665Hcvgec9+5rIxpN95G04orUTxxAjX77w0zZsybQEVERHqg7qzimO7Gc4u0T8McRbpNyQfvUfHv2wGYcfb5kGhrBbRfSg8cxNQ77qV5gQUo/eA9ao48BFKp7g5VRESkR8o1QfsfsLuZVbe10cz6A7tH+4nMcw1DhgPRMMdkMuZoRHqJdJqqkaeQSKeZtdseNP12/U4f2rzMsky99S7SFRWUP/k4VSNP7sZARUREeq5cE7R/AUsAr5rZ7ma2AICZLWBmewCvRNuvyU+YItmZPcxx2lTKXng27nBEeoWyxx+lbOxLpCsqqDt1VNbHN/12faZdfS0Aldf9k37X6b8IERGR1nJK0Nz9JuAqwhyze4DvzawR+B64O3r9Kne/JV+BimSlqIjkkGGAhjmK5EVDA1WjTgWg/sg/07zkUrmdZvAwZpx+FgBVp59M2ROP5S1EERGR3iDnOWjufjSwBXAz8C4wLnq8CdjC3f/S5ehEuiA5JKrm+PijMGtWzNGI9Gz9bryWki+/oHnBhZh59LFdOtfMP/+FmQeMINHcTM3vD6bkvXfyFKWIiEjPl/NC1QDu/hLwUp5iEcmrpvXWJ7XoYhRPnEDZ88/SsOPOcYck0iMlfppM5aUXAVB38umkq/t38YQJZlxwKcVfjafsheeo2X9vpjz+LM2LL5GHaEVERHq27qziKBKvXwxzfCDeWER6sMpLLqBo6hSaVluDWfvsn5+TlpYy7YZbaVp5FYq//44B++5JYvq0/JxbRESkB+tUD5qZZSYbfOvuqRbPO+TuX+UUmUgeJIcMp/Jf/wjzXGbNgoqKuEMS6VGKP/2EfjddD8CMs86D4uK8nTtdM4Cpd9zLoB23puSj/1Fz+Aim3n4PlHRpcIeIiEiP1tketHHAl8DyrZ539PVF/kIVyV7TuuuRWnwJimZMp+y5Z+IOR6THqRp1KolUiuSOO9O42RZ5P3/zkksx9fa7SffrR9mzT1N98gmQ1jKaIiLSd3X2Y8pbCQtPT231XKSwFRWRHDyMyn9eRfmYB2jYaZe4IxLpMUqff5byp54gXVJC3Rlnd9t1mtZel2n/uJ6aQ/an3y03kFpueWb+4c/ddj0REZFC1qkEzd1HzO25SCFLDgkJWtkTj8HMmdCvX9whiRS+VIrqM0JZ/ZmHHE5q+RW79XINuwymbtS5VJ9xClWjTiW11NI07DK4W68pIiJSiFQkRHq9pnXXI7XEkhTVzaDs2afjDkekR6i441ZKPvofzQMHUv/XE+fJNWce+SdmjjiURDpNzR8Po+Sdt+bJdUVERApJTgmamaXM7PQO9jnVzJpyC0skjxIJkoOHAVBeq2qOIh1JTJ9G1QXnAFB//EmkB803jy6cYMZ5F5PcZjsSM2cyYP+9KfpadaZERKRvybUHLRF9dWY/kdglhw4HoPyJx6G+PuZoRApb5f9dRtGkH2lafgVmHnz4vL14SQnTr7uZplVXp+jHHxiw354kpk3t+DgREZFeojuHOC4IzOzG84t0WtPa65JacikS9XWUPfNU3OGIFKyir8bT719XA1A36lwoLZ3nMaSr+zP1zntJLbIoJR9/RM2hB0Jj4zyPQ0REJA6dXmzGzA5s9dJabbwGUAwsCRwI/LcLsYnkTyIR1kS7+v8orx1Nw+ChcUckUpCqzj6DRDJJw2Zb0rD9jrHF0bzY4ky7/W4GDtmJsheeo/rE45hx6d8hoYEZIiLSu2WzGujNzCmtnwaGRl+tZf73nAmMyjUwkXxLDo0StKceZ3p9PVRWxh2SSEEpeeN1KsY8QDqRYMaZ58aeDDX9Zi2mXXsjNQfuQ7/bbyG1zHLMPPrYWGMSERHpbtkkaAdHjwngRuBBYEwb+6WAn4BX3f3nLkUnkkdNa65NaqllKP5qHGXPPElDVDhERIDmZqpHngTArP0OJLX6GjEHFDRsvxMzzrmA/qf8jepzziC1zDI0DBked1giIiLdptMJmrvfkvm3mW0BjHb32m6JSqQ7JBKhF+3KyykfM1oJmkgL5Q/cS+nbb9FcVU3diafFHc4vzDrsSIq//ILK6/5JzZ+OYMqii9G03gZxhyUiItItcioS4u4HKzmTnig5ZBgA5U89DnV18QYjUijq66k6Z1T45zF/Jb3wwvHG04a6s84nucNOJJJJBhy0D0Xjvow7JBERkW6RzRDHNplZMbAAUN7WdnfXIjZSMJp+sxappZehePw4yp9+guTQ3eIOSSR2lf+8iuIJ35JaYklmHvHHuMNpW3Ex0665gYFDd6L0g/cYsN+eTHnkKdIDB8UdmYiISF7lXGbfzNY1syeAGcAE4Ms2vr7IR5AieZNIzE7KyseMjjkYkfgVfTeRyr9fDkDd6WdCv34xRzQX1dVMu+MeUostTsmnn1BzyAHQ0BB3VCIiInmVU4JmZmsBLwEbAU8SCoe8H/17UvT8BeC2vEQpkkeZRavLnn4CZsyIORqReFWefzaJ+joa112P5LDd4w6nQ82LLMrU2++huaqaspdfpP/xf4F0uuMDRUREeohce9BOjx43cPdMqf3R7r4TsAzwT2B14MyuhSeSf02r/4amZZcjMWtWmIsm0keVfPAeFXfdAcCMs8+Pvax+Z6VWX4Pp199MuriYirvuoPKKS+IOSUREJG9ynYO2KVDr7h+1eC0B4O4zzezPwMbAecC+2Z7czBLAcOAoYGVgAPA18Dxwobv/auikmS0LnAJsDywCTAE+BP7h7vd213WlB4qGOZZccQnlY0aTHL5H3BGJzHvpNFUjTyGRTjNrtz1o+u36cUeUlYZttmfGeRfT/8TjqDr/bFJLL0Nytz3jDktERKTLcu1BG8Av55c1AtWZJ+7eTEhqtsnx/JcA9wNGWG/tSsKctsOBd81s9ZY7m9l2wH8JyeCrwKXAA0AZsG13XVd6rmS0jlLZM0+SmDE95mhE5r2yxx6hbOxLpCsqqDutZw52mHXwYdT/4SgA+h/9B0peezXmiERERLou1x60H4CWpbO+A1ZstU8FUJntic1sEeAYYDywprtPbbHtWOAy4DjgkOi1pYD7gG+BbVtXjTSzTr3HbK8rPVtqtdVpWn4FSj7/jLInH9cn79K3NDRQdWZY66z+D3+meYklYw4od3VnnB2qsj76EANG7MPPjz5D83LLxx2WiIhIznLtQfuQ0MuUMRbY3sw2AjCzVYC9gI9zOPcyUVxjWyZJkYejxwVbvHYKUAMc2VZJf3dv6qbrSk8WLVoNquYofU+/G6+l5MsvSC20MDOPOjbucLqmqIhp/7iOxrXXoeinnxiw7x4kfpocd1QiIiI5yzVBewTY3MwWjZ5fSJiD9rKZ/Qh8AAwkzEHL1qdAA7CJmdW02rZr9PgMzJ4zticw2d2fjUr/H2dmx5vZtmaWzfvr9HWld0gOjoY5PvsUienTYo5GZN5I/DSZyksvAqD+5NNJV/ePOaI8qKxk6q13k1piSUq++JyaEftBMhl3VCIiIjnJdYjjP4F7gJ8B3P09M9sGOBVYDngLuNLdH8n2xO4+2cxOIswj+9jMxgDTgDWBrYF/AFdFuy8LzAe8aWb/Ao5odbp3zGyIu3+T5+tmpRAKo2ViKIRYCkXzaqvRtMKKlHz2KeVPPkZyj73jDqmgqM30TlUXn0/R1Ck0rbYGyX32y+vPN9Y2s8jCTLvzXgbssj1lr71C/2P/xIx/XKcGXOD0d0aypTYj2Si09tLZOBLpAl0/xsz2Aq4HWn68+zJwkruPjfbZkFAUJAXMJFRfHEMoYnIKobjH6+6+YT6vm4UaoPVwSSkkI0fC2WfDkCEwZkzc0Yh0r48/htVXh1QKnnkGtt467ojy76mnYKedwns84wwYNSruiERERFobQOgIalNBJmhmNhI4DRgJ3E4omb8WcHn0uLu715rZxoT5bwDHuvsVrc7zGrABsJm7v5yv62bxVmqAqZMnT499HdVEAuafvz+FEEshKf7oQwZtviHpsjJ++uhz0jUD4g6pYKjN9D41++5J2VNPkNxxZ6bfdlfez18obab8tpvpf9zRAEy/6p8k9856tReZRwqlzUjPoTYj2Si09pKJhw4StM5WONw810Dc/cVs9jezbQkLXF/u7he02PSymQ0mlPe/FKjll71TbSVODxEStN8SesHydd2spNMURKOAwoqlEDTZKjStZJR84pQ+9ijJvfaJO6SCozbTO5Q+/yxlTz1BuqSEujPO7tafadxtZtb+Iyj+8ksqr7yc6mOPIrXEUjRuvGl8AUmH4m4z0vOozUg2elp76WwRjeeB53L8ytZO0eOvjnX37wiVIVcws2rgc8LwRgi9Xa1lXuuX5+tKb5FIzF4TrbxW1Ryll2pqovqMUwCYeegRpJZvvSpK71N36hkkBw8j0dhIzYh9Kf7s07hDEhER6ZTOJmhntfH1JKFy42fALcBF0eOn0etPRvtlqyx6bK+k/YJAM9Do7rOAV6LXV21j38xr4/J53U6cS3qQ2YtWP/8sialT4g1GpBtU3HkbJR99SPPAgdQf97e4w5k3ioqYdtW/aFz3txRNmcKAfXYnMWlS3FGJiIh0qFMJmruPcvczM1/As8BWhKqJK7v7Ie5+srsfAqwCHAlsSW5l6TNzyo4zs19MCDKzI4ElgFfdPVND+ZrocZSZlbfYd2VgBDAdeLzF66VmtrKZtV7JNNvrSi+RWnkVmlZehURDA2WPPxp3OCJ5lZg+jaoLzgGg/oSTSQ+aL+aI5qF+/UL5/aWWoXj8OAYctA/MmhV3VCIiInOVa5n9s4FH3P361hvcPQ1ca2Y7RvttleW57wX+AGwOfGJmtYShiusQyt3PBI5rsf9dwG7AHsB7ZvYEYeLd7kAFcKC7/9xi/8WBj4DxhMWpc72u9CLJIcMp+fgjymtHq6CA9CqVV1xK0aQfaVp+BWaOOCzucOa59IILMvXOexm4y3aU/ud1+h99JNP/eSMU5boMqIiISPfK9X+odQlJztx8RCjOkRV3TwHbAycD3wL7AscARqisuK67v9Fi/zSwDyF5agJ+DwwnDH3c2t3v6I7rSu/yi2GOU37uYG+RnqFo/Dj6/etqAOpGnQulpTFHFI/USsa0m24nXVJCxYMPUHnhOXGHJCIi0q5ce9AagLU72GftaL+sRcMIL4i+OrN/E6EU/uWd2HccYY5cl68rvUdqJaNplVUp+ehDyh5/lOTv9os7JJEuqzpnFImGBho225KG7XeMO5xYNW66OdMvu5Kao/9A1eWXkFpmOZL77B93WCIiIr+Saw/ak8COZnaSmZW13GBmZWZ2MrAD8ERXAxSZV2ZXcxzzQMyRiHRdyeuvUTHmAdJFRcw467yw+Eofl/zdftQddwIA/f96NKUvPh9vQCIiIm3INUE7AZgInAuMN7OHzOwGM3uIMLfrHGAC0EfKhUlvMHuY4wvPkfj5p5ijEemC5maqR54EwKz9DiS12uoxB1Q46k88jVm77UGiqYmaQw6g2D+OOyQREZFfyClBc/dvCPPLbiMU5NgFODh6HBC9vl60n0iPkFpxJZpWXZ1EUxPljz0SdzgiOSt/4F5K33mb5qpq6v52atzhFJZEgulX/IPG9TekaNpUBuy3J4kffog7KhERkdlyLmPl7t+5+whCQvYbYLPocaC7j4gWdxbpUZJDNcxRerj6eqrOGRX+ecxfSS+8cLzxFKKKCqbe8m9SyyxL8VfjGXDg3lBfH3dUIiIiQBcStAx3b3T3/7r72Ogxp8IgIoUgOWQYAKUvvUDip8nxBiOSg8prrqR4wrekllyKmb//U9zhFKz0/PMz9d/30TxwIKVvv0XNn38Pzc1xhyUiItL1BE2kN0ktvyKNq/9GwxylRyr6biKVV4ZitnWnjYKKingDKnCp5Vdk2i3/Jl1aSvnDY2b3PIqIiMSpU2X2zexZIA0c5O7fRM87I+3u2+QcnUgMkkOHU/rf9ykf8wCz9jsw7nBEOq3y/LNJ1NfT+Nv1SQ7bPe5weoTGjTZh+hVXU/OnI6i86gpSyyzLrAMPjjssERHpwzq7DtqWhAStssXzzkhnGY9I7JKDh1F97plhmOPkyaTnnz/ukEQ6VPL+u1TcdQcAM84+X2X1s5Dc83fUjfuSqovPp/rE40gtuRSNW+mzRRERiUenEjR3L5rbc5HepHm55WlcY01KP3iP8kcfYtYBI+IOSWTu0mmqRp5CIp1m1m570rTuenFH1OPUH38SxeO+pOLeu6g59ECmPPIUqVVWjTssERHpg5RoibRhTjXH0TFHItKxssceoeyVl0lXVIS5Z5K9RILpl11Jw8abUjRjOgP225Oi71WMWERE5j0laCJtSA4eBkDpyy+QmDQp3mBE5qahgaozTwOg/g9/pnmJJWMOqAcrL2faTbfTtPwKFH/zNTUH7A11dXFHJSIifUxni4RsnusF3P3FXI8ViUvzssvRuObalL73DuWP1DLroEPiDkmkTf1uuJaSL78gtdDC1B91XNzh9HjpQfMx9c77GLTzNpS++w41fziMaTfdDsXFcYcmIiJ9RGeLhDxP7gU/9L+a9EjJIcNDglY7WgmaFKTE5MlUXnohAPUnnw7V1TFH1Ds0L7scU2/+NwP3GEz5449Qdebp1J11XtxhiYhIH9HZBO0sVJFR+pjkkGFUnz2S0rEvkfjxR9ILLhh3SCK/UHXJ+RRNm0rj6r9h1u/2izucXqVpgw2Z/vdrqPn9IVT+86pQfv+Qw+MOS0RE+oDOVnEc1c1xiBSc5qWXoXHtdSh95+0wzHHEoXGHJDJb8SdOxc03AITeHQ3By7vk8D2oGz+OqvPOovqUE2heaikatt0h7rBERKSXU5EQkblIDtkNgPJaVXOUwlI16lQSqRTJHXehcdOcpwlLB+r/8ldm7rM/ieZm+h9+MMUfvB93SCIi0sspQROZi+TgoQCUvvIyie+/jzkakaD0uWcof/pJ0iUl1J1xVtzh9G6JBDMuvoKGzbagqG4GA/bfi6KJE+KOSkREerHOzkH7FTPrD/wZ2BZYDChvY7e0uy+f6zVE4ta81NI0rrMupW+/FYY5ag6KxK2piepRpwIw89AjSC2/YswB9QFlZUy78TYG7rIdJZ84NfvtxZTax1WURUREukVOPWhmtiDwNnAusC5gwCBgYWCZ6Kss1/OLFBINc5RCUnHHrZR89CHNgwZR/9cT4w6nz0gPGMjUO+6leYEFKP3v+9T8/mBIpeIOS0REeqFcE6hRwPLAgYTEDOByd68CNgDeAMYBq3UxPpHYzR7m+OpYir7/LuZopC9LTJ9G1YXnAFB//EmkBw7q4AjJp+all2HqbXeTrqig/KknqDr9pLhDEhGRXijXBG1n4Bl3v93df1F+393/A+xE6EU7o2vhicSvecmlaFx3PRLpNGUPj4k7HOnDKq+4lKJJk2haYUVmjjgs7nD6pKZ112Pa1dcBUHn9v+h33TUxRyQiIr1NrgnaosA7LZ6ngH6ZJ+7+M/AYsFfuoYkUjuTQ4QCUj9EwR4lH0fhx9PvX1QDUjToHSktjjqjvahg8lBkjzwag6rSTKHv80ZgjEhGR3iTXBG0q0PLu4GdgiVb7TCPMSRPp8ZKDhwFQ+vqrFH03Md5gpE+qOvsMEg0NNGy+FQ3b7Rh3OH3ezD8dzcwDDiaRTlNz5CGUvPdOxweJiIh0Qq4J2heEIYwZ7wDbmdn8AGbWDxgMfNWl6EQKRPPiS9C43gYa5iixKHn9NSpqR5MuKmLGmedCIhF3SJJIMOOCS2jYcmsS9fXU7L83Rd9+E3dUIiLSC+SaoD0JbGNmldHzfwELAe+Z2b3AfwlFRG7ucoQiBSI5ZBgAFRrmKPNSczPVI0Mxiln7HUhqtdVjDkhmKy1l2vW30LTKqhR//x0D9t2TxPRpcUclIiI9XK4J2j+Bw4FKAHd/ADgBqAJ2BxYBLgMuzkOMIgXhF8MctVCtzCPl999D6Ttv01xVTd2Jp8UdjrSSrhnA1DvuJbXQwpR89D9qDh8BTU1xhyUiIj1YpxM0M/vIzI43s4XcfaK73+3ukzLb3f1SYAFCAZFqdz/B3bVIjPQazYstTuP6GwJQ/tCD8QYjfUN9PVXnnhn+ecxfSS+0UMwBSVual1iSabffTbpfP8qefZrqk46HdLrjA0VERNqQTQ+aARcCX5vZA2a2i5n94nh3T7n7961L74v0FqrmKPNS5TVXUjzhW1JLLsXM3/8p7nBkLprWWodp19xAOpGg36030u+aq+IOSUREeqhsErSVgIuAH4FhQC0hWTvPzFbshthECk5y16GkEwlK//O6CgJItyr6biKVV14OQN3pZ0JFRcwRSUcadt6VujPPBaDqzNMoe7g25ohERKQn6nSC5u6fufvJwFKECo2jgfmBk4CPzex5MzsgquAo0is1L7oYjRtsBGiYo3SvqvPOIlFfT+Nv1yc5dLe4w5FOmvn7PzHz4MNC+f0/HU7J22/GHZKIiPQwWRcJcfdmd3/E3fcAFgeOI1Rt3JxQtXGimV1jZuvlNVKRAqFhjtLdSt5/l/K77wRgxtnnq6x+T5JIMOPci0husx2JmTMZsP/eFH01Pu6oRESkB8m1iiMA7j7Z3a9w9zWB9QjVHVPA74HXzOy9PMQoUlAaMsMc3/oPRd98HXc40tuk01SNPIVEOs2s3fakaV191tXjlJQw/bqbaVptDYom/ciA/fYkMXVK3FGJiEgP0aUErSV3f8vd/wT8BhgLJAAt2CO9TvPCi9C40SYAlD+kRaslv8oefZiyV14mXVFB3Wmj4g5HcpSu7s/UO+4htciilPjH1Bx6EDQ2xh2WiIj0AHlL0MxsBzO7B/gM2ARIA8/l6/wihSSzJlp57QPxBiK9S0MD1WeGtc7q//BnmpdYMuaApCuaF1ucaXfcQ7qyirIXn6P6b8eq/L6IiHSoSwmamS1rZmeb2XjgUWAPYBJwLrCCu2+bhxhFCs7sao5vvan5JZI3/W64luJxX5JaaGHqjzou7nAkD5rWWJNp195IuqiIfnfcSr8rr4g7JBERKXBZJ2hmVhFVa3wO+BQ4FViEUNVxF2Bpdz/d3b/Mb6gihSO98MI0brwpoGGOkh+JyZOpvPRCAOpPGQnV1TFHJPnSsP1OzDg3/GyrzzmDsloVGBIRkfaVdHZHM9sAOATYG+hPmGP2MXADcKu7/9gtEYoUqOSQ4ZSNfYny2geY+aej4w5Heriqi8+jaNpUGlf/DbP23jfucCTPZh36e4q//ILKa6+h5k9HMGXRxWhab4O4wxIRkQKUTQ/aq8Dh0TE3AZu4+6rufqmSM+mLkrsMIV1UROk7b1M0flzc4UgPVvyJU3HLjQDUnXUeFBfHHJF0h7ozzyO5w04kkkkGHLQPReM00ERERH4tmwTtNeAwYFF3P8zdX+2mmER6hPRCC9G4yWYAlNc+GG8w0qNVjTqVRCpFcsddaNx087jDke5SXMy0a26g8TdrUTRpEgP23YPElJ/jjkpERApMpxM0d9/Y3W9097ruDEikJ0kOiRatfkhzSiQ3pc89Q/nTT5IuKaFu1NlxhyPdrbqaabffTWqxxSn57FNqDt4fGhrijkpERApI3srsi/RFs4c5vvuOhitJ9pqaqD7jFABmHnoEqeVWiDkgmReaF1mUqXfcS3NVNWVjX6L/X49W+X0REZlNCZpIF6QXWIDGTbcANMxRsldxx62UfPwRzYMGUf/XE+MOR+ah1GqrM+2GW0gXF1Nx951UXn5x3CGJiEiBUIIm0kXJIcMAKFfpbMlCYtpUqi48B4C6E04mPXBQzBHJvNa49XbMOC8kZlUXnEP5/ffEHJGIiBQCJWgiXZTceTDp4mJK33+Xoi8+jzsc6SEqr7iUokmTaFphRWYddGjc4UhMZh18GPV/OAqA/n/5IyWvqf6WiEhfpwRNpIvCMMdQea/8oQfjDUZ6hKLx4+h37T8AqBt1DpSWxhyRxKnujLNJ7jKEREMDA0bsQ/EXn8UdkoiIxEgJmkgeJIfuBkD5GA1zlI5VnX0GiYYGGjbfiobtdow7HIlbURHTrr6WxrXXoeinn6jZd08SP02OOyoREYmJEjSRPEjuvGsY5vjf9yn+/NO4w5ECVvLaq1TUjiZdVMSMs86DRCLukKQQVFYy9da7SS25FCVffE7NiP0gmYw7KhERiUFJZ3Yysy9yPH/a3ZfP8ViRHiM93/w0br4lZc89Q3ntg9Qfe0LcIUkham6meuRJAMza70BSq64Wc0BSSNILL8zUO+5l4C7bUfbaK/Q/5k9M/8d1SuJFRPqYzvagFQGJVl/lwDLR1xJAv+gx81p5FucX6fFmD3NUuX1pR/n991D67js0V/en7sTT4g5HClBq5VWYduNtpEtKqLj/HiovPj/ukEREZB7rVALl7su4+7KZL2BtYCLwIrAZUOHuiwIVwObR6xOAtbolapEClNxpF9IlJZT87wOKP9MwR2mlvp6qc88M/zzmr6QXWijmgKRQNW6xFTMuuhyAqksuoPzuO2OOSERE5qVce7guJCRj27j7WHdvBnD3Znd/GdgWqIz2E+kT0oPmo2GLrQCtiSa/VvmPv1M84VtSSy7FzCP+GHc4UuBm7X8Q9UcfB0D/446idOxLMUckIiLzSq4J2lDgYXdPtbXR3ZuAh6P9RPqM5JDhgKo5yi8VfTeRyquuAKDu9DOhoiLegKRHqDtlJLOGDCfR2EjNiP0o/vSTuEMSEZF5INcErQYY0ME+Azqxj0iv0rDTLqRLSyn56H8Uf+JxhyMFouq8s0jU19O43gaz5yqKdKioiOlX/pPGddejaOoUBuy7B4lJk+KOSkREulmnqji24X/A78zsEnf/vPVGM1sR+B3w31xObmYJYDhwFLAyIdH7GngeuNDdf1VV0syWBU4BtgcWAaYAHwL/cPd7s7z+cOCPwDpAFWG+3WvA39z961zek/QN6YGDaNhiK8qffpLy2tHUH39S3CFJzEref3f2HCKV1Zes9evH1FvvYtBO21A8fhwDDvwdUx54WL2wIiK9WK49aOcQkqZ3zOxyM9vdzDaLHq8A3gL6R/vl4hLgfsCAB4ErgS+Bw4F3zWz1ljub2XaEZHBf4FXgUuABoIwwH65TzCxhZv+Kjl0WuAu4AngJ2BhYOsf3I33I7GGOmocm6TRVp59MIp1m1m570rTuenFHJD1QesEFmXrnvTQPGEjpm2/Q/+gjobk57rBERKSb5NSD5u5jzGwEIXH6C3B0i80JYBpwsLvXZntuM1sEOAYYD6zp7lNbbDsWuAw4Djgkem0p4D7gW2Bbd/+q1fmyeY9HA0cA/wCObj3HLstzSR81e5jjxx9R/PFHpFZeJe6QJCZljz5M2atjSVdUUHfaqLjDkR4stZIx7eY7GLDXMCoefIDUMstRf8rIuMMSEZFukPM6Ze5+K2Hds4OAy4Ebo8eDgKWi7blYJoprbMvkLPJw9Lhgi9dOIcyJO7J1chbF2dSZi5pZP+AM4AvgL20VQOnsuaRvSw8YSMNW2wDqRevTkkmqzwxrndX/8Sial1gy5oCkp2vcZDOmX/p3AKquuISKO2+LOSIREekOXeoRcvfpwG3RV758CjQAm5hZjbtPa7Ft1+jxGZg9V21PYLK7P2tm6wJbEBK8d4FnM0sAdML2wCDgJqDYzIYAKxHmsj3t7p916V1Jn5IcMpzyJx+n/KEHqf/bKXGHIzHod8O1FI/7ktRCC1P/52PjDkd6ieTv9qNu3JdUXXYR1cf/hdQSS9K4+ZZxhyUiInnU5SF7ZlZNSGSq3L3LC7W4+2QzO4kwj+xjMxtDGDK5JrA1YfjhVdHuywLzAW9Gc8eOaHW6d8xsiLt/04lLrxs9poD3Ce8po9nMLnf343N5T4VQEyATQyHE0hc07rQz6bIySvxjSrxnDnNUm8ldYvJkKi+7CID6U0aS6F8dc0TzhtrMvDHzpFMpHv8lFfffS80hBzD10adI2cpxh5UTtRnJltqMZKPQ2ktn48g5QTOzZYD/A3Ym9FilM+czs02A64A/uvvz2Z7b3S83s2+B64EjW2x6GbizxVDDhaLHtQnVHg8GxhAKmJxCKCpyH7BhJy6bOddxwNvA+sBH0bmvBf5qZp+7+zXZvp/55++f7SHdppBi6dUW6A877gi1tQx66hHYdP24I8qZ2kwORp0M06bCWmvR/8+/p39xcdwRzVNqM/PA7bfC9xMpevllBu2/F7z2Giy8cNxR5UxtRrKlNiPZ6GntJZFOp7M+KCrM8QYwPyEhWgTYyN2Lo+0lwARgtLv/PofzjwROA0YCtxOGGa5FmOO2FrC7u9ea2cbA2OiwY939ilbneQ3YANjM3V/u4JrXEhK6mcAK7j6hxbbVgfeAL919hSzeSg0wdfLk6eTwbc6rRCI0zkKIpa8ov/cu+v/xCJpWXIkpY/9TOB/fdJLaTG6K/WMGbrERiVSKqaMfpnHTzeMOaZ5Rm5m3EpMnM3CnbSj+8gsa11mXqaMfgcrKuMPKitqMZEttRrJRaO0lEw+hM2lae/vl2oN2JmG+1hbu/oqZnQFslNno7k1m9hKwSbYnNrNto/Nf7u4XtNj0spkNJhTxuBSoBVoWEWmrYuRDhATtt4Tet7nJnOvNlskZgLv/18y+AFYws4HuPqWz7wcgnaYgGgUUViy9XXKHnakuL6fk008o+vBDUquuFndIOVGbyU7lGaeSSKVI7rgLDZtsHsYW9DFqM/NGer75mfrv+xi40zaUvv0W/f/0e6ZdfwsU5Vz/KzZqM5IttRnJRk9rL7n+Fd+B0Dv2ylz2GQ8snsO5d4oen2u9wd2/Az4mJErVwOeEOWMQetlay7zWrxPX9bmcJ9tziZDuX0PDVmEZvvLaB2KORuaF0mefpvyZp0iXllI36uy4w5E+ILXcCky75d+ky8oof3gMVWefEXdIIiLSRbkmaPMB4zrYJwGU53DusuhxwXa2Lwg0A43uPgvIJImrtrFv5rVxnbhuJiH8VTUHMysFVgDqgB87cS4RAJJDo0Wrx4zuWR/dSPaamqgedSoAMw85gtRy2YyGFsld44YbM/2KqwGovPr/qLjlxpgjEhGRrsg1QfseWLGDfdYAfrUuWSdk5pQdZ2YDWm4wsyMJa6+96u7J6OVM0Y5RZlbeYt+VgRHAdODxFq+XmtnKZrZ8y3O7++fAk4TeucNaxXQSMJDQa6i10KTTGnbYiXR5OSWff0bx//4bdzjSjSpuv4WSjz+iedAg6v/6t7jDkT4mucfe1EVLelSf9FdKn3065ohERCRXuc5Bewo4wMx+4+7vt95oZpsRSuJfkcO57wX+AGwOfGJmtYThhetE55xJqLSYcRewG7AH8J6ZPUGYeLc7UAEc6O4/t9h/cUJ1xvGERbFb+iOhR+46MxtGGE65dnTd8cAJObwf6cPS1f1p2GZ7yh99iPLa0dSvvkbcIUk3SEybStVF5wJQd8LJpAcOijki6Yvq/3oixV9+QcW9d1Fz2EFMefjJHjv3VUSkL8u1B+0cQqL0opmdShj+h5ntZGZnE3qsJgEXZ3tid08RFo0+GfgW2Bc4BjBCRcd13f2NFvungX0ISVsT8HtgOCHR2trd78ji2p8TCorcTFgX7WhCT+HVwPrRHDiRrMwe5lirYY69VeUVl1I0aRJNK67ErIMOjTsc6asSCaZfdiUNG29K0YzpDNhvT4q+139bIiI9TU5l9gHMbANC79XShDpliRaPXwF7uPubeYqzp6oBpk6aFH9pz0QCFligP4UQS58zYwYLrLociVmz+OmZl0mt8Zu4I+oUtZnOKRr3JfNtuh6Jhgam3nEPDdvtGHdIsVGbKQyJn39i4C7bUfLZpzSuuTZTHnwUqqriDqtNajOSLbUZyUahtZdMPHRQZj/nWrzu/jqhd2l3Qk/Z9YR1yvYGVlJyJhKprqZh2x0AqKgdHXMwkm/VZ59BoqGBhs23mv1zFolTetB8TL3jXprnn5/S996h5g+HQSrV8YEiIlIQcu5Bk05RD5oAUP7g/dQccTCpZZblp9ff7RGLVqvNdKzktVcZNGQH0kVF/Pzs2D4/30dtprCUvPE6A3fflUQySf3v/0Td2efHHdKvqM1IttRmJBuF1l66tQfNzJ41swM72Gd/M3s2l/OL9DbJbXcg3a8fxeO+pOSD9+IOR/KhuZnqkScBMGu/g/p8ciaFp2n9DZh+5T8BqPzX1VTccG3MEYmISGfkOsRxS35dAbG1pYEtcjy/SO/SYphj+RgNc+wNyu+7m9J336G5uj91J54adzgibUoO250Zp4bFq6tP/RtlTz8Rc0QiItKRnOegdUIV0NiN5xfpUWZp0ereo76eqnPPDP885q+kF1oo5oBE2jfz6OOYue8BJJqb6X/4wRR/8KvVcUREpIB0eh00M1uq1UsD23gNoBhYklA8ZFzuoYn0Lg3bbE+6spLir8ZR8t47NK21TtwhSY4q//F3iidOILXU0sw84o9xhyMyd4kEMy6+guKvv6bspecZsP9eTHnsGZoXWzzuyEREpA3Z9KCNA76MvtLAX1o8b/n1GfAcocLjdXmMVaRnq6oiGZVg1zDHnqto4gQqr7oCgLrTz4SKingDEumM0lKm3XgrTSsZxRMnULP/3iRmTI87KhERaUOne9CAW5mzztmBwHvAu23slwJ+Ap5198e7GqBIb5IcMpyKMQ9Q/tCD1I08q0dUc5RfqjrvLBL19TSutwHJIcPjDkek09IDBjL1jnsZtNM2lP73ffofcTDTbr0LSrK5FRARke7W6b/K7j4i828z2wK4yd3/3h1BifRWDdtsR7qyiuKvxlPy7ts0rb1u3CFJFkree4eKu+8EYMbZ5yvBlh6neellmHrbXQwcvgvlTz9J9WknMuP8S9SWRUQKSE5FQtx9WSVnIjmorCS5g4Y59kjpNFUjTwFg1u570bTOb2MOSCQ3Teuux7SrwwyEfjdeR7/rrok5IhERaSnXddBWNbOjzWzBdrYvFG1fpWvhifQ+ycFRNcdaVXPsScoeeYiyV8eSrqig7rRRcYcj0iUNg4cyY+TZAFSdfjJljz0Sc0QiIpKRa5n9k4ATgcntbJ8MnAD8Lcfzi/Ras4c5fvM1JW+/GXc40hnJJNVnnQ5A/R+PonnxJWIOSKTrZv7paGYeeAiJdJqaPxxKyXvvxB2SiIiQe4K2GfCMuze3tdHdU8AzwOa5BibSa/XrR3LHnQANc+wp+t1wLcXjviS18CLU//nYuMMRyY9EghkXXELDVtuQqK+nZr+9KPrm67ijEhHp83JN0BYBOvor/i2waI7nF+nVkkN2A6D8oQehuc3POaRAJCZNovKyiwCoO2UkVFfHHJFIHpWUMO36W2haZTWKf/ieAfvtRWL6tLijEhHp03JN0OqAhTrYZyFgVo7nF+nVGrbeluaqaoq//YaSt/4TdzgyF1UXn0fRtKk0rv4bknvtE3c4InmX7l/D1DvuIbXQwpR89D9qDjsImpriDktEpM/KNUF7GxhmZgPb2mhmg4Dh0X4i0lpFBQ077gxAee2D8cYi7Sr2j6m49SYA6s4+H4qLY45IpHs0L7Ek0+64h3RlJWXPPUP1SceriJGISExyTdCuBuYHnjOzX8wzi9ZIew4YBFzVtfBEeq/kUA1zLHRVo04lkUqR3GlXGjfZLO5wRLpV05prM+2aG0gnEvS79Ub6XaP/wkVE4pDrOmhjgMuBNQlJWr2ZfWFm9cCzwG+AS9z9wbxFKtLLNGy5Nc39ayie8C0lb2qYY6EpffZpyp95inRpKXVnnBV3OCLzRMNOu1B31nkAVJ15GmUPjYk5IhGRvifXHjTc/a/AEOAJwpy0JYAZwGPALu5+Yl4iFOmtfjHM8YGYg5FfaGqietSpAMw85AhSy60Qc0Ai887MI/7IzEMOD+X3/3S45smKiMxjJV052N0fBh7OUywifU5yyHAq7r2L8toHqTvrfCjK+TMTyaOK22+h5OOPaJ5vPur/quUcpY9JJJhxzoUUfTWe8qefZMABv+Pnx5+leaml445MRKRP0N2gSIxmD3P8biIlb7wedzgCJKZNpeqicwGoO+Fk0gMHxRyRSAxKSph+7U00rbYGRZN+ZMB+e5KYOiXuqERE+gQlaCJxKi+nYaddwj81zLEgVF5+CUWTJtG04krMOvCQuMMRiU26un8ov7/IopT4x9QcciA0NsYdlohIr9epIY5m1gw0A6u6+yfR887U3027e5eGUYr0dsmhw6m459+UPzSGurMvUCn3GBWN+5J+110DQN2oc6C0NOaIROLVvNjiTL3jXgYN3oGyl56n+m/HMuOyKyGRiDs0EZFeq7PJ04uEhKy+1XMR6aKGLbamuWYAxd9/R+kbr9G40SZxh9RnVZ99BomGBhq22IqGbXeIOxyRgpBa4zdMu+4mag74Hf3uuJXUsssx8+jj4g5LRKTX6lSC5u5bzu25iHRBWRkNO+9KxV13UF47WglaTEpfe4Xyhx4kXVTEjDPPUw+BSAsN2+3IjHMvov/Jx1N9ziial15m9lqOIiKSX5qDJlIAkkOHA4Q1h1KpmKPpg5qbqTr9ZABm7XcQqVVXizkgkcIz69AjqP/9HwHo/+ffU/IfFTYSEekOStBECkDDZlvSPHAgxT98T+nrr8YdTp9Tft/dlL73Ds3V/ak78dS4wxEpWHWjziW5484kkkkGHPg7isZ9GXdIIiK9TmeLhNyY4/nT7n5ojseK9B1lZSR3Hky/O2+jfMwDNG68adwR9R11dVSdeyYA9cccT3qhhWIOSKSAFRcz7ZobGDh0J0rff5cB++7BlEef1nIUIiJ51NkiISPaeT0NtDVRI/N6GlCCJtIJySHDQoL20BhmnHexqjnOI5X/+DvFEyeQWmppZh7xh7jDESl8VVVMu/1uBu60DSWffUrNwfsz9e7RUFYWd2QiIr1CZ4c4Ltvqa3ngYWAycDqwJbBK9Dgyev0hYMW8RivSizVGwxyLJv1I6atj4w6nTyiaOIHKq/8PgLrTz4SKipgjEukZmhdZlKm330NzdX/Kxr5E/+OOgrSKO4uI5ENnqziOb/nczE4CNgDWdPeJLXcFXjSzm4B3gD2Ai/IUq0jvVlpKcpch9LvjVsrHjKZx083jjqjXqzrvLBL19TSuvyHJIcPjDkekR0mttjrTrr+FAfvtScU9/ya17HLU//XEuMMSEenxci0ScihwT6vkbDZ3/xa4Bzg818BE+qJMklD+yBhoaoo5mt6t5L13qLj7TgBmnKWy+iK5aNx6W2ZccCkAVReeS/l9d8cckYhIz5drgrYEMKuDfWZF+4lIJzVuujnN881H0aRJlL7yctzh9F7p9Jyy+rvvRdM6v405IJGea9ZBh1D/x6MB6H/Mnyh97ZWYIxIR6dlyTdC+AYabWZsTNsysEhge7ScinRUNcwQor30w3lh6sbJHHqLstVdI9+tH3Wmj4g5HpMerG3kWyV2GkGhooOagfSj+4rO4QxIR6bFyTdCuB5YDxprZUDObH8DM5jezYcDLwDLAdfkIUqQv0TDHbpZMUn3maQDU/+EomhdXR79IlxUVMe3qa2lcZ12Kfv6Zmn32IPHT5LijEhHpkXJN0C4GbgLWBh4AfjCzRuAH4H5gLeDmaD8RyULjJpvRPP/8FE2eTOnYl+IOp9fpd/2/KB4/jtTCi1D/52PiDkek96isZOqtd5NacilKvvyCAQftC8lk3FGJiPQ4OSVo7t4cLUC9FXALoWLjuOjxZmBrdz/U3VVzVyRbJSUkdxkKQHnt6JiD6V0SkyZReVkoLFt3ykioro45IpHeJb3QQky98z6aawZQ+vqr9P/LH1V+X0QkS51dqLpN7v4C8EKeYhGRSHLIMPrdeiPlj9SGCmmlpXGH1CtUXXweRdOn0bjGmiT33jfucER6pZStzLQbbmXAPrtT8cC9ofz+iafGHZaISI+R6xBHEelGjRtvSvMCC1D000+Uvvxi3OH0CsX+MRW33gRA3VnnQZH+/Il0l8YttmLGxVcAUHXphZRHS1qIiEjHcr5DMbMSMzvWzN4ws2lm1tRi21pm9g8zWyk/YYr0MRrmmHfVZ5xCIpUiudOuNG6yWdzhiPR6s/Y7kPq//BWA/scdpTm1IiKdlFOCZmb9gOeAS4ClgWlAy1VevwQOBg7saoAifVVyaKaaYy00NsYcTc9W+uxTlD37NOnSUmaccXbc4Yj0GXUnn86sobuRaGykZsR+FH/6SdwhiYgUvFx70E4BNgFOBhYhlN2fzd2nEuam7dCl6ET6sMaNNqF5gQUpmjKF0peejzucnqupieozwvyXmYf+nubllo85IJE+pKiI6X+/hsbfrk/R1CkM2HcPEpMmxR2ViEhByzVB2xt4zt0viio1tlWi6QtgqZwjE+nriotJDs4Mc3ww3lh6sIrbbqbEP6Z5vvmoP+6EuMMR6Xv69WPqrXeRWnoZisePY8CBv4OZMyGVCsMe//3v8JhKxR2pFDq1GclGD24vuSZoSwFvdrDPdGBAjucXESA5dDcAyh99CBoaYo6m50lMm0rVRecCUHfCyaQHDoo5IpG+Kb3AAqH8/oCBlL75BgP2GMJ866zGgGG7wL77MmDYLsy37mqUPVwbd6hSoMoermW+ddVmpHN6envJNUGbDizUwT7LAz/meH4RARo32IjUQgtTNGUKZS89H3c4PU7l5ZdQNHkyTSuuxKwDD4k7HJE+LbXiSky7+Q7SxcWU/ed1iiZO+MX2ookTqTn0gB5zAyXzTtnDtdQcegBFE9RmpGO9ob3kmqC9Bgw2s4FtbTSzJYGdAdUHF+mK4mIaMsMcx6iaYzaKxn1Jv+uuAaDuzHO1lpxIAWjccGPS/fsDv6wsBpCIFrSuPu3EHjUUSbpZKkX1aX+DdFptRjrWS9pLrgtVX0yo4viMmR2dOY+ZVQIbAVdGr12WjyBF+rLkkOH0u+Fayh59GC75PygrizukHqH6rJEkGhpo2GIrGrbZPu5wRAQofe0ViqZMaXd7Ip2meMK3DBi6E+n5F5h3gUnBSkyeRHGrnpBfbFebkRY6215KX3uloJfcySlBc/cXzezPwP/xy16y6dFjCviju7/VxfhE+rzG9TcktfAiFH//HWUvPEvDdjvGHVLBK33tFcofHkO6qIgZZ50Pidafo4lIHIq+/65T+5W98Vo3RyK9jdqMZKOzf4vikmsPGu5+jZk9DxwJbADMR1gP7XXgH+7+v7xEKNLXRdUcK6//F+VjRitB60hzM1WnnwzArP1HkFpl1ZgDEpGM5oUX6dR+9Uf+mdQKK3ZzNNITFH/2KZX/vKrD/dRmBDrfXjr7tyguiXS6rQr5c2dmmwPT3P3dvEfUu9QAUydNmk4O3+a8SiRggQX6UwixSPZKXnuVQUN2oLl/DZM//BzKy7v9mj21zZTffSc1Rx1Jc3V/fnrtHdILdVTPSPKlp7YZmYdSKeZbdzWKJk6cPR+kpXQiQfOii/HTW/+F4uIYApSCozYj2Sjw9pL5f5JQ6X5ae/vlWiTkOeCIHI8VkSw1rb8BqUUWpWj6NMqefzbucApXXR1V550FQP0xxys5Eyk0xcXMOOciINwotZR5PuOcC3WjLXOozUg2ekl7yTVB+wGYlc9ARGQuiopIDhkGQHmtqjm2p/Iff6d44gRSSy3NzCP+EHc4ItKGhl2HMO2G22hedNFfvN686GJMu+E2GnYdElNkUqjUZiQbvaG95DrE8VZgdWBdd9dAlvZpiKPkTckbrzNo1+3CMMf/fQYVFd16vZ7WZoomTmC+jdYhUV/P1OtvoWHI8LhD6nN6WpuRmKVSlL3+CgPqpzK1cgANG2xc8J9qS8zUZiQbBdheOjvEMdciIScBrwLXmtmJ7v5Tjudpk5klgOHAUcDKhDfxNfA8cKG7f9HGMcsCpwDbA4sAU4APCQVL7s0xjhOBC6KnG7m7SgRJbJp+ux6pxRaneMK3lD3/LA077hx3SAWl6twzSdTX07j+hjQMHhZ3OCLSkeLiUOZ6gf40TpoOSuqlI2ozko0e3F5yTdBuJyRAhwD7m9mXwPf8+q2n3X2bHM5/CXAcMBF4kJBhrgkcDuxjZhu7+38zO5vZdtF+AA8BXwCDgN8A2wJZJ2hmtjpwJlAHVOXwHkTyq6iI5OBhVP7rasrHPKAErYWSd9+m4p5/AzDjbJXVFxERkZ4r1wRtyxb/Lif0cq3cxn5Z56pmtghwDDAeWNPdp7bYdixh8evjCMkhZrYUcB/wLbCtu3/V6nxZv0czKwVuAd4FPgX2z/YcIt0hOSQkaGWPPwozZ0K/fnGHFL90mqqRpwAwa4+9aVp73ZgDEhEREcldrgtV51pcpDOWIRQvGdsyOYs8TEjQFmzx2imEuV7DWydnAO7elEMMpwKrAesAf8vheJFu0bTueqQWX4Lib7+h7LlnaNh517hDil3Zw7WUvfYK6X79qDv1jLjDEREREemS7ky0cvUp0ABsYmY1rbZl7kafgdlz1fYEJrv7s2a2rpkdZ2bHm9m2Zpb1+zOzdQgJ2pnu/mHub0OkG0TDHAHKax+IN5ZCkExSfdbpANT/4SiaF18i5oBEREREuiarHjQz2wg4F1iPMHzxdeA0d389XwG5+2QzOwm4FPjYzMYwZw7a1sA/gMwS4csC8wFvmtm/+PXabO+Y2RB3/6Yz1zazcuBWwtDGi7r6XjIKYTpMJoZCiEW6pmHYcCr/eRXljz/GjFndN8yxJ7SZfjf8i+Lx40gtvAgzjzqmoGPtC3pCm5HCojYj2VKbkWwUWnvpbBydTtDMbA1Cz1XL2t7bABub2fru/r9sApwbd7/czL4FrgeObLHpZeDOFsMWM6vQrk2YA3cwMIZQ9fEUQlGR+4ANO3nps4AVCcsHpLr0JlqYf/7++TpVlxVSLJKj7beCpZYi8dVXLPDmWBjeveXkC7bN/PgjXBY+Rym+4HwWWGbRDg6QeaVg24wULLUZyZbajGSjp7WXbHrQTiIkZ+cCV0av/Qk4HTgRODBfQZnZSOA0YCRzKkauBVwOPG9mu7t7LXOGaBYDp7v7zdHzn4EjzOw3wAZmtqm7v9zBNTcCjgdGtawQmQ+TJ8e/JlAiERpnIcQiXVc5eBiVV/+d5G13MH2zbbvlGoXeZqpOPJl+06bRtMaaTNl5OEyaHndIfV6htxkpPGozki21GclGobWXTDwdySZB2wx42d1Pb/HaGWa2JbBFduG1z8y2JZS3v9zdL2ix6WUzG0wooX8pUAu0LCJS28bpHgI2AH5L6H1r75olhKqN7zNn3bO8SacpiEYBhRWL5C45ZDiVV/+dsiceJ11XD5WV3XatQmwzxR9/RMUtNwGhrH46UdSj1jfp7QqxzUhhU5uRbKnNSDZ6WnvJpojGwkBbCzW/Hm3Ll52ix+dab3D374CPgRXMrBr4HMgMRZzSxrkyr3U0SaeaMLRxLaDBzNKZL+CgaJ9Xo9eGde5tiHSfprXWIbXU0iTq6yh75qm4w5nnqkedSqK5meTOg2nceNO4wxERERHJm2wStFJgRhuv10Xb8qUselywne0LAs1Ao7vPAl6JXl+1jX0zr43r4JpJ4IZ2vj6N9qmNnnd0LpHul0iQHBLmnpXXjo45mHmr9NmnKHv2adKlpcwYeVbc4YiIiIjkVa4LVXenscCfgePM7P5WC1UfCSxBWCMtGb18DWH45Sgz2yXzupmtDIwApgOPtzhHKbA8IcH7HMDdZwKHtRWMmd1M6F07393b6kEUiUVyyDAqr7qC8qceZ3pdHVRVxR1S92tqovqMUwGYeejvaV5u+ZgDEhEREcmvbBO0/c2sdUXEFQDM7NE29k+7+y5ZXuNe4A/A5sAnZlZLGKq4DqHM/kzguBb73wXsBuwBvGdmTxCqOO5OKGpyoLv/3GL/xYGPgPGERbFFeqSmNdcmtdQyFH81jrJnnqRhSPdWcywEFbfdTIl/TPN881H/V60hLyIiIr1PtgnaCtFXW3Zs47Wsp+O5e8rMtgeOBfYC9iUMe/yeUNHxPHf/qMX+aTPbhzDU8VDg94Qhi69E+76QbQwiPUIiQXLocCqvvJyKMaN7fYKWmDqFqovOBaDuhFNIDxgYb0AiIiIi3SCR7mRJEzNbOpcLuPv4XI7rJWqAqZMmxV/aM5GABRboTyHEIvlT8v67DNp2c9L9+jHpwy/yOsyx0NpM1ajTqPzH32lacSV+fv5VKM3n1FfJh0JrM1L41GYkW2ozko1Cay+ZeAij/aa1t1+ne9D6eKIlUpCa1liT1DLLUjzuS8qffoLk0N3iDqlbFH35Bf2u/ycAdWeeq+RMREREeq1sqjiKSKFJJGYnZeVjem81x+qzzyDR0EDDllvTsM32cYcjIiIi0m2UoIn0cLOiuWdlTz8BM9paCaNnK311LOUPjyFdVMSMM88L4wNEREREeiklaCI9XGr1NWhabnkSs2ZR/tTjHR/QkzQ3UzXyFABm7T+C1CptLXcoIiIi0nsoQRPp6aJqjtD7hjmW33sXpe+9Q3P/GupOPDXucERERES6nRI0kV4gOTga5vjMkyRmTI85mjypq6Pq3DMBqD/meNILLhhzQCIiIiLdTwmaSC+QWm11mpZfgUQySdkTj8UdTl5UXv1/FH83kdRSSzPz8CPjDkdERERknlCCJtIb9LJhjkUTJ1B59f8BMGPkWVBREXNEIiIiIvOGEjSRXiI5JJTbL3vuaRLT2137sEeoOvdMEjNn0rjBRjQMHhZ3OCIiIiLzjBI0kV4itcqqNK24Uo8f5ljy7ttU3PNvAGacpbL6IiIi0rcoQRPpLRIJktGaaOW1PXSYYzo9p6z+HnvTtPa6MQckIiIiMm8pQRPpRZJDo2GOzz5NYtrUmKPJXtnDtZS99grpfv2oO21U3OGIiIiIzHNK0ER6kdTKq9BkK5P4//buO06q8uz/+GcWlqUsYFRCsRHbhSUqNhRSjFFjAdQkGk1s2KJJNIo1oCBE0WjUFFseC1h+0cQUQKxRQQMiaiIm+UUv86jYSwBFWGDbzPPHfQaGcWZ3Fnb3nN35vl8vX8c95577XGfnZvZcc5dTV0e3Rx6KO5yWqa2levKlAKz8wdmkB20Wc0AiIiIi7U8Jmkgn01GHOfa49Ra6vLmIxv4DWPnDH8cdjoiIiEgslKCJdDLZBK3b7CdILfsk3mBKlFq8mJ7XXwNAzfiJUF0dc0QiIiIi8VCCJtLJNNoQGobsQKq+nm4PPxh3OCXpdfUVVCz/lPpddqP26GPjDkdEREQkNkrQRDqhjjTMscsrL9P9rqkA1EyeAhX6WBIREZHypTshkU5ozTDHp2aT+uTjmKNpWvXEcaTSaWoPHUX98C/FHY6IiIhIrJSgiXRCjdsbDTvsFIY5Jng1x8on/0K32U+QqaxkxYTJcYcjIiIiEjslaCKdVO3h0TDHGX+KOZIiGhqonjgegFWnnkF6621iDkhEREQkfkrQRDqpdYY5frw05mg+q/tdU+nqr5DeeGNWjr0g7nBEREREEkEJmkgn1bjtdjTs9EVSDQ1UJWw1x9SyT+h1zRQAai4YR6bvRvEGJCIiIpIQStBEOrGkDnPsef3PqViyhIbtjdUnnhx3OCIiIiKJoQRNpBOrHX0EAJVPzyG1dEm8wUQq3nidHrfeDEDNpCuga9eYIxIRERFJDiVoIp1Y49bbUr/zLqQaG6l6aFbc4QBQPXkCqfp66vbbn7r9D4w7HBEREZFEUYIm0sklaZhj5fx5VD04k0xFBSsmTYFUKu6QRERERBJFCZpIJ1c76ggAKuc+TWpJjMMc02l6TRgHwOrjx9C4w47xxSIiIiKSUErQRDq59NbbUL/LbtEwxwdii6Pq9/dS+dKLpHv3oebCcbHFISIiIpJkStBEykD2mWhVM/4cTwA1NfSaMhmAleecT6Zfv3jiEBEREUk4JWgiZWDNao5znyK1eHG7n7/njb+kywfv07jlYFadfma7n19ERESko1CCJlIG0oO/QP1uQ0ml01Q9OLNdz13x3rv0vPGXAKyYOBmqqtr1/CIiIiIdiRI0kTJROyoa5jizfYc59rpiEqlVq6gfti91Iw9v13OLiIiIdDRK0ETKxJphjvP+Suqjj9rlnF0X/p3u998HwIrJWlZfREREpDlK0ETKRHqrwdQP3b39hjlmMlRf+hMAVh91DA1D92j7c4qIiIh0cErQRMpI7ehvAu0zzLHbrBlULphPpkcPasZPbPPziYiIiHQGStBEysiaYY7z55H68MM2PFEt1ZMmALDyB2eTHrRZ251LREREpBNRgiZSRtJbbEn9Hnu2+TDHHrfeQpe3FtE4YCArf3ROm51HREREpLNRgiZSZtp6mGPqv/+l5/XXAFAzbgL06tUm5xERERHpjJSgiZSZ2lFhqfvK+fOo+PCDVq+/19VTqFj+KfW77Ebt0ce2ev0iIiIinZkSNJEyk958C+r33JtUJkO3WTNate4ur7xM97unAlDz0yuhQh8xIiIiIi2huyeRMpRdLKRqRusOc6yeOI5UOk3tYaOp33dEq9YtIiIiUg6UoImUodpRRwBQuWA+Fe+/1yp1dnviMbrNfoJMZSUrLp3UKnWKiIiIlBslaCJlKL3Z5tTvNYxUJkNVawxzbGig18TxAKw69QzSW2+z4XWKiIiIlCElaCJlqvbwI4HWGebY/a6pdH3VSW+yCSvHXrDB9YmIiIiUKyVoImVqzTDH557doGGOqWWf0OvqKwCouWAcmb4btUJ0IiIiIuVJCZpImUoPHET9sH0BqHpg+nrX0/O6a6hYupSG7Y3VJ4xppehEREREypMSNJEytnoDhzlWvPE6PW67BYCaSVdA166tFpuIiIhIOVKCJlLG6kYeTiaVovL5BVS8+06LX189eQKp+nrqvvZ16r5+UBtEKCIiIlJelKCJlLH0gIHU7zMcaPkwx8pn5lL14EwyFRWsuOyKNohOREREpPwoQRMpc+v10Op0ml4TxgGw+vgxNO6wYxtEJiIiIlJ+lKCJlLk1wxz/9jwVb79V0muqfn8vlf9YSLp3H2ouGt/GEYqIiIiUDyVoImUu3X8A9fuOAKDqgRIeWl1TQ68pkwFYee4FZDbdtC3DExERESkrStBEhNrR0WqOM//UbNmeN/yCLh+8T+OWg1l12hltHZqIiIhIWVGCJiLUHjaaTEUFlX//GxVvvVm0XMV779Lzpl8BsGLiZKiqaq8QRURERMpCIh9aZGYp4EjgLGAI0Bd4G5gD/MzdXy/wmi8A44CDgAHAJ8C/gZvc/f4SzrkZcBRwaHTOAcBSYB5wtbsv2NDrEkmqTP/+1A//Et3mPk3VAzNY9cOzC5brdcUkUqtWUT9sX+pGHt7OUYqIiIh0fkntQfs58EfAgOnAr4E3gNOAhWa2c25hMzsQ+BfwXWA+cC3wJ6AbcECJ5zwLuB7YGngsqmMucDjwjJl9Z4OuSCThmhvm2PXFv9H9/vsAWDF5CqRS7RabiIiISLlIXA+amQ0AzgHeBHZ192U5x84FrgPGAidH+7YE/gC8Cxzg7m/l1VfqNT4H7OfuT+W9/svAE8DNZjbd3WvX57pEkq72sNFUX3welS/+nYo3F5EZPHjtwUyG6uyy+kcdQ8PQPeIJUkRERKSTS2IP2mBCXPNyk7PIrGjbL2ffOKAPcEZ+cgbg7g2lnNTd/5SfnEX7/wrMBj4HfLGUukQ6oky/ftSP+AoAVTOnr3Os26wZVC6YT6ZHD2rGT4whOhEREZHykMQE7T9AHTDCzPrkHRsZbZ+ANXPVjgKWuPuTZraHmY01s/PN7AAza63rq4+2JSV7Ih3VmodWz8x5aPXq1VRPmgDAyh/+mPSgzWKITERERKQ8JG6Io7svMbOLCXPAXjGzGcCnwK7A/sBNwA1R8S8AGwMvmNlvgNPzqnvRzEa7+zvrG080hPIA4H3gn+tTRxKm6mRjSEIsklx1I0eTuWgslS+9SJdFr8Omu9Lj1lvo8tYiGgcMZNWPfqw2JEXpc0ZaSm1GWkptRloiae2l1DhSmUymbSNZT2Z2NHAb0Dtn91zgYnefF5XZh7AoSCOwirDQxwzCqo/jCIuKLHD3fdYzhkrgceArwAnufncLq+gD5A/TFEm2Aw+Exx+H006D3XeH886DlSth2jQ48cS4oxMRERHp6PoSOqAKSlwPGoCZTQAuASYA9xCWzN+NsMriHDP7lrvPZO0QzS7Ape4+Lfr5Y+B0M9sFGGZmX3L3uS2MoQKYRkjObl2P5GyNJUuWE3cenErBJpv0TkQskmy9Nt+KHgC33rpmX6aykuXpCuoWL48tLkk+fc5IS6nNSEupzUhLJK29ZONpTuISNDM7AJgEXO/uV+Ucmmtmo4DXCcMfZ7Ju79TMAtU9AAwD9iT0vpUaQwVwB2HZ/nuAM1pyDfkyGRLRKCBZsUjydJs1k+533kEGWKcXvr6e3qecyKe3303dyNExRScdhT5npKXUZqSl1GakJTpae0niIiGHRNvZ+Qfc/QPgFWBbM6sGXiMMb4TQy5Yvu69HqSePkrOpwInAvcBJ7p4u9fUiHVZjI9WXXAiZDPlDpLM/V19yETQ25r9SRERERFpJEhO0btG2X5Hj/YA0UO/uq4Fnov07Fiib3beolBPnJGcnAL8Djnd33Y1KWah89hm6vPfeZ5KzrFQmQ5f33qXy2WeKlBARERGRDZXEBG1etB1rZn1zD5jZGcDmwPycB0bfHG0vM7OqnLJDgJOA5cAjOfsrzWyImW2TV3d2WOMJwP3AcUrOpJxUfPhBq5YTERERkZZL3Bw0QnJ0JmFxjlfNbCZhqOLuhGX2VwFjc8rfB3wT+Dbwkpk9SlgZ5VtAd8Lqix/nlN8MeBl4k/BQ7KwJhGGNK4BXgUvMLD+26e6+cEMvUCSJ0v0HtGo5EREREWm5xCVo7t5oZgcB5wJHExbq6AZ8SFiwY4q7v5xTPmNmxxKGOp4CfB+ojX6e4u5PlXjqwdG2GhhfpMwiYGELLkekw6jfZziNgwZR8f77pArMpM2kUqQHDqJ+n+ExRCciIiJSHhL7HLROog+wbPHi+Jf2TKVg0017k4RYJLm6zZpJn1OOB1gnSctET1bUKo7SFH3OSEupzUhLqc1ISyStvWTjoZnnoCVxDpqIxKRu5Gg+vf1u0gMHrrM/PXCQkjMRERGRdpC4IY4iEq+6kaNZeshhdFvwDH1XLmNZz77UDRsOXbrEHZqIiIhIp6cETUQ+q0sX6kd8GTbtTf3i5ZCAYQEiIiIi5UBDHEVERERERBJCCZqIiIiIiEhCKEETERERERFJCCVoIiIiIiIiCaEETUREREREJCGUoImIiIiIiCSEEjQREREREZGEUIImIiIiIiKSEErQREREREREEkIJmoiIiIiISEIoQRMREREREUkIJWgiIiIiIiIJoQRNREREREQkIbrGHUA5SKXijmBtDEmIRToGtRlpKbUZaSm1GWkptRlpiaS1l1LjSGUymbaNpLxtBrwTdxAiIiIiIpIYmwPvFjuoBK1tpYBBwPK4AxERERERkdj1Bt4DiiZhStBEREREREQSQouEiIiIiIiIJIQSNBERERERkYRQgiYiIiIiIpIQStBEREREREQSQgmaiIiIiIhIQihBExERERERSQglaCIiIiIiIgmhBE1ERERERCQhusYdgLQNM+sOTAH2BLYFNgY+AV4DbgPucff62AKUDsPMLgKuin7c192fjTMeSRYzWwRsVeTwU+6+X/tFIx2JmR0J/ADYHegFvA88C1zo7m/HGZskh5mdBExtptiT7v71dghHOggzSwFHAmcBQ4C+wNvAHOBn7v56fNE1Twla51UNnAk8BzwI/Bf4HHAIcAdwjJkd4u7p+EKUpDOznYFJQA3hBkqkkGXALwrsX9S+YUhHEN043QKcTvjS8D5gOTAI+Coh4VeCJlkLCX+HCvk2sBPwaLtFIx3Fz4GxhC9+pgOfArsCpwHHmtlwd/9XfOE1TQla57UU6Ovudbk7zawr8BfgIEKy9mAMsUkHYGaVwJ2EP47/AY6LNSBJsk/c/bK4g5AO42xCcnYTcLa7N+YejP5OiQDg7gsJf4fWYWbdgB8BDYS/VSIAmNkA4BzgTWBXd1+Wc+xc4DpC8nZyLAGWQHPQOil3T+cnZ9H+BuDP0Y/btm9U0sGMJ3wzeTLQ2ExZEZFmmVkPYCLwOvDj/OQM1vydEmnOEcAmwCx3/zDmWCRZBhNynHm5yVlkVrTt164RtZC+pSozZlYBHBz9mNiuXYmXme1OSNAmuPu/zSzukCTZqqJ5IoMIw0ied/cF8YYkCXUQYbj9VKCLmY0GtifMkX7c3f83xtikYzk12t4WaxSSRP8B6oARZtbH3T/NOTYy2j7R/mGVTglaJxcNARgHpAjfNH2dMFlyqrsnunFKPMysCriLMKTk6nijkQ5iAHmT+M3seeBYd38tnpAkofaIto3APwjJWVbazK539/PbPyzpSMxsK8L9zDvAIzGHIwnj7kvM7GLgWuAVM5vB2jlo+xOGV98QY4jN0hDHzq8bYTjJBOCHgBEmTp4eZ1CSaJOB7YAxhYYfieSZSrhR6k9YSGYocDewF/CEmfWOMTZJns9H27GExWX2BnoDXwFeBc4zszNjik06jjGEe9hp+jslhbj79cAxhEXzzgAuBL4BLAB+m/Sh1ErQOjl3X+HuKaALsAUhSTsVmGNmfWINThLHzPYFzgcuT/LqRpIc7j7J3Z9094/cfaW7L3T3EwhJ2laEFbNEsrL3HXXAEe7+fPR36q/AUUAaOC+26CTxoqkaY4AMYVVqkc8wswnAPYRHTm1B+CLoy0B3wj3w6BjDa5YStDIRLRryjrvfTOg9G0GYYyQCrFk57U7CsKOrmiku0pzfRNsRsUYhSZOdsP+Cu7+XeyD6Uuh1YBsz26i9A5MO4wBgS8Kzz96IOxhJHjM7gPBohhvc/aro/neFu88FRgH1hOGPiaU5aOXpsWi7X5xBSOJUE4Y2AtQVWRhkfrT/SHef3k5xSce0ONrq+XmSy6PtJ0WOZ/f3aKKMlDctDiLNOSTazs4/4O4fmNkrwFAzq3b3Fe0bWmmUoJWnQdG2PtYoJGlqgduLHPsKIXmbSXjo+aJ2ikk6rmHRdlGcQUjiZG+Ydsg/ED17cVughvA5I7IOM9sEOJzwrNc/N1Ncyle3aFtsKf1+hOHUib0PVoLWSZnZjsAid1+Zt78n4QF9AA+1e2CSWO6+irXfTK7DzKYRErQr3f3Z9oxLksvMhgBvFficGQL8LPrxt+0emCSWu79mZo8BB5nZqe6e2wtyMbARcE/SJ/BLbI4n3Hzf4+61cQcjiTWP8BDzsWb2x7wHVZ8BbE54Rlpi25AStM7raELDnEv4BvtTYDNCt+8mwF+B62OLTkQ6g2MInzNPA28Sej62Bw4FKgkJ/dMxxifJ9APgGeBWMzsCeIWw+uf+hHZ0QXyhScKdEm01vFGacj9wJtHqsGY2kzBkenfC58wqwkqyiaUErfOaRRjKOBzYlzC/aBlhAYj7gDv0DaWIbKDZhKFqQwmrY/UkzD17CLjJ3R9r4rVSpqJetD0Jj/Q4mPDw6g+AG4HJ7v5RnPFJMpnZ3sDOwHPu/s+445HkcvdGMzsIOJfQYfFdQs/rh0QrO7r7yzGG2KxUJpOJOwYRERERERFBy+yLiIiIiIgkhhI0ERERERGRhFCCJiIiIiIikhBK0ERERERERBJCCZqIiIiIiEhCKEETERERERFJCCVoIiIiIiIiCaEETUREREREJCGUoImIiIiIiCRE17gDEBGRDWdmJwFTgTHuPi3eaNqWmfUBfgqMBjYn/C0b6u4Li5QfDLwB3OnuJ7VPlOXJzOYAX3X31AbWkwGecvf92vO8IiJJoARNRKSNmdlvgWOB77r7vU2U6wN8ANQBA919VTuF2NFcDXwfmAXcAzQSfm8iIiIdnhI0EZG2dzshQTsZKJqgRWV6EHp6lJwVNxJ41d1HxR2IfMYJQM+4gxAR6cg0B01EpO09SRhit7+ZbdlEuZOj7e1tH1KHNgh4P+4g5LPc/S13fyXuOEREOjL1oImItDF3z5jZVGAyMAaYlF/GzHYC9gb+4e4vmFlf4AzgUGA7YFNgMfA4MMndX2vuvM3NvSo2z8fMegPnA98GtgZqgQXAT919bl7ZgcDFUZybR2XfB54CLnL3ZSXE2RU4GzgR2J4wxPPvwHXu/kBOuWlRGYCvRvFT6BpKYWbdCEMkjwKuieLNNPOaTYFLgMMJieIyYA4w2d3/lVc2G+82wJHAKdH/39vUXDgz2w+YTWgns4ArgX2ANCHZP9fdF7XgUrP1DiZqD8DlhGveD+gGzAfOc/eXCrzu88BPgFHAFsBywvs7scA1z6HAXDAz6wlcRugl7gf8L/BL4D/Za3X3ywqcuz9hSOthQC/gJeBid59T5Bq7R+f5LvD56HpvAm7If29LbXdR2ZOI5ngCSwhtfhdgibsPjsp8K6pvB6APsBT4N3Czu/+xULwiIoWoB01EpH1MI9xgn2RmhRYyGBNts71nOxASulXAn4FfAC8QbjyfM7Ot2iJIM9uYcLM+AfgYuAX4I7AHMNvMjsgp2xOYB5wFvAb8mnCdrwLHE27EmztfCvgDcC3QHbgR+C2wKzDTzM7NKT6dtcntm9H/T4rO2dLr7A08TEhCz3P3C0tIzvoBzwI/BhYB1xESpm8CC8zsS0Ve+mtgHOH9+wXwzxLD3At4mpA4/CZ6/RHA41Eisr4GE65jY+AO4C/A1wnvb//cgma2DfA34BzWvscPAQcDz5rZsOZOZmZdCInmBYQ29UtCG7sWGNvESzcC5gI7AXcDfwL2BB41s52LvOb3wPeisrcA1cCvgJ/nxdSSdpfrqKjujwiJ38NRfWdG9W1H+Pd6HfAIMICQnIuIlEw9aCIi7cDd3zazxwg3tvsDT2SPRd/kH0fofbon2v0yYaGQpbn1mNnXCL1olwCntUGovybcEJ/m7rflnPcnhAThf8zsEXdfTbip/wLwC3df54bWzKqB+hLOdzyhN+op4CB3r4tefyUhMbjazGa4++vuPh2YbmYTgUWFelxKESUhDwM7Aye4+z3NvCTrZ4QesCvdfVxOfYcCDwJTzczcPZ33ul0Iq0y+1cJQDwWOcfff5ZzrLsLv7AjgvhbWl/VVQi/Uz3Lq/SmhTY0BrsopexcwEDjY3R/NKX85oT3cSri+ppwEfI3wOx/l7o1RHdcT3uNidiUkQWdlf6dm9iRwG/AjQg9zvu2BnbM9t1FbWQCca2b3uvsLUbmS211e/QcD33D3x/P2n0pIpHdz949yD5jZJk1co4jIZ6gHTUSk/WR7x07O2z8S6A/MyCZk7r4sPzmL9s8G/j9wQGsHFw3f+w7wZG5yFp33I8KQuH4Fzv2ZBU3cfYW715Zw2uyQxQuzN8nR698Crid8kfi9ki+iGVGP0DzAgNGlJmfRcMhjCcPbLs895u4PEXqhtgVGFHj5NeuRnAE8nZucRe6ItnutR31ZbxDey1zZtrmmXjMbCgwnDJF9NLewu79KSM6+2ERvVtZx0XZ8NjmL6vg3IQEspoYw7DQ34b0TaKD49f80d1ht9P+XAynWtjVY/3Y3o0ByllVPgS8l3H1JkfIiIgWpB01EpP3MAP4LHGlmfXNuJAsuDhLNRToHGEaYg5b7mV1H69sL6AJUmdllBY5vF22HEIasPU2Yb3axme0a7XsKeLm54YI5hgIr3f25AsdmR9vdSqyrOUMIyVlXYH93X9DC13YHZrv7ygLHZwMHEmL9a96xQtdWikK9S+9E243Ws06AhQV6+QrVu0+07V+kPQzJ2f6rwPGsXYEad3+xwLF5wOlFXvequ6/I3eHuDWb2IcWvP/93n7tvaM6+9W13xd7L+whz5f4VPVZjNjDX3T8tUl5EpCglaCIi7cTd683sbsK8m+8CN5vZAOAQ4C3C0EUAzOwo4HfACuBRwpynlUCGMGSsLeagbRxtR1C4JyirF4TeCTPbhzBXbhRhSB7A22Z2lbvfVMI5+wBvFzn2fk6Z1rA98DngGZpOKArJxvBhkeNNxVrsNc0pdHPfEG27rGedBeuNEp/8erPt4bDov2J6NXO+pt7jpn43xZKbBopff6H6svv6lhjT+ryXPyf0rp4JnEdYZKfBzB4kLOryRpHXiYh8hhI0EZH2dTshQTsFuJkwF6YrMDWvV+MyYDWwh7v/J7cCMzumxHNl6/vMZ320SmS+7A3xte5+fikniIaEnWRmFYS5SAcRVrK70cw+burB3Dnn/HyRYwPy4tpQMwnD+y4DHjKzQ929psTXZmPoX+R4U7GW2puYNNlrOcvdb9jAeootGFPs97m++hO+7Ch0jtwVRde33RV8L6Me4zuAO6I5Z18mDIk9GtjOzHbJHd4pItIUzUETEWlH0bybZ4E9zGwXwqIMGcIS3rm2IQwVzE/OBhKWvi/FJ9F2swLHhhbY93wUy74l1r+Gu6fdfaG7X024MQUYXcJLXwR6mtneBY7tF20XtjSeYtx9EnAp8BXg4Wgxk1K8QkiY94pWr8y3X7RduKExJkh2CGiL20Oel4BeZrZbgWPDN7DufF9uYl/uEMs2a3fuvsTdp7v7dwirfO5ImJ8oIlISJWgiIu0vO9fsJsJy+o+7+5t5Zd4Ets1d9jxaWv1moLKUk0TzXxz4kpmtuUGMlpi/skD5DwjLlA83swsKPQ7AzIZlExQz2yl/WfZIdt/qEsK8M9peaWZrrsvMtiD0NDYA/6+Eekrm7pcD4wk37iUladFCEvcS5gL+JPeYmR0MfIPwbK95rRlrnKL5WQuAY83sO/nHzazCzL5aQlXZ9+/yqKc1+/ohrLtwR2u4NLd3OPr/SwhfPNyZU65V252Z7Zf/7yWqNztMtJR/CyIigIY4iojE4XeE52Fl53ndXqDMr6P/XjSzPxA+rw8krEb3EmHhhVJcC/wPMN/M7id8MXcIobeskB8QVji8GjjezOYTeuK2IDyDajvCsusro3iuMbN5hGefLSH07o0m3JDeWEJ8dxOeI3Y48A8zm0WY0/Qdws3teQWWOt9g7j7FzNKERPURMzs4f0GKAi4iLFF/iZkNJyQvgwnPxloJjCmw+EZHdyxhwYv7zOwcwoOcVwFbEnrW+hEWT2nKVMJQ3sMI7flhwnt7DGH1y1GsHY67oV4lLNSRfTD0twgPUL8uZ4l9aP12Nx341MyeJXy5Ukn497Ej8IcCX8CIiBSlHjQRkXbm7ssJPVUASwk3d/luJDznaSnheWdHElZI3Je1QxdLOdetwA8JDwg+lZCcTWPtMMT88ksJw84uJKwU+T3Cg6j3ISzvfwKwOCr+aBRnH8LN7rmEJO53hLlzL9CMaO7OtwmLKtRH5zqO8DDnw939ulKvtaXc/SpC0jWC8PDj3s2U/y9hRc1fEYagnk+4CZ8ODHP3uW0Va1yixS2GEpaqryYMyf0+YYXDpynSjvLqaCQsIHMtoQfyHMLv/DzW9mS11jzDowkPnP4mYcGOGsKcyHXmVLZBu/sJYdjk3oRntB1HWODnTMKCQCIiJUtlMh117rKIiIh0ZNEDr8cDh7r7w3HHIyKSBOpBExERkTYVLW6Tv29HQu/WJ8Ccdg5JRCSxNAdNRERE2trNZjaY8KDnjwlDREcR5mqd4u6rYoxNRCRRlKCJiIh0UNHCHRuVUHSauy9q02Cadj9hTuU3CQ+MXkGYU3mtuz8aY1wiIomjOWgiIiIdlJktArYqoejX3H1O20YjIiKtQQmaiIiIiIhIQmiREBERERERkYRQgiYiIiIiIpIQStBEREREREQSQgmaiIiIiIhIQihBExERERERSQglaCIiIiIiIgmhBE1ERERERCQhlKCJiIiIiIgkxP8BF9dID5mmFjMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting validation score V/S values of k\n",
        "plt.plot(range(3,9), score, color = 'red', marker = 'o')\n",
        "plt.xlabel('Values of k or n_neighbors')\n",
        "plt.ylabel('Predicted Validation Accuracy')\n",
        "plt.title('Values of K versus Validation Accuracy')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84a0e796",
      "metadata": {
        "id": "84a0e796"
      },
      "source": [
        "So, from the above results, research and conclusion, we can conlude that our best Knn model has n_neighbors value to be 3 respectively."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ba5c91d4",
      "metadata": {
        "id": "ba5c91d4"
      },
      "source": [
        "### Tuning the weights parameter"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ae1f210c",
      "metadata": {
        "id": "ae1f210c"
      },
      "source": [
        "The weights parameter for our Knn model has two values: uniform and distance. By default, it is uniform. Lets check the accuracy when measured with weights parameter valued with distance value and n_neighbors value to be 3."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfe780dc",
      "metadata": {
        "id": "cfe780dc"
      },
      "source": [
        "<b> When weights = uniform? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd2598ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dd2598ac",
        "outputId": "99962dc2-3246-4050-a438-a438b73ddfde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 10.1 s, sys: 37.7 ms, total: 10.1 s\n",
            "Wall time: 7.1 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "87.41499999999999"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "Knn = KNeighborsClassifier(n_jobs = -1, n_neighbors = 3, weights = 'uniform')\n",
        "Knn.fit(X_train,y_train)\n",
        "n_3_pred = Knn.predict(X_val)\n",
        "n_3_pred_accuracy = accuracy_score(y_val, n_3_pred)*100\n",
        "n_3_pred_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fad01c30",
      "metadata": {
        "id": "fad01c30"
      },
      "source": [
        "<b> When weights = distance? </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "91b2bcc5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91b2bcc5",
        "outputId": "6c8ae730-5912-48ee-f52a-061d638dd1fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 9.54 s, sys: 12.6 ms, total: 9.55 s\n",
            "Wall time: 5.05 s\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.88685"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "Knn_weight = KNeighborsClassifier(n_neighbors = 3, n_jobs = -1, weights = 'distance').fit(X_train, y_train)\n",
        "weight_distance_accuracy = accuracy_score(y_val, Knn_weight.predict(X_val))\n",
        "weight_distance_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "50bae6c3",
      "metadata": {
        "id": "50bae6c3"
      },
      "source": [
        "we can see that, when `weights = distance`, our model performs better than when `weights = uniform`."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56d20147",
      "metadata": {
        "id": "56d20147"
      },
      "source": [
        "## Lets make the predictions on the Testing set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04b2d30f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "04b2d30f",
        "outputId": "0cbb1251-65e5-4155-d8d3-0dd984d3f549"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f5b1b2fa-824b-43e2-8728-130ea33226e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.359539</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.712024</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.665363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.455530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.359539</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.712024</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.665363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.453587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.713826</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.671834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.452158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.713826</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.671834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.453396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.712024</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.679922</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.450718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120835</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.710983</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.504984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120836</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.712600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.504884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120837</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.715836</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.501330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120838</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.712600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.499589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120839</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.712600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.499589</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120840 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5b1b2fa-824b-43e2-8728-130ea33226e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f5b1b2fa-824b-43e2-8728-130ea33226e3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f5b1b2fa-824b-43e2-8728-130ea33226e3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "0      -0.086237 -0.748259 -0.807726  3.686720  0.231779 -0.359539 -1.119518   \n",
              "1      -0.086237 -0.748259 -0.807726  3.686720  0.231779 -0.359539 -1.119518   \n",
              "2      -0.086237 -0.748259 -0.807726  3.686720  0.344428 -0.140801 -1.119518   \n",
              "3      -0.086237 -0.748259 -0.807726  3.686720  0.344428 -0.140801 -1.119518   \n",
              "4      -0.086237 -0.748259 -0.807726  3.686720  0.344428 -0.140801 -1.119518   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "120835 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120836 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120837 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120838 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120839 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "\n",
              "              E7        E8       E9       E10      E11        V1        V2  \\\n",
              "0      -0.265267 -0.238101  0.37487  0.036459 -0.25066  0.712024  0.093365   \n",
              "1      -0.265267 -0.238101  0.37487  0.036459 -0.25066  0.712024  0.093365   \n",
              "2      -0.265267 -0.238101  0.37487  0.089394 -0.25066  0.713826  0.266677   \n",
              "3      -0.265267 -0.238101  0.37487  0.089394 -0.25066  0.713826  0.266677   \n",
              "4      -0.265267 -0.238101  0.37487  0.089394 -0.25066  0.712024  0.093365   \n",
              "...          ...       ...      ...       ...      ...       ...       ...   \n",
              "120835 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120836 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120837 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120838 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120839 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "0      -1.068274 -0.244269 -0.468227  0.665363  0.0  0.424004  0.0  0.553032   \n",
              "1      -1.068274 -0.244269 -0.468227  0.665363  0.0  0.424004  0.0  0.553032   \n",
              "2      -1.068274 -0.244269 -0.468227  0.671834  0.0  0.424004  0.0  0.553032   \n",
              "3      -1.068274 -0.244269 -0.468227  0.671834  0.0  0.424004  0.0  0.553032   \n",
              "4      -1.068274 -0.244269 -0.468227  0.679922  0.0  0.424004  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "120835 -1.068274 -0.315493 -0.468227 -1.710983  0.0 -1.102174  0.0 -1.859348   \n",
              "120836 -1.068274 -0.315493 -0.468227 -1.712600  0.0 -1.102174  0.0 -1.859348   \n",
              "120837 -1.068274 -0.315493 -0.468227 -1.715836  0.0 -1.102174  0.0 -1.859348   \n",
              "120838 -1.118540 -0.315493 -0.468227 -1.712600  0.0 -1.102174  0.0 -1.859348   \n",
              "120839 -1.118540 -0.315493 -0.468227 -1.712600  0.0 -1.102174  0.0 -1.859348   \n",
              "\n",
              "             V11  \n",
              "0       0.455530  \n",
              "1       0.453587  \n",
              "2       0.452158  \n",
              "3       0.453396  \n",
              "4       0.450718  \n",
              "...          ...  \n",
              "120835  0.504984  \n",
              "120836  0.504884  \n",
              "120837  0.501330  \n",
              "120838  0.499589  \n",
              "120839  0.499589  \n",
              "\n",
              "[120840 rows x 23 columns]"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# our testing set\n",
        "test_new_df[numeric_feature_cols]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "787f025c",
      "metadata": {
        "id": "787f025c"
      },
      "source": [
        "<b> We will use our optimized KNN model, that has hyperparameters tuned to n_neighbors = 3, and weights = distance. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e146aed1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e146aed1",
        "outputId": "16ddcbf8-a417-4ec1-c0d0-286de2a95d12",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 58.4 s, sys: 68.8 ms, total: 58.5 s\n",
            "Wall time: 34.8 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "testingSet = test_new_df[numeric_feature_cols]\n",
        "testing_set_predictions = Knn_weight.predict(testingSet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "391e8deb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "391e8deb",
        "outputId": "1bfde2bc-7a35-4c9a-fc3a-1187790e7669"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "testing_set_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fbbb7e92",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "fbbb7e92",
        "outputId": "ec588e61-9fa6-4fdb-e8ce-08eb6f234949"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cb1b504d-d761-4fb3-a109-6b99f667488b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Public</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Public</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120835</th>\n",
              "      <td>99</td>\n",
              "      <td>1206</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120836</th>\n",
              "      <td>99</td>\n",
              "      <td>1207</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120837</th>\n",
              "      <td>99</td>\n",
              "      <td>1208</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120838</th>\n",
              "      <td>99</td>\n",
              "      <td>1209</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120839</th>\n",
              "      <td>99</td>\n",
              "      <td>1210</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120840 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb1b504d-d761-4fb3-a109-6b99f667488b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb1b504d-d761-4fb3-a109-6b99f667488b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb1b504d-d761-4fb3-a109-6b99f667488b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        TrialID  ObsNum  Prediction Indicator\n",
              "0             0       0           1    Public\n",
              "1             0       1           1    Public\n",
              "2             0       2           1   Private\n",
              "3             0       3           1   Private\n",
              "4             0       4           1   Private\n",
              "...         ...     ...         ...       ...\n",
              "120835       99    1206           1   Private\n",
              "120836       99    1207           1   Private\n",
              "120837       99    1208           1   Private\n",
              "120838       99    1209           1   Private\n",
              "120839       99    1210           1   Private\n",
              "\n",
              "[120840 rows x 4 columns]"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "solution_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "edc49d46",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edc49d46",
        "outputId": "231729a4-ae0f-48ee-d68e-59b789edd56e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7400943396226415"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(solution_df['Prediction'], testing_set_predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "996df464",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "996df464",
        "outputId": "c6728bc0-bee2-4658-91ac-2886dce3b0ee"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1    0.75245\n",
              "0    0.24755\n",
              "Name: Prediction, dtype: float64"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "solution_df.Prediction.value_counts()/ len(solution_df.Prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "049a34ad",
      "metadata": {
        "id": "049a34ad"
      },
      "source": [
        "We can clearly see that the model that we have developed isn't performing well. In the testing set, if a random row is selected, the chances of column prediction's value to be 1 is 75 %. And, out models accuracy is only 75%."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "66966d63",
      "metadata": {
        "id": "66966d63"
      },
      "outputs": [],
      "source": [
        "# lets create a csv file and save our prediction for this model to be uploaded in kaggle\n",
        "solution_Knn_df = solution_df.copy()\n",
        "solution_Knn_df['Prediction'] = testing_set_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd0f9d46",
      "metadata": {
        "id": "dd0f9d46"
      },
      "outputs": [],
      "source": [
        "# saving out predictions to a new file called 'submission_knn.csv'\n",
        "solution_Knn_df.to_csv('submission_knn.csv', index = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43207f8d",
      "metadata": {
        "id": "43207f8d"
      },
      "source": [
        "# We will now try another model: Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ac70a76",
      "metadata": {
        "id": "5ac70a76"
      },
      "source": [
        "# ---------------------------------------------------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "41cc80e8",
      "metadata": {
        "id": "41cc80e8"
      },
      "source": [
        "Logistic regression is a very popular technique to solve binary classification problems. This project is also a binary classification problem because the `Target` column has only 2 responses i.e 0 and 1 ,where 0 represents that the driver is not alert and 1 represents driver is alert."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3dc0735b",
      "metadata": {
        "id": "3dc0735b"
      },
      "outputs": [],
      "source": [
        "# To train our logistic regression model, lets import logistic regression class from sklearn library\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "log_reg = LogisticRegression(n_jobs = -1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c742216c",
      "metadata": {
        "id": "c742216c"
      },
      "source": [
        "For the logistic regression model, I will be using the original training and validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "66a1f3e9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "66a1f3e9",
        "outputId": "a663a4af-b2f0-45d0-84c3-4e035b536f5c",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-836dd7ea-00e1-4ada-9422-1b70c111c550\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>IsAlert</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>517921</th>\n",
              "      <td>1.026033</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.980233</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>4.399439</td>\n",
              "      <td>-0.974732</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.592893</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.503847</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540287</th>\n",
              "      <td>-1.082106</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>2.101267</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.830487</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.394588</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.220506</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.103160</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.006108</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>244957</th>\n",
              "      <td>1.853768</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.277513</td>\n",
              "      <td>-0.499079</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.761362</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.743011</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.681367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.270053</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374229</th>\n",
              "      <td>1.543367</td>\n",
              "      <td>1.334087</td>\n",
              "      <td>1.923399</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.106168</td>\n",
              "      <td>1.034918</td>\n",
              "      <td>0.121360</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>1.005057</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.142329</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.716529</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-0.210401</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.662128</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.189874</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.435234</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471795</th>\n",
              "      <td>0.185364</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.550936</td>\n",
              "      <td>-3.309303</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-0.016476</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.718332</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.655657</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.063448</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110268</th>\n",
              "      <td>-1.004505</td>\n",
              "      <td>-0.304101</td>\n",
              "      <td>0.702900</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.725737</td>\n",
              "      <td>-0.058774</td>\n",
              "      <td>2.019174</td>\n",
              "      <td>2.186701</td>\n",
              "      <td>1.005057</td>\n",
              "      <td>-2.667592</td>\n",
              "      <td>1.942127</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-0.666308</td>\n",
              "      <td>0.526645</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.372891</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.443651</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259178</th>\n",
              "      <td>-0.034503</td>\n",
              "      <td>1.648343</td>\n",
              "      <td>1.752904</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.218817</td>\n",
              "      <td>-0.113459</td>\n",
              "      <td>-0.207108</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.877838</td>\n",
              "      <td>0.786613</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.872424</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.362862</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365838</th>\n",
              "      <td>-0.357837</td>\n",
              "      <td>1.344052</td>\n",
              "      <td>-0.762440</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.387790</td>\n",
              "      <td>-0.004090</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.075181</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.456318</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.303007</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.814220</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.422174</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131932</th>\n",
              "      <td>-0.319037</td>\n",
              "      <td>1.336934</td>\n",
              "      <td>0.824353</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.006481</td>\n",
              "      <td>-0.222828</td>\n",
              "      <td>-0.280100</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>0.512876</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>0.542605</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.220759</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.419479</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.554076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.305589</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121958</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-3.039085</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.374870</td>\n",
              "      <td>-1.710404</td>\n",
              "      <td>-0.250660</td>\n",
              "      <td>-0.680051</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>1.455078</td>\n",
              "      <td>-0.078312</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.338596</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.285260</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.410837</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>483463 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-836dd7ea-00e1-4ada-9422-1b70c111c550')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-836dd7ea-00e1-4ada-9422-1b70c111c550 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-836dd7ea-00e1-4ada-9422-1b70c111c550');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "517921  1.026033 -0.748259 -0.807726 -0.288786  0.119130  0.980233  0.303842   \n",
              "540287 -1.082106 -0.748259 -0.807726 -0.288786  0.119130  2.101267  0.267346   \n",
              "244957  1.853768 -0.748259 -0.807726 -0.288786  0.119130 -0.277513 -0.499079   \n",
              "374229  1.543367  1.334087  1.923399 -0.288786 -0.106168  1.034918  0.121360   \n",
              "471795  0.185364 -0.748259 -0.807726 -0.288786  0.231779 -0.550936 -3.309303   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "110268 -1.004505 -0.304101  0.702900 -0.288786 -0.725737 -0.058774  2.019174   \n",
              "259178 -0.034503  1.648343  1.752904 -0.288786 -0.218817 -0.113459 -0.207108   \n",
              "365838 -0.357837  1.344052 -0.762440 -0.288786 -0.387790 -0.004090  0.267346   \n",
              "131932 -0.319037  1.336934  0.824353 -0.288786  0.006481 -0.222828 -0.280100   \n",
              "121958 -0.086237 -0.748259 -0.807726  3.686720  0.119130 -3.039085  0.303842   \n",
              "\n",
              "              E7        E8        E9       E10       E11        V1        V2  \\\n",
              "517921 -0.615548 -0.859680  0.374870  0.036459  4.399439 -0.974732  0.526645   \n",
              "540287 -0.615548 -0.859680  0.374870  0.830487 -0.250660  0.394588  0.526645   \n",
              "244957  0.085015 -0.238101  0.374870  0.089394 -0.250660  0.761362 -0.079947   \n",
              "374229  0.085015  1.005057  0.374870  0.142329 -0.250660  0.716529  0.093365   \n",
              "471795 -0.265267 -0.238101  0.374870 -0.016476 -0.250660  0.718332  0.526645   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "110268  2.186701  1.005057 -2.667592  1.942127 -0.250660 -0.666308  0.526645   \n",
              "259178  0.085015 -0.238101  0.374870  0.248200 -0.250660  0.877838  0.786613   \n",
              "365838  0.085015 -0.859680  0.374870 -1.075181 -0.250660  0.456318  0.266677   \n",
              "131932 -0.265267 -0.238101  0.374870  0.512876 -0.250660  0.542605  0.093365   \n",
              "121958 -0.615548 -0.859680  0.374870 -1.710404 -0.250660 -0.680051  0.266677   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "517921 -1.068274 -0.291982 -0.468227 -1.592893  0.0 -1.102174  0.0 -0.251094   \n",
              "540287  1.455078 -0.244269 -0.468227  0.220506  0.0  0.103160  0.0  0.553032   \n",
              "244957 -0.260667 -0.291982 -0.468227  0.743011  0.0  1.681367  0.0  0.553032   \n",
              "374229 -0.210401 -0.291982 -0.468227  0.662128  0.0  0.189874  0.0  0.553032   \n",
              "471795 -1.118540 -0.291982 -0.468227  0.655657  0.0 -1.102174  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "110268  0.597205 -0.220759 -0.468227 -1.372891  0.0 -1.102174  0.0  0.553032   \n",
              "259178  1.455078 -0.220759 -0.468227  0.872424  0.0 -1.102174  0.0  0.553032   \n",
              "365838 -1.118540 -0.315493 -0.468227  0.303007  0.0  0.814220  0.0  0.553032   \n",
              "131932  0.597205 -0.220759 -0.468227  0.419479  0.0  0.554076  0.0  0.553032   \n",
              "121958  1.455078 -0.078312 -0.468227  0.338596  0.0  0.285260  0.0 -0.251094   \n",
              "\n",
              "             V11  IsAlert  \n",
              "517921  0.503847        1  \n",
              "540287 -0.006108        0  \n",
              "244957  0.270053        1  \n",
              "374229 -0.435234        0  \n",
              "471795  0.063448        1  \n",
              "...          ...      ...  \n",
              "110268 -0.443651        0  \n",
              "259178  0.362862        1  \n",
              "365838 -0.422174        0  \n",
              "131932 -0.305589        0  \n",
              "121958  0.410837        1  \n",
              "\n",
              "[483463 rows x 24 columns]"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainingSet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ea04155",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "3ea04155",
        "outputId": "90dc3578-18aa-48dd-bc3a-2534c81f8094",
        "scrolled": false
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e6b07a89-5bda-45bc-bc1d-13be7ebef4de\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>IsAlert</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>551187</th>\n",
              "      <td>0.262964</td>\n",
              "      <td>1.551468</td>\n",
              "      <td>0.548411</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.004090</td>\n",
              "      <td>1.216253</td>\n",
              "      <td>0.435296</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.142329</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.704589</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.705805</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.831563</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.063343</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155822</th>\n",
              "      <td>-0.409571</td>\n",
              "      <td>-0.742066</td>\n",
              "      <td>-0.652994</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>0.870864</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>0.383478</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.651699</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.149536</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.706130</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>-0.199969</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186872</th>\n",
              "      <td>-0.306104</td>\n",
              "      <td>1.284048</td>\n",
              "      <td>0.935568</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.556763</td>\n",
              "      <td>-0.250170</td>\n",
              "      <td>-0.061122</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.301135</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.509487</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>0.647471</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.443744</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.729301</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.145542</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66220</th>\n",
              "      <td>0.159497</td>\n",
              "      <td>1.538229</td>\n",
              "      <td>0.526251</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.058774</td>\n",
              "      <td>-0.791050</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.016476</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.771275</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.728452</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.408457</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.238162</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541241</th>\n",
              "      <td>0.857899</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.386882</td>\n",
              "      <td>1.909685</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.248200</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.699182</td>\n",
              "      <td>-0.339916</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.642716</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571419</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.082187</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70728</th>\n",
              "      <td>0.185364</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.288103</td>\n",
              "      <td>-0.222828</td>\n",
              "      <td>-0.718057</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.459941</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.958943</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.940366</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.503843</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>-0.268538</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438945</th>\n",
              "      <td>-0.991572</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-1.120008</td>\n",
              "      <td>1.636448</td>\n",
              "      <td>-1.849446</td>\n",
              "      <td>0.435296</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.936358</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.667867</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>0.597205</td>\n",
              "      <td>-0.267780</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.584480</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.408457</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.166816</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557129</th>\n",
              "      <td>-0.461304</td>\n",
              "      <td>1.577306</td>\n",
              "      <td>0.209738</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>-0.331465</td>\n",
              "      <td>0.788837</td>\n",
              "      <td>0.632310</td>\n",
              "      <td>0.085015</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.301135</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.925600</td>\n",
              "      <td>-1.293133</td>\n",
              "      <td>1.505344</td>\n",
              "      <td>-0.291982</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>1.115073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458690</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.022510</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449348</th>\n",
              "      <td>-1.107972</td>\n",
              "      <td>-0.747191</td>\n",
              "      <td>1.521092</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.113459</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>-0.916375</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.599884</td>\n",
              "      <td>-0.260667</td>\n",
              "      <td>1.486532</td>\n",
              "      <td>2.135718</td>\n",
              "      <td>-1.725542</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.251094</td>\n",
              "      <td>0.141309</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493164</th>\n",
              "      <td>-0.396637</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.195486</td>\n",
              "      <td>0.303842</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.459941</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.741718</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.386205</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120866 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6b07a89-5bda-45bc-bc1d-13be7ebef4de')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e6b07a89-5bda-45bc-bc1d-13be7ebef4de button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e6b07a89-5bda-45bc-bc1d-13be7ebef4de');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "551187  0.262964  1.551468  0.548411 -0.288786  0.344428 -0.004090  1.216253   \n",
              "155822 -0.409571 -0.742066 -0.652994 -0.288786  0.119130  0.870864  0.267346   \n",
              "186872 -0.306104  1.284048  0.935568 -0.288786 -0.556763 -0.250170 -0.061122   \n",
              "66220   0.159497  1.538229  0.526251 -0.288786  0.231779 -0.058774 -0.791050   \n",
              "541241  0.857899 -0.748259 -0.807726 -0.288786  0.119130 -0.386882  1.909685   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "70728   0.185364 -0.748259 -0.807726 -0.288786  0.288103 -0.222828 -0.718057   \n",
              "438945 -0.991572 -0.748259 -0.807726 -0.288786 -1.120008  1.636448 -1.849446   \n",
              "557129 -0.461304  1.577306  0.209738 -0.288786 -0.331465  0.788837  0.632310   \n",
              "449348 -1.107972 -0.747191  1.521092 -0.288786  0.119130 -0.113459  0.303842   \n",
              "493164 -0.396637 -0.748259 -0.807726 -0.288786  0.119130 -0.195486  0.303842   \n",
              "\n",
              "              E7        E8       E9       E10      E11        V1        V2  \\\n",
              "551187  0.435296  0.383478  0.37487  0.142329 -0.25066  0.704589 -0.079947   \n",
              "155822 -0.615548  0.383478  0.37487 -0.651699 -0.25066 -1.733963 -0.599884   \n",
              "186872 -0.265267 -0.238101  0.37487  0.301135 -0.25066  0.509487 -0.599884   \n",
              "66220  -0.265267 -0.238101  0.37487 -0.016476 -0.25066  0.771275  0.266677   \n",
              "541241 -0.615548 -0.859680  0.37487  0.248200 -0.25066  0.699182 -0.339916   \n",
              "...          ...       ...      ...       ...      ...       ...       ...   \n",
              "70728  -0.265267 -0.238101  0.37487  0.459941 -0.25066  0.958943  0.093365   \n",
              "438945  0.435296 -0.238101  0.37487  0.936358 -0.25066  0.667867 -0.079947   \n",
              "557129  0.085015 -0.859680  0.37487  0.301135 -0.25066  0.925600 -1.293133   \n",
              "449348 -0.615548 -0.859680  0.37487 -0.916375 -0.25066 -1.733963 -0.599884   \n",
              "493164 -0.615548 -0.859680  0.37487  0.459941 -0.25066 -1.733963 -0.079947   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "551187 -1.068274 -0.291982 -0.468227  0.705805  0.0  0.831563  0.0  0.553032   \n",
              "155822 -1.118540 -0.149536 -0.468227 -1.706130  0.0 -1.102174  0.0 -1.859348   \n",
              "186872  0.647471 -0.267780 -0.468227  0.443744  0.0 -0.729301  0.0  0.553032   \n",
              "66220  -0.260667 -0.267780 -0.468227  0.728452  0.0 -0.408457  0.0  0.553032   \n",
              "541241  1.505344 -0.315493 -0.468227  0.642716  0.0  0.571419  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "70728  -0.260667 -0.244269 -0.468227  0.940366  0.0 -0.503843  0.0  0.553032   \n",
              "438945  0.597205 -0.267780 -0.468227  0.584480  0.0 -0.408457  0.0  0.553032   \n",
              "557129  1.505344 -0.291982  2.135718  1.115073  0.0  0.458690  0.0  0.553032   \n",
              "449348 -0.260667  1.486532  2.135718 -1.725542  0.0 -1.102174  0.0 -0.251094   \n",
              "493164 -1.068274 -0.315493 -0.468227 -1.741718  0.0 -1.102174  0.0 -1.859348   \n",
              "\n",
              "             V11  IsAlert  \n",
              "551187 -0.063343        0  \n",
              "155822 -0.199969        1  \n",
              "186872 -0.145542        1  \n",
              "66220  -0.238162        1  \n",
              "541241 -0.082187        0  \n",
              "...          ...      ...  \n",
              "70728  -0.268538        1  \n",
              "438945  0.166816        1  \n",
              "557129  0.022510        0  \n",
              "449348  0.141309        1  \n",
              "493164  0.386205        1  \n",
              "\n",
              "[120866 rows x 24 columns]"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "validatingSet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e557b2ae",
      "metadata": {
        "id": "e557b2ae"
      },
      "outputs": [],
      "source": [
        "X_train_log_reg = trainingSet[numeric_feature_cols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfe48834",
      "metadata": {
        "id": "cfe48834"
      },
      "outputs": [],
      "source": [
        "y_train_log_reg = trainingSet['IsAlert']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e896757c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e896757c",
        "outputId": "b2bd93bf-8ebf-4279-8263-c7acb0b0a973"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((483463, 23), (483463,))"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_log_reg.shape, y_train_log_reg.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cedc8eff",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "cedc8eff",
        "outputId": "da54e900-998b-4d88-ed20-6e58e3ad9e0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 130 ms, sys: 143 ms, total: 274 ms\n",
            "Wall time: 3.18 s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(n_jobs=-1)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression(n_jobs=-1)"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "# Lets train our logistic regression model using fit() method\n",
        "log_reg.fit(X_train_log_reg,y_train_log_reg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a9cef6d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7a9cef6d",
        "outputId": "3b09e762-8354-4c31-a861-12983d9b0ffd",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[ 2.91032842e-04,  6.42172153e-02, -1.00488959e-01,\n",
              "         1.67310936e-01,  2.35467270e-03, -1.82015296e-01,\n",
              "        -4.08708105e-01, -1.35190350e-01, -3.37463892e-01,\n",
              "         1.05763279e+00,  1.36795793e-01, -1.04627128e-02,\n",
              "        -6.97104932e-01, -7.41228054e-03, -1.26556747e-01,\n",
              "        -2.68511432e-02, -2.31355911e-02,  2.58617315e-01,\n",
              "         0.00000000e+00, -7.29493506e-02,  0.00000000e+00,\n",
              "        -1.37860773e-01,  2.09789372e+00]])"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "log_reg.coef_"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5ee77f67",
      "metadata": {
        "id": "5ee77f67"
      },
      "source": [
        "Here, each value in the above list is a weight that is applied to the respective column in the training set. Similarly, lets check the intercept."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a59d52ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a59d52ac",
        "outputId": "f71175a5-7a39-4d7d-b96e-99d87ed84387"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.38792004])"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "log_reg.intercept_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c57e921",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c57e921",
        "outputId": "364f0d33-aae4-4d8e-da94-be2dace0afe8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 36 ms, sys: 8.99 ms, total: 45 ms\n",
            "Wall time: 33.5 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# Lets make predictions on the training set\n",
        "training_pred = log_reg.predict(X_train_log_reg)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5f2c522",
      "metadata": {
        "id": "f5f2c522"
      },
      "source": [
        "<b> Lets measure the accuracy for training set. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1d914b0",
      "metadata": {
        "id": "f1d914b0"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "training_accuracy = accuracy_score(y_train_log_reg, training_pred) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7312b06a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7312b06a",
        "outputId": "28505f0d-8f4f-46a3-c6e3-5e497962d60b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "79.06396146137347"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47ab3690",
      "metadata": {
        "id": "47ab3690"
      },
      "source": [
        "<b> Lets measure the accuracy for validation set. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d7c6a572",
      "metadata": {
        "id": "d7c6a572"
      },
      "outputs": [],
      "source": [
        "X_train_vali = validatingSet[numeric_feature_cols]\n",
        "y_train_vali = validatingSet['IsAlert']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c1912c0e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c1912c0e",
        "outputId": "8337d312-1849-40ad-c0ae-6188d9fd7cd4",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((120866, 23), (120866,))"
            ]
          },
          "execution_count": 89,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_train_vali.shape,y_train_vali.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ce61e0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ce61e0f",
        "outputId": "04c1e789-398e-4598-fac9-5e0c89bec657"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 29.1 ms, sys: 16.9 ms, total: 45.9 ms\n",
            "Wall time: 31.8 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "validating_accuracy = accuracy_score(y_train_vali, log_reg.predict(X_train_vali))*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3da37154",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3da37154",
        "outputId": "30823a64-dc76-4ee2-bafa-69c04b7c9f58"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "78.92293945360979"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "validating_accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59a87c76",
      "metadata": {
        "id": "59a87c76"
      },
      "source": [
        "Our training accuracy is 79.1 % whereas our validation accuracy is 78.95 %. Thus, we can make a conclusion that our model has not overfitted with the training set."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83ebdbd1",
      "metadata": {
        "id": "83ebdbd1"
      },
      "source": [
        "## Hyperparameter Tuning for the Logistic Regression model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47d753e3",
      "metadata": {
        "id": "47d753e3"
      },
      "source": [
        "### Lets tune the hyperparameter `max_iter`"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99d1eee2",
      "metadata": {
        "id": "99d1eee2"
      },
      "source": [
        "By default, the hyperparameter max_iter is set to 100 for our logistic regression model. So, lets check our models performance for both training and validation set by tuning max_iter hyperparameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b8c4a6a7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8c4a6a7",
        "outputId": "8a3ee60a-755d-4fe0-deb7-96d01bbc3220",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "When max_iter is 50, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 75, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 100, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 150, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 200, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 250, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 300, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 350, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 400, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 450, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 500, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 750, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "When max_iter is 1000, the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979.\n",
            "CPU times: user 3.28 s, sys: 2.88 s, total: 6.16 s\n",
            "Wall time: 38.9 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "training_accuracy_maxIterTuned = []\n",
        "validation_accuracy_maxIterTuned = []\n",
        "\n",
        "for i in [50,75,100,150,200,250,300,350,400,450,500,750,1000]:\n",
        "    log_reg = LogisticRegression(max_iter = i, n_jobs = -1)\n",
        "    log_reg.fit(X_train_log_reg,y_train_log_reg)\n",
        "    training_accuracy = accuracy_score(y_train_log_reg, log_reg.predict(X_train_log_reg))*100\n",
        "    validating_accuracy = accuracy_score(y_train_vali, log_reg.predict(X_train_vali))*100\n",
        "\n",
        "    training_accuracy_maxIterTuned.append(training_accuracy)\n",
        "    validation_accuracy_maxIterTuned.append(validating_accuracy)\n",
        "\n",
        "    print(\"When max_iter is {}, the training accuracy is {} and validation accuracy is {}.\".format(i, training_accuracy, validating_accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc96bc84",
      "metadata": {
        "id": "cc96bc84"
      },
      "source": [
        "<b> Visually representing the above calculations. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e7b24b25",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "e7b24b25",
        "outputId": "a7acd399-7abd-4e50-b7c6-7e808fb512bb"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plotting validation Scores and Training Scores V/S values of max_iter Hyperparameter\n",
        "plt.plot([50,75,100,150,200,250,300,350,400,450,500,750,1000], training_accuracy_maxIterTuned, color = 'red', marker = 'o')\n",
        "plt.plot([50,75,100,150,200,250,300,350,400,450,500,750,1000], validation_accuracy_maxIterTuned, color = 'blue', marker = 'x')\n",
        "plt.xlabel('Values of max_iter Hyperparameter')\n",
        "plt.ylabel('Predicted Training and Validation Accuracy')\n",
        "plt.title('validation Scores and Training Scores V/S values of max_iter Hyperparameter')\n",
        "plt.legend(['Training Accuracy', 'Validation Accuracy'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ef9609e",
      "metadata": {
        "id": "9ef9609e"
      },
      "source": [
        "Well, we can see that the change in hyperparameter max_iter doesnot result in change in the predictions. So, we will let the original value of max_iter be used in our model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35e65f6e",
      "metadata": {
        "id": "35e65f6e"
      },
      "source": [
        "### Lets tune the hyperparameter `solver`"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "032c6ad8",
      "metadata": {
        "id": "032c6ad8"
      },
      "source": [
        "By default, our logistic regression model has the value of max_iter parameter set to `lbfgs`. But, we will check our model performance on both training and validation set by tuning the values of max_iter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3282c06",
      "metadata": {
        "id": "b3282c06"
      },
      "outputs": [],
      "source": [
        "# Hence, this is our optimized model\n",
        "log_reg = LogisticRegression(n_jobs = -1, max_iter = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e3cac45",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e3cac45",
        "outputId": "06633d8f-0b7f-4999-8ed0-0a2518c61e0e",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "When solver is newton-cg the training accuracy is 79.06396146137347 and validation accuracy is 78.92459417867721 .\n",
            "When solver is lbfgs the training accuracy is 79.06396146137347 and validation accuracy is 78.92293945360979 .\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:1211: UserWarning: 'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 2.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "When solver is liblinear the training accuracy is 79.06354777925094 and validation accuracy is 78.92211209107607 .\n",
            "When solver is sag the training accuracy is 79.06561618986355 and validation accuracy is 78.92211209107607 .\n",
            "When solver is saga the training accuracy is 79.06209989182213 and validation accuracy is 78.92542154121092 .\n",
            "CPU times: user 16.8 s, sys: 1.03 s, total: 17.9 s\n",
            "Wall time: 27.2 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "solver_list = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n",
        "training_accuracy_maxIterTuned = []\n",
        "validation_accuracy_maxIterTuned = []\n",
        "\n",
        "for i in solver_list:\n",
        "    log_reg = LogisticRegression(n_jobs = -1, max_iter = 100, solver = i)\n",
        "    log_reg.fit(X_train_log_reg,y_train_log_reg)\n",
        "    training_accuracy = accuracy_score(y_train_log_reg, log_reg.predict(X_train_log_reg))*100\n",
        "    validating_accuracy = accuracy_score(y_train_vali, log_reg.predict(X_train_vali))*100\n",
        "\n",
        "    training_accuracy_maxIterTuned.append(training_accuracy)\n",
        "    validation_accuracy_maxIterTuned.append(validating_accuracy)\n",
        "\n",
        "    print(\"When solver is \"+i+\" the training accuracy is \"+str(training_accuracy)+\" and validation accuracy is \"+str(validating_accuracy)+\" .\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f201530b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f201530b",
        "outputId": "4d209a1b-dac6-41c4-92f3-1634ce1a9078"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The training accuracy is maximum when solver = sag\n"
          ]
        }
      ],
      "source": [
        "print('The training accuracy is maximum when solver = '+solver_list[training_accuracy_maxIterTuned.index(max(training_accuracy_maxIterTuned))])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2ff0133",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2ff0133",
        "outputId": "1290101d-8f00-43c6-9128-724cb73ada90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The Validation accuracy is maximum when solver = saga\n"
          ]
        }
      ],
      "source": [
        "print('The Validation accuracy is maximum when solver = '+solver_list[validation_accuracy_maxIterTuned.index(max(validation_accuracy_maxIterTuned))])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b6e2a5df",
      "metadata": {
        "id": "b6e2a5df"
      },
      "outputs": [],
      "source": [
        "# Hence, our new optimized model is given below\n",
        "log_reg = LogisticRegression(n_jobs = -1, solver = 'newton-cg', max_iter = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "520c6415",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "520c6415",
        "outputId": "987caf9a-a000-4980-a7b9-c6c510cdcbf6",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] jovian.commit() is no longer required on Google Colab. If you ran this notebook from Jovian, \n",
            "then just save this file in Colab using Ctrl+S/Cmd+S and it will be updated on Jovian. \n",
            "Also, you can also delete this cell, it's no longer necessary.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "jovian.commit()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "80d76192",
      "metadata": {
        "id": "80d76192"
      },
      "source": [
        "### Lets tune the hyperparameter `C`"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "959d1704",
      "metadata": {
        "id": "959d1704"
      },
      "source": [
        "The C parameter controls the penality strength. Our model can also be effective by tuning this hyperparameter. By default, our logistic regression model has value of C set to 1.0. So, lets tune it with different values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "35133919",
      "metadata": {
        "id": "35133919"
      },
      "outputs": [],
      "source": [
        "# Hence, our new optimized model is given below\n",
        "log_reg = LogisticRegression(n_jobs = -1, solver = 'newton-cg', max_iter = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "55822f01",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55822f01",
        "outputId": "1db8ac27-340b-43b2-a9a5-45e989ab83fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "When value of C is 100, the training accuracy is 79.06375462031221 and validation accuracy is 78.92459417867721.\n",
            "When value of C is 10, the training accuracy is 79.06416830243472 and validation accuracy is 78.92459417867721.\n",
            "When value of C is 1.0, the training accuracy is 79.06396146137347 and validation accuracy is 78.92459417867721.\n",
            "When value of C is 0.1, the training accuracy is 79.06727091835363 and validation accuracy is 78.91963000347492.\n",
            "When value of C is 0.01, the training accuracy is 79.09416025631745 and validation accuracy is 78.96927175549783.\n",
            "CPU times: user 1.39 s, sys: 1.09 s, total: 2.48 s\n",
            "Wall time: 43 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "C_list =  [100, 10, 1.0, 0.1, 0.01]\n",
        "training_accuracy_maxIterTuned = []\n",
        "validation_accuracy_maxIterTuned = []\n",
        "\n",
        "for i in C_list:\n",
        "    log_reg = LogisticRegression(n_jobs = -1, solver = 'newton-cg', max_iter = 100, C = i)\n",
        "    log_reg.fit(X_train_log_reg,y_train_log_reg)\n",
        "    training_accuracy = accuracy_score(y_train_log_reg, log_reg.predict(X_train_log_reg))*100\n",
        "    validating_accuracy = accuracy_score(y_train_vali, log_reg.predict(X_train_vali))*100\n",
        "\n",
        "    training_accuracy_maxIterTuned.append(training_accuracy)\n",
        "    validation_accuracy_maxIterTuned.append(validating_accuracy)\n",
        "\n",
        "    print(\"When value of C is {}, the training accuracy is {} and validation accuracy is {}.\".format(i, training_accuracy, validating_accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cc197793",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc197793",
        "outputId": "e2be6f9b-5971-4766-ce16-2fe772d247ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The training accuracy is maximum when value of C = 0.01\n"
          ]
        }
      ],
      "source": [
        "print('The training accuracy is maximum when value of C = '+str(C_list[training_accuracy_maxIterTuned.index(max(training_accuracy_maxIterTuned))]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6d3e3172",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6d3e3172",
        "outputId": "0e1e21b0-e281-4695-f781-e43ba2fc340f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The Validation accuracy is maximum when value of C = 0.01\n"
          ]
        }
      ],
      "source": [
        "print('The Validation accuracy is maximum when value of C = '+str(C_list[validation_accuracy_maxIterTuned.index(max(validation_accuracy_maxIterTuned))]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd3c6ad4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "fd3c6ad4",
        "outputId": "9131d45d-2d98-4fdd-d2e9-8dc046628089"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, n_jobs=-1, solver=&#x27;newton-cg&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, n_jobs=-1, solver=&#x27;newton-cg&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression(C=0.01, n_jobs=-1, solver='newton-cg')"
            ]
          },
          "execution_count": 104,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Thus, Our final optimized/ Tuned model is given below\n",
        "log_reg = LogisticRegression(n_jobs = -1, max_iter = 100, C = 0.01, solver = 'newton-cg')\n",
        "log_reg.fit(X_train_log_reg,y_train_log_reg)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bafe6cb6",
      "metadata": {
        "id": "bafe6cb6"
      },
      "source": [
        "## Lets Measure Accuracy on Training and Validation Set One Last Time Using Optimized Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85cc5475",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85cc5475",
        "outputId": "57a97917-a2cb-42b3-8bb3-f4b793de3169"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Accuracy With Optimized Model:  79.094\n"
          ]
        }
      ],
      "source": [
        "print('Training Accuracy With Optimized Model: ', round(accuracy_score(y_train_log_reg, log_reg.predict(X_train_log_reg))*100,3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2edd13fa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2edd13fa",
        "outputId": "0a8c7076-3832-4e43-b3cd-6706f7e80616"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Validation Accuracy With Optimized Model:  78.969\n"
          ]
        }
      ],
      "source": [
        "print('Validation Accuracy With Optimized Model: ', round(accuracy_score(y_train_vali, log_reg.predict(X_train_vali))*100,3))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0cfdd8b0",
      "metadata": {
        "id": "0cfdd8b0"
      },
      "source": [
        "# Predictions on the `Testing Set` with optimized logistic regression model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "761b109e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "761b109e",
        "outputId": "a3226c53-6541-48c6-92ca-e67a2d3a80aa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8d02185f-5733-4ed8-8522-e8722d216c37\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.359539</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.712024</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.665363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.455530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.231779</td>\n",
              "      <td>-0.359539</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.036459</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.712024</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.665363</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.453587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.713826</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.671834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.452158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.713826</td>\n",
              "      <td>0.266677</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.671834</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.453396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.086237</td>\n",
              "      <td>-0.748259</td>\n",
              "      <td>-0.807726</td>\n",
              "      <td>3.686720</td>\n",
              "      <td>0.344428</td>\n",
              "      <td>-0.140801</td>\n",
              "      <td>-1.119518</td>\n",
              "      <td>-0.265267</td>\n",
              "      <td>-0.238101</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.089394</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>0.712024</td>\n",
              "      <td>0.093365</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.244269</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>0.679922</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.424004</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.553032</td>\n",
              "      <td>0.450718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120835</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.710983</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.504984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120836</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.712600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.504884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120837</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.068274</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.715836</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.501330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120838</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.712600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.499589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120839</th>\n",
              "      <td>-0.435437</td>\n",
              "      <td>-0.743419</td>\n",
              "      <td>0.874149</td>\n",
              "      <td>-0.288786</td>\n",
              "      <td>0.119130</td>\n",
              "      <td>-0.168143</td>\n",
              "      <td>0.267346</td>\n",
              "      <td>-0.615548</td>\n",
              "      <td>-0.859680</td>\n",
              "      <td>0.37487</td>\n",
              "      <td>0.407005</td>\n",
              "      <td>-0.25066</td>\n",
              "      <td>-1.733963</td>\n",
              "      <td>-0.079947</td>\n",
              "      <td>-1.118540</td>\n",
              "      <td>-0.315493</td>\n",
              "      <td>-0.468227</td>\n",
              "      <td>-1.712600</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.102174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.859348</td>\n",
              "      <td>0.499589</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120840 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d02185f-5733-4ed8-8522-e8722d216c37')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d02185f-5733-4ed8-8522-e8722d216c37 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d02185f-5733-4ed8-8522-e8722d216c37');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              P3        E1        E2        E3        E4        E5        E6  \\\n",
              "0      -0.086237 -0.748259 -0.807726  3.686720  0.231779 -0.359539 -1.119518   \n",
              "1      -0.086237 -0.748259 -0.807726  3.686720  0.231779 -0.359539 -1.119518   \n",
              "2      -0.086237 -0.748259 -0.807726  3.686720  0.344428 -0.140801 -1.119518   \n",
              "3      -0.086237 -0.748259 -0.807726  3.686720  0.344428 -0.140801 -1.119518   \n",
              "4      -0.086237 -0.748259 -0.807726  3.686720  0.344428 -0.140801 -1.119518   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "120835 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120836 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120837 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120838 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "120839 -0.435437 -0.743419  0.874149 -0.288786  0.119130 -0.168143  0.267346   \n",
              "\n",
              "              E7        E8       E9       E10      E11        V1        V2  \\\n",
              "0      -0.265267 -0.238101  0.37487  0.036459 -0.25066  0.712024  0.093365   \n",
              "1      -0.265267 -0.238101  0.37487  0.036459 -0.25066  0.712024  0.093365   \n",
              "2      -0.265267 -0.238101  0.37487  0.089394 -0.25066  0.713826  0.266677   \n",
              "3      -0.265267 -0.238101  0.37487  0.089394 -0.25066  0.713826  0.266677   \n",
              "4      -0.265267 -0.238101  0.37487  0.089394 -0.25066  0.712024  0.093365   \n",
              "...          ...       ...      ...       ...      ...       ...       ...   \n",
              "120835 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120836 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120837 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120838 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "120839 -0.615548 -0.859680  0.37487  0.407005 -0.25066 -1.733963 -0.079947   \n",
              "\n",
              "              V3        V4        V5        V6   V7        V8   V9       V10  \\\n",
              "0      -1.068274 -0.244269 -0.468227  0.665363  0.0  0.424004  0.0  0.553032   \n",
              "1      -1.068274 -0.244269 -0.468227  0.665363  0.0  0.424004  0.0  0.553032   \n",
              "2      -1.068274 -0.244269 -0.468227  0.671834  0.0  0.424004  0.0  0.553032   \n",
              "3      -1.068274 -0.244269 -0.468227  0.671834  0.0  0.424004  0.0  0.553032   \n",
              "4      -1.068274 -0.244269 -0.468227  0.679922  0.0  0.424004  0.0  0.553032   \n",
              "...          ...       ...       ...       ...  ...       ...  ...       ...   \n",
              "120835 -1.068274 -0.315493 -0.468227 -1.710983  0.0 -1.102174  0.0 -1.859348   \n",
              "120836 -1.068274 -0.315493 -0.468227 -1.712600  0.0 -1.102174  0.0 -1.859348   \n",
              "120837 -1.068274 -0.315493 -0.468227 -1.715836  0.0 -1.102174  0.0 -1.859348   \n",
              "120838 -1.118540 -0.315493 -0.468227 -1.712600  0.0 -1.102174  0.0 -1.859348   \n",
              "120839 -1.118540 -0.315493 -0.468227 -1.712600  0.0 -1.102174  0.0 -1.859348   \n",
              "\n",
              "             V11  \n",
              "0       0.455530  \n",
              "1       0.453587  \n",
              "2       0.452158  \n",
              "3       0.453396  \n",
              "4       0.450718  \n",
              "...          ...  \n",
              "120835  0.504984  \n",
              "120836  0.504884  \n",
              "120837  0.501330  \n",
              "120838  0.499589  \n",
              "120839  0.499589  \n",
              "\n",
              "[120840 rows x 23 columns]"
            ]
          },
          "execution_count": 107,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#  our testing set\n",
        "test_new_df[numeric_feature_cols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1432848a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "1432848a",
        "outputId": "4fe45729-bf63-4816-a774-7e81daa73f8f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, n_jobs=-1, solver=&#x27;newton-cg&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, n_jobs=-1, solver=&#x27;newton-cg&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression(C=0.01, n_jobs=-1, solver='newton-cg')"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Thus, Our final optimized/ Tuned model is given below\n",
        "log_reg_tuned = LogisticRegression(n_jobs = -1, max_iter = 100, C = 0.01, solver = 'newton-cg')\n",
        "log_reg_tuned.fit(X_train_log_reg,y_train_log_reg)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82a35b03",
      "metadata": {
        "id": "82a35b03"
      },
      "source": [
        "<b> Lets use the above model to make predictions on our testing set. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e13030c1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e13030c1",
        "outputId": "20688afd-bfd3-48a2-f26d-d647bca98a0f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 14.6 ms, sys: 8.91 ms, total: 23.5 ms\n",
            "Wall time: 21.3 ms\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "testingSet = test_new_df[numeric_feature_cols]\n",
        "testing_set_predictions = log_reg_tuned.predict(testingSet)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1d6b311e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1d6b311e",
        "outputId": "88b11aa8-2e1c-49c3-bd58-1c7375d35038"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "testing_set_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "994fa931",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "994fa931",
        "outputId": "7ba9e458-d495-4a69-e3f0-6ed938ee698f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1345281b-74c2-4c62-b34c-7f42e4e86044\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TrialID</th>\n",
              "      <th>ObsNum</th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Public</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Public</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120835</th>\n",
              "      <td>99</td>\n",
              "      <td>1206</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120836</th>\n",
              "      <td>99</td>\n",
              "      <td>1207</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120837</th>\n",
              "      <td>99</td>\n",
              "      <td>1208</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120838</th>\n",
              "      <td>99</td>\n",
              "      <td>1209</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120839</th>\n",
              "      <td>99</td>\n",
              "      <td>1210</td>\n",
              "      <td>1</td>\n",
              "      <td>Private</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120840 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1345281b-74c2-4c62-b34c-7f42e4e86044')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1345281b-74c2-4c62-b34c-7f42e4e86044 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1345281b-74c2-4c62-b34c-7f42e4e86044');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        TrialID  ObsNum  Prediction Indicator\n",
              "0             0       0           1    Public\n",
              "1             0       1           1    Public\n",
              "2             0       2           1   Private\n",
              "3             0       3           1   Private\n",
              "4             0       4           1   Private\n",
              "...         ...     ...         ...       ...\n",
              "120835       99    1206           1   Private\n",
              "120836       99    1207           1   Private\n",
              "120837       99    1208           1   Private\n",
              "120838       99    1209           1   Private\n",
              "120839       99    1210           1   Private\n",
              "\n",
              "[120840 rows x 4 columns]"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "solution_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d16478c",
      "metadata": {
        "id": "2d16478c"
      },
      "outputs": [],
      "source": [
        "testing_accuracy = round(accuracy_score(solution_df['Prediction'], testing_set_predictions)*100, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a8378673",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8378673",
        "outputId": "9fcbec05-3326-4bbf-e3d7-62ede021c1a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Testing Accuracy:  84.925\n"
          ]
        }
      ],
      "source": [
        "print(\"Testing Accuracy: \", testing_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90e8e159",
      "metadata": {
        "id": "90e8e159"
      },
      "source": [
        "<b> Our testing accuracy is calculated to be 84.925 ,which is extremely better than the Knn model which has testing accuracy at best 75 % approximately. An increment of 10 % additional accuracy proves that this model is much better at measuring accuracy for this project to detect or classify whether the driver is alert or not. </b>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22b6c702",
      "metadata": {
        "id": "22b6c702"
      },
      "outputs": [],
      "source": [
        "# lets create a csv file and save our prediction for this model to be uploaded in kaggle\n",
        "solution_log_reg_df = solution_df.copy()\n",
        "solution_log_reg_df['Prediction'] = testing_set_predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "819c0dc3",
      "metadata": {
        "id": "819c0dc3"
      },
      "outputs": [],
      "source": [
        "# saving out predictions to a new file called 'solution_log_reg.csv'\n",
        "solution_log_reg_df.to_csv('solution_log_reg.csv', index = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cf667386",
      "metadata": {
        "id": "cf667386"
      },
      "source": [
        "# Making a sample Prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5e9641c8",
      "metadata": {
        "id": "5e9641c8"
      },
      "outputs": [],
      "source": [
        "# Lets create a sample dictionary\n",
        "new_input = {'P3': 23,\n",
        "             'E1': 400,\n",
        "             'E2': 3.5,\n",
        "             'E3': 600,\n",
        "             'E4': 340,\n",
        "             'E5': 542,\n",
        "             'E6': 50,\n",
        "             'E7': 878.5,\n",
        "             'E8': 52.0,\n",
        "             'E9': 2400,\n",
        "             'E10': 3000,\n",
        "             'E11': 13.0,\n",
        "             'V1': 20.0,\n",
        "             'V2': 89.0,\n",
        "             'V3': 58.0,\n",
        "             'V4': 1004.8,\n",
        "             'V5': 1001.5,\n",
        "             'V6': 8.0,\n",
        "             'V7': 5.0,\n",
        "             'V8': 25.7,\n",
        "             'V9': 33.0,\n",
        "             'V10': 290,\n",
        "             'V11': 300}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "868a053e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "868a053e",
        "outputId": "f7d6dc71-99ec-4802-d5ea-e71726af6860",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f7c1c671-a453-415b-93be-1555eb61df49\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>P3</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>E6</th>\n",
              "      <th>E7</th>\n",
              "      <th>E8</th>\n",
              "      <th>E9</th>\n",
              "      <th>E10</th>\n",
              "      <th>E11</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>400</td>\n",
              "      <td>3.5</td>\n",
              "      <td>600</td>\n",
              "      <td>340</td>\n",
              "      <td>542</td>\n",
              "      <td>50</td>\n",
              "      <td>878.5</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2400</td>\n",
              "      <td>3000</td>\n",
              "      <td>13.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>1004.8</td>\n",
              "      <td>1001.5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>25.7</td>\n",
              "      <td>33.0</td>\n",
              "      <td>290</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7c1c671-a453-415b-93be-1555eb61df49')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f7c1c671-a453-415b-93be-1555eb61df49 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f7c1c671-a453-415b-93be-1555eb61df49');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   P3   E1   E2   E3   E4   E5  E6     E7    E8    E9   E10   E11    V1    V2  \\\n",
              "0  23  400  3.5  600  340  542  50  878.5  52.0  2400  3000  13.0  20.0  89.0   \n",
              "\n",
              "     V3      V4      V5   V6   V7    V8    V9  V10  V11  \n",
              "0  58.0  1004.8  1001.5  8.0  5.0  25.7  33.0  290  300  "
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Converting the dictionary into a dataframe\n",
        "pd.DataFrame([new_input])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bbf07bd9",
      "metadata": {
        "id": "bbf07bd9"
      },
      "outputs": [],
      "source": [
        "# transforming / scaling the data from the dataframe\n",
        "X = scaler.transform(pd.DataFrame([new_input]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c08f637",
      "metadata": {
        "id": "6c08f637"
      },
      "outputs": [],
      "source": [
        "# Making predictions using our tuned model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "log_reg = LogisticRegression(n_jobs = -1, max_iter = 100, C = 0.01, solver = 'newton-cg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "441D2PXGDsMa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "441D2PXGDsMa",
        "outputId": "783d46e7-7751-4f3f-b49d-cd28b197d675"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, n_jobs=-1, solver=&#x27;newton-cg&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, n_jobs=-1, solver=&#x27;newton-cg&#x27;)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression(C=0.01, n_jobs=-1, solver='newton-cg')"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "log_reg.fit(X_train_log_reg,y_train_log_reg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d84234d5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d84234d5",
        "outputId": "0ff33b48-d306-42a2-8256-af50f750b24b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "l = ['Not Alert', 'Alert']\n",
        "prediction = log_reg.predict(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c058d036",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "c058d036",
        "outputId": "c838d8fe-021d-4f0c-a5ae-fe270da44b7e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Not Alert'"
            ]
          },
          "execution_count": 122,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "l[int(prediction)]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "866834e7",
      "metadata": {
        "id": "866834e7"
      },
      "source": [
        "<b> Thus, the prediction that the driver is `Not Alert` has been made by the help of our logistic regression model.</b>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad10534c",
      "metadata": {
        "id": "ad10534c"
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43bdb067",
      "metadata": {
        "id": "43bdb067"
      },
      "source": [
        "I would Like to thank the [jovian](jovian.ml) community and teachers for providing such a great machine learning course for free. Through their material, I learnt many things regarding data analysis, exploration, visualization, manipulation, and cleaning. I also learnt about different supervised learning algorithms. I have completed this project all by myself, without taking help from the solution found on the internet. I, however, have taken references of the syntax, methods, and hyperparameters from the previous jovian's course's notebooks and sklearn's official documentation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2c37a7ec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2c37a7ec",
        "outputId": "660ce51a-4390-466d-80c5-2d2e95f1a3a2",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] jovian.commit() is no longer required on Google Colab. If you ran this notebook from Jovian, \n",
            "then just save this file in Colab using Ctrl+S/Cmd+S and it will be updated on Jovian. \n",
            "Also, you can also delete this cell, it's no longer necessary.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "jovian.commit()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "271f94b5",
      "metadata": {
        "id": "271f94b5"
      },
      "source": [
        "# Submitting the file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f6b2fac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f6b2fac",
        "outputId": "9c7e02ca-4188-4d96-abc7-39ade9912a9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[jovian] Detected Colab notebook...\u001b[0m\n",
            "[jovian] jovian.commit() is no longer required on Google Colab. If you ran this notebook from Jovian, \n",
            "then just save this file in Colab using Ctrl+S/Cmd+S and it will be updated on Jovian. \n",
            "Also, you can also delete this cell, it's no longer necessary.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "jovian.submit(assignment=\"zerotogbms-project\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}