notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "zoomcamp_midterm_spotify.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "9NOChJP23ax9",
        "1VdklIF53k2M",
        "l2LFy8Py7MeC"
      ],
      "authorship_tag": "ABX9TyNZGC5jexpYO2p5GCHqeY1B",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashok-arora/ML-Zoomcamp/blob/main/mid_term_project/notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Al_jL6S5Kfo3"
      },
      "source": [
        "# Spotify Hit Predictor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34oLH7iDcvqT"
      },
      "source": [
        "**Dataset**: https://www.kaggle.com/theoverman/the-spotify-hit-predictor-dataset\n",
        "\n",
        "**Link to repository**: https://github.com/ashok-arora/ML-Zoomcamp/mid_term_project\n",
        "\n",
        "Note: The README.md inside the repository containes the problem statement and the detailed description of the approach.  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zRWrXaOid31u"
      },
      "source": [
        "## This notebook has been organized in the form of:\n",
        "\n",
        "code snippet\n",
        "\n",
        "---\n",
        "\n",
        "followed by the inference from the output of the code snippet. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5myUJd9tbOs-"
      },
      "source": [
        "## Importing the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAPtA_2McYjM"
      },
      "source": [
        "import glob\n",
        "import os\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import sklearn\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvNTXvZcuAXm"
      },
      "source": [
        "## Load Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLHxYnJCBeXy"
      },
      "source": [
        "# all_files = glob.glob(\"./*.csv\")\n",
        "# df = pd.concat((pd.read_csv(f) for f in all_files))\n",
        "# df.to_csv('./combined_dataset.csv', index=False)\n",
        "\n",
        "df = pd.read_csv('./combined_dataset.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "G7w7twVmElfz",
        "outputId": "31e13322-ce9e-4dc6-dc95-a1ae20bced9f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>track</th>\n",
              "      <th>artist</th>\n",
              "      <th>uri</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Misty Roses</td>\n",
              "      <td>Astrud Gilberto</td>\n",
              "      <td>spotify:track:50RBM1j1Dw7WYmsGsWg9Tm</td>\n",
              "      <td>0.527</td>\n",
              "      <td>0.316</td>\n",
              "      <td>1</td>\n",
              "      <td>-15.769</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0310</td>\n",
              "      <td>0.693000</td>\n",
              "      <td>0.00699</td>\n",
              "      <td>0.1680</td>\n",
              "      <td>0.543</td>\n",
              "      <td>116.211</td>\n",
              "      <td>158840</td>\n",
              "      <td>4</td>\n",
              "      <td>53.89523</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Never Ever</td>\n",
              "      <td>All Saints</td>\n",
              "      <td>spotify:track:5FTz9qQ94PyUHETyAyfYZN</td>\n",
              "      <td>0.738</td>\n",
              "      <td>0.541</td>\n",
              "      <td>1</td>\n",
              "      <td>-5.485</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0311</td>\n",
              "      <td>0.559000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0492</td>\n",
              "      <td>0.309</td>\n",
              "      <td>134.187</td>\n",
              "      <td>387573</td>\n",
              "      <td>4</td>\n",
              "      <td>32.16853</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Soul Sermon</td>\n",
              "      <td>Gregg Karukas</td>\n",
              "      <td>spotify:track:6m24oe3lk1UMxq9zq4iPFi</td>\n",
              "      <td>0.736</td>\n",
              "      <td>0.419</td>\n",
              "      <td>0</td>\n",
              "      <td>-10.662</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0300</td>\n",
              "      <td>0.693000</td>\n",
              "      <td>0.49500</td>\n",
              "      <td>0.0809</td>\n",
              "      <td>0.265</td>\n",
              "      <td>93.982</td>\n",
              "      <td>237267</td>\n",
              "      <td>4</td>\n",
              "      <td>42.05369</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Clarinet Marmalade - Live</td>\n",
              "      <td>Alton Purnell</td>\n",
              "      <td>spotify:track:5FOXuiLI6knVtgMUjWKj6x</td>\n",
              "      <td>0.565</td>\n",
              "      <td>0.594</td>\n",
              "      <td>5</td>\n",
              "      <td>-13.086</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0646</td>\n",
              "      <td>0.655000</td>\n",
              "      <td>0.92600</td>\n",
              "      <td>0.6750</td>\n",
              "      <td>0.763</td>\n",
              "      <td>114.219</td>\n",
              "      <td>375933</td>\n",
              "      <td>4</td>\n",
              "      <td>80.99693</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>До смерті і довше - Drum &amp; Base and Rock Remix</td>\n",
              "      <td>Skryabin</td>\n",
              "      <td>spotify:track:6CxyIPTqSPvAPXfrIZczs4</td>\n",
              "      <td>0.513</td>\n",
              "      <td>0.760</td>\n",
              "      <td>4</td>\n",
              "      <td>-10.077</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0355</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.00339</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.961</td>\n",
              "      <td>153.166</td>\n",
              "      <td>430653</td>\n",
              "      <td>4</td>\n",
              "      <td>25.57331</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            track  ... target\n",
              "0                                     Misty Roses  ...      0\n",
              "1                                      Never Ever  ...      1\n",
              "2                                     Soul Sermon  ...      0\n",
              "3                       Clarinet Marmalade - Live  ...      0\n",
              "4  До смерті і довше - Drum & Base and Rock Remix  ...      0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjoIqCJQEpPg",
        "outputId": "447d4935-1777-4af1-b432-a574f5711a5e"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['track', 'artist', 'uri', 'danceability', 'energy', 'key', 'loudness',\n",
              "       'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness',\n",
              "       'valence', 'tempo', 'duration_ms', 'time_signature', 'chorus_hit',\n",
              "       'sections', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FENWIygbeAz"
      },
      "source": [
        "The column names contain no spaces and are all lowercase and hence correctly formatted. \n",
        "\n",
        "Next, dropping the non-useful columns: track, artist and uri.  \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9THSlBcI9Pf"
      },
      "source": [
        "df = df.drop(['track', 'artist', 'uri'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cU5zWZuGJMf-",
        "outputId": "4c786a2d-14d9-49f8-a9a5-b9d4d5c249b6"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',\n",
              "       'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',\n",
              "       'duration_ms', 'time_signature', 'chorus_hit', 'sections', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSqqUatHFk9G",
        "outputId": "30ac7465-177e-41af-d27d-33350cacfa01"
      },
      "source": [
        "print(f'The dataset consists of {len(df)} rows.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The dataset consists of 41106 rows.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nsqq6PE6JraC",
        "outputId": "71a3f2cb-5037-4ce9-883e-856441bdd08d"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "danceability        float64\n",
              "energy              float64\n",
              "key                   int64\n",
              "loudness            float64\n",
              "mode                  int64\n",
              "speechiness         float64\n",
              "acousticness        float64\n",
              "instrumentalness    float64\n",
              "liveness            float64\n",
              "valence             float64\n",
              "tempo               float64\n",
              "duration_ms           int64\n",
              "time_signature        int64\n",
              "chorus_hit          float64\n",
              "sections              int64\n",
              "target                int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1g-bfyObcu3t"
      },
      "source": [
        "Since the dtype is either float64 or int64, the dataset consists of **numerical attributes only**. This information will be helpful in selecting a criterion for removing non-useful features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNedoEkYBmKr"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6rWV2NVEsBk",
        "outputId": "0ce724ab-a557-4711-8959-89e02ddc3b2e"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "danceability        0\n",
              "energy              0\n",
              "key                 0\n",
              "loudness            0\n",
              "mode                0\n",
              "speechiness         0\n",
              "acousticness        0\n",
              "instrumentalness    0\n",
              "liveness            0\n",
              "valence             0\n",
              "tempo               0\n",
              "duration_ms         0\n",
              "time_signature      0\n",
              "chorus_hit          0\n",
              "sections            0\n",
              "target              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j9b1LApXeSXD"
      },
      "source": [
        "**No missing values** were found for any of the columns in the dataset and hence there is no need to fill them with mean or median."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CkdKPl2JBpDK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c923e22a-70c8-4a4c-c100-dc76b1d2930e"
      },
      "source": [
        "df.target.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.5\n",
              "0    0.5\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5RL690sdTYY"
      },
      "source": [
        "The ratio of values in each class is **equal**, this highlights that there is **no class imbalance** and thus the classifier will not be biased towards a particular class. \n",
        "\n",
        "---\n",
        "\n",
        "Incase of class imbalance, Synthetic Minority Oversampling Technique (SMOTE) is used to fix it.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "3ojHiiGiEo_W",
        "outputId": "c48b1dc4-6a22-43b6-b0e5-4a229bb76a6f"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>4.110600e+04</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.539695</td>\n",
              "      <td>0.579545</td>\n",
              "      <td>5.213594</td>\n",
              "      <td>-10.221525</td>\n",
              "      <td>0.693354</td>\n",
              "      <td>0.072960</td>\n",
              "      <td>0.364197</td>\n",
              "      <td>0.154416</td>\n",
              "      <td>0.201535</td>\n",
              "      <td>0.542440</td>\n",
              "      <td>119.338249</td>\n",
              "      <td>2.348776e+05</td>\n",
              "      <td>3.893689</td>\n",
              "      <td>40.106041</td>\n",
              "      <td>10.475673</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.177821</td>\n",
              "      <td>0.252628</td>\n",
              "      <td>3.534977</td>\n",
              "      <td>5.311626</td>\n",
              "      <td>0.461107</td>\n",
              "      <td>0.086112</td>\n",
              "      <td>0.338913</td>\n",
              "      <td>0.303530</td>\n",
              "      <td>0.172959</td>\n",
              "      <td>0.267329</td>\n",
              "      <td>29.098845</td>\n",
              "      <td>1.189674e+05</td>\n",
              "      <td>0.423073</td>\n",
              "      <td>19.005515</td>\n",
              "      <td>4.871850</td>\n",
              "      <td>0.500006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-49.253000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.516800e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-12.816000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033700</td>\n",
              "      <td>0.039400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.094000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>97.397000</td>\n",
              "      <td>1.729278e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>27.599793</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.552000</td>\n",
              "      <td>0.601000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>-9.257000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.043400</td>\n",
              "      <td>0.258000</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.132000</td>\n",
              "      <td>0.558000</td>\n",
              "      <td>117.565000</td>\n",
              "      <td>2.179070e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>35.850795</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.669000</td>\n",
              "      <td>0.787000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>-6.374250</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.069800</td>\n",
              "      <td>0.676000</td>\n",
              "      <td>0.061250</td>\n",
              "      <td>0.261000</td>\n",
              "      <td>0.768000</td>\n",
              "      <td>136.494000</td>\n",
              "      <td>2.667730e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>47.625615</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.988000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>3.744000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.996000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999000</td>\n",
              "      <td>0.996000</td>\n",
              "      <td>241.423000</td>\n",
              "      <td>4.170227e+06</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>433.182000</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       danceability        energy  ...      sections        target\n",
              "count  41106.000000  41106.000000  ...  41106.000000  41106.000000\n",
              "mean       0.539695      0.579545  ...     10.475673      0.500000\n",
              "std        0.177821      0.252628  ...      4.871850      0.500006\n",
              "min        0.000000      0.000251  ...      0.000000      0.000000\n",
              "25%        0.420000      0.396000  ...      8.000000      0.000000\n",
              "50%        0.552000      0.601000  ...     10.000000      0.500000\n",
              "75%        0.669000      0.787000  ...     12.000000      1.000000\n",
              "max        0.988000      1.000000  ...    169.000000      1.000000\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rlkxhxeeyUu"
      },
      "source": [
        "From the description above, it can be inferred that attributes `danceability, energy, mode, speechiness, acousticness, instrumentalness, liveliness and valence` have been normalized between `[0, 1]`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "Eq4dtXapOPts",
        "outputId": "33d512af-c517-4700-c793-fb6485ce8496"
      },
      "source": [
        "#going to focus on the spotify features when comparing the hits and flops\n",
        "all_songs_hits = df.drop('target', axis=1).loc[df['target'] == 1]\n",
        "all_songs_flops = df.drop('target', axis=1).loc[df['target'] == 0]\n",
        "\n",
        "hits_means = pd.DataFrame(all_songs_hits.describe().loc['mean'])\n",
        "flops_means = pd.DataFrame(all_songs_flops.describe().loc['mean'])\n",
        "means_joined = pd.concat([hits_means,flops_means, (hits_means-flops_means)], axis = 1)\n",
        "means_joined.columns = ['hit_mean', 'flop_mean', 'difference']\n",
        "means_joined"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
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              "        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>hit_mean</th>\n",
              "      <th>flop_mean</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>danceability</th>\n",
              "      <td>0.601237</td>\n",
              "      <td>0.478153</td>\n",
              "      <td>0.123085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>0.624296</td>\n",
              "      <td>0.534794</td>\n",
              "      <td>0.089501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key</th>\n",
              "      <td>5.248528</td>\n",
              "      <td>5.178660</td>\n",
              "      <td>0.069868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loudness</th>\n",
              "      <td>-8.702237</td>\n",
              "      <td>-11.740813</td>\n",
              "      <td>3.038576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>0.730064</td>\n",
              "      <td>0.656644</td>\n",
              "      <td>0.073420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>speechiness</th>\n",
              "      <td>0.069444</td>\n",
              "      <td>0.076477</td>\n",
              "      <td>-0.007033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acousticness</th>\n",
              "      <td>0.280814</td>\n",
              "      <td>0.447581</td>\n",
              "      <td>-0.166767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>instrumentalness</th>\n",
              "      <td>0.030687</td>\n",
              "      <td>0.278145</td>\n",
              "      <td>-0.247458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liveness</th>\n",
              "      <td>0.192637</td>\n",
              "      <td>0.210432</td>\n",
              "      <td>-0.017796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>valence</th>\n",
              "      <td>0.609578</td>\n",
              "      <td>0.475302</td>\n",
              "      <td>0.134276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tempo</th>\n",
              "      <td>120.288280</td>\n",
              "      <td>118.388219</td>\n",
              "      <td>1.900061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration_ms</th>\n",
              "      <td>226095.539970</td>\n",
              "      <td>243659.560940</td>\n",
              "      <td>-17564.020970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_signature</th>\n",
              "      <td>3.938063</td>\n",
              "      <td>3.849316</td>\n",
              "      <td>0.088746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chorus_hit</th>\n",
              "      <td>39.224034</td>\n",
              "      <td>40.988048</td>\n",
              "      <td>-1.764014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sections</th>\n",
              "      <td>10.183380</td>\n",
              "      <td>10.767966</td>\n",
              "      <td>-0.584586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       hit_mean      flop_mean    difference\n",
              "danceability           0.601237       0.478153      0.123085\n",
              "energy                 0.624296       0.534794      0.089501\n",
              "key                    5.248528       5.178660      0.069868\n",
              "loudness              -8.702237     -11.740813      3.038576\n",
              "mode                   0.730064       0.656644      0.073420\n",
              "speechiness            0.069444       0.076477     -0.007033\n",
              "acousticness           0.280814       0.447581     -0.166767\n",
              "instrumentalness       0.030687       0.278145     -0.247458\n",
              "liveness               0.192637       0.210432     -0.017796\n",
              "valence                0.609578       0.475302      0.134276\n",
              "tempo                120.288280     118.388219      1.900061\n",
              "duration_ms       226095.539970  243659.560940 -17564.020970\n",
              "time_signature         3.938063       3.849316      0.088746\n",
              "chorus_hit            39.224034      40.988048     -1.764014\n",
              "sections              10.183380      10.767966     -0.584586"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "cKss6ZqsPEpq",
        "outputId": "8e757efe-306d-4005-e5a4-40fc71965ddd"
      },
      "source": [
        "means_joined.sort_values('difference', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>hit_mean</th>\n",
              "      <th>flop_mean</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loudness</th>\n",
              "      <td>-8.702237</td>\n",
              "      <td>-11.740813</td>\n",
              "      <td>3.038576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tempo</th>\n",
              "      <td>120.288280</td>\n",
              "      <td>118.388219</td>\n",
              "      <td>1.900061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>valence</th>\n",
              "      <td>0.609578</td>\n",
              "      <td>0.475302</td>\n",
              "      <td>0.134276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>danceability</th>\n",
              "      <td>0.601237</td>\n",
              "      <td>0.478153</td>\n",
              "      <td>0.123085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>0.624296</td>\n",
              "      <td>0.534794</td>\n",
              "      <td>0.089501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_signature</th>\n",
              "      <td>3.938063</td>\n",
              "      <td>3.849316</td>\n",
              "      <td>0.088746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>0.730064</td>\n",
              "      <td>0.656644</td>\n",
              "      <td>0.073420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key</th>\n",
              "      <td>5.248528</td>\n",
              "      <td>5.178660</td>\n",
              "      <td>0.069868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>speechiness</th>\n",
              "      <td>0.069444</td>\n",
              "      <td>0.076477</td>\n",
              "      <td>-0.007033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liveness</th>\n",
              "      <td>0.192637</td>\n",
              "      <td>0.210432</td>\n",
              "      <td>-0.017796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acousticness</th>\n",
              "      <td>0.280814</td>\n",
              "      <td>0.447581</td>\n",
              "      <td>-0.166767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>instrumentalness</th>\n",
              "      <td>0.030687</td>\n",
              "      <td>0.278145</td>\n",
              "      <td>-0.247458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sections</th>\n",
              "      <td>10.183380</td>\n",
              "      <td>10.767966</td>\n",
              "      <td>-0.584586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chorus_hit</th>\n",
              "      <td>39.224034</td>\n",
              "      <td>40.988048</td>\n",
              "      <td>-1.764014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration_ms</th>\n",
              "      <td>226095.539970</td>\n",
              "      <td>243659.560940</td>\n",
              "      <td>-17564.020970</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       hit_mean      flop_mean    difference\n",
              "loudness              -8.702237     -11.740813      3.038576\n",
              "tempo                120.288280     118.388219      1.900061\n",
              "valence                0.609578       0.475302      0.134276\n",
              "danceability           0.601237       0.478153      0.123085\n",
              "energy                 0.624296       0.534794      0.089501\n",
              "time_signature         3.938063       3.849316      0.088746\n",
              "mode                   0.730064       0.656644      0.073420\n",
              "key                    5.248528       5.178660      0.069868\n",
              "speechiness            0.069444       0.076477     -0.007033\n",
              "liveness               0.192637       0.210432     -0.017796\n",
              "acousticness           0.280814       0.447581     -0.166767\n",
              "instrumentalness       0.030687       0.278145     -0.247458\n",
              "sections              10.183380      10.767966     -0.584586\n",
              "chorus_hit            39.224034      40.988048     -1.764014\n",
              "duration_ms       226095.539970  243659.560940 -17564.020970"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZhTn_m46FGyE",
        "outputId": "2b7b264b-caf1-456e-8b35-6c5f4e30cc10"
      },
      "source": [
        "#create histograpms of all the variables to see distributions\n",
        "fig, ax = plt.subplots(5,3, figsize=(20,20))\n",
        "\n",
        "def hist_plot(row=0, column=0, variable=None, binsnum=10, color='g'):\n",
        "    ax[row, column].hist(df[variable], bins = binsnum, color = color)\n",
        "    ax[row, column].set_title(variable + ' histogram')\n",
        "\n",
        "idx = 0\n",
        "for i in range(0, 5):\n",
        "  for j in range(0, 3):\n",
        "      hist_plot(i, j, df.columns[idx])\n",
        "      idx += 1 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 15 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQ3ebtwmL-Jv"
      },
      "source": [
        "From the above cells, it can be concluded that hit songs have high values of `loudness, tempo, valence, danceability, energy, time_signature, mode` and low values of `speechiness, liveness, acousticness, instrumentalness, sections, chorus_hit, duration_ms`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3UKe9AIIdUM",
        "outputId": "d7980d67-87d5-4e21-df5b-e5f708651ad8"
      },
      "source": [
        "pearson_corr = df.drop('target', axis=1).corr(method='pearson')\n",
        "pearson_corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>danceability</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.206128</td>\n",
              "      <td>0.015433</td>\n",
              "      <td>0.274160</td>\n",
              "      <td>-0.032743</td>\n",
              "      <td>0.156452</td>\n",
              "      <td>-0.261252</td>\n",
              "      <td>-0.301915</td>\n",
              "      <td>-0.115251</td>\n",
              "      <td>0.553858</td>\n",
              "      <td>-0.066541</td>\n",
              "      <td>-0.063030</td>\n",
              "      <td>0.191664</td>\n",
              "      <td>-0.036277</td>\n",
              "      <td>-0.058615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>0.206128</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022523</td>\n",
              "      <td>0.772628</td>\n",
              "      <td>-0.033780</td>\n",
              "      <td>0.122430</td>\n",
              "      <td>-0.715088</td>\n",
              "      <td>-0.208153</td>\n",
              "      <td>0.157779</td>\n",
              "      <td>0.341427</td>\n",
              "      <td>0.224018</td>\n",
              "      <td>0.011830</td>\n",
              "      <td>0.196072</td>\n",
              "      <td>0.009620</td>\n",
              "      <td>-0.040233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key</th>\n",
              "      <td>0.015433</td>\n",
              "      <td>0.022523</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.008438</td>\n",
              "      <td>-0.140455</td>\n",
              "      <td>0.026515</td>\n",
              "      <td>-0.024178</td>\n",
              "      <td>-0.013101</td>\n",
              "      <td>0.000668</td>\n",
              "      <td>0.007742</td>\n",
              "      <td>0.001154</td>\n",
              "      <td>0.015493</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.010264</td>\n",
              "      <td>0.006784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loudness</th>\n",
              "      <td>0.274160</td>\n",
              "      <td>0.772628</td>\n",
              "      <td>0.008438</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000509</td>\n",
              "      <td>0.069241</td>\n",
              "      <td>-0.566548</td>\n",
              "      <td>-0.374301</td>\n",
              "      <td>0.086708</td>\n",
              "      <td>0.271775</td>\n",
              "      <td>0.169453</td>\n",
              "      <td>-0.049890</td>\n",
              "      <td>0.165520</td>\n",
              "      <td>-0.013401</td>\n",
              "      <td>-0.080065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>-0.032743</td>\n",
              "      <td>-0.033780</td>\n",
              "      <td>-0.140455</td>\n",
              "      <td>0.000509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.059636</td>\n",
              "      <td>0.049961</td>\n",
              "      <td>-0.075992</td>\n",
              "      <td>0.008886</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0.027053</td>\n",
              "      <td>-0.074828</td>\n",
              "      <td>-0.009483</td>\n",
              "      <td>-0.022594</td>\n",
              "      <td>-0.057095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>speechiness</th>\n",
              "      <td>0.156452</td>\n",
              "      <td>0.122430</td>\n",
              "      <td>0.026515</td>\n",
              "      <td>0.069241</td>\n",
              "      <td>-0.059636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.083948</td>\n",
              "      <td>-0.084092</td>\n",
              "      <td>0.131488</td>\n",
              "      <td>0.001528</td>\n",
              "      <td>0.036854</td>\n",
              "      <td>0.007631</td>\n",
              "      <td>0.021085</td>\n",
              "      <td>0.012972</td>\n",
              "      <td>-0.008636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acousticness</th>\n",
              "      <td>-0.261252</td>\n",
              "      <td>-0.715088</td>\n",
              "      <td>-0.024178</td>\n",
              "      <td>-0.566548</td>\n",
              "      <td>0.049961</td>\n",
              "      <td>-0.083948</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.204137</td>\n",
              "      <td>-0.030765</td>\n",
              "      <td>-0.195331</td>\n",
              "      <td>-0.179700</td>\n",
              "      <td>-0.070271</td>\n",
              "      <td>-0.188139</td>\n",
              "      <td>-0.012409</td>\n",
              "      <td>-0.016824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>instrumentalness</th>\n",
              "      <td>-0.301915</td>\n",
              "      <td>-0.208153</td>\n",
              "      <td>-0.013101</td>\n",
              "      <td>-0.374301</td>\n",
              "      <td>-0.075992</td>\n",
              "      <td>-0.084092</td>\n",
              "      <td>0.204137</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.050582</td>\n",
              "      <td>-0.287047</td>\n",
              "      <td>-0.053618</td>\n",
              "      <td>0.106551</td>\n",
              "      <td>-0.077980</td>\n",
              "      <td>0.048654</td>\n",
              "      <td>0.081188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liveness</th>\n",
              "      <td>-0.115251</td>\n",
              "      <td>0.157779</td>\n",
              "      <td>0.000668</td>\n",
              "      <td>0.086708</td>\n",
              "      <td>0.008886</td>\n",
              "      <td>0.131488</td>\n",
              "      <td>-0.030765</td>\n",
              "      <td>-0.050582</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000784</td>\n",
              "      <td>0.024861</td>\n",
              "      <td>0.005336</td>\n",
              "      <td>0.004773</td>\n",
              "      <td>0.037981</td>\n",
              "      <td>-0.011742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>valence</th>\n",
              "      <td>0.553858</td>\n",
              "      <td>0.341427</td>\n",
              "      <td>0.007742</td>\n",
              "      <td>0.271775</td>\n",
              "      <td>0.035636</td>\n",
              "      <td>0.001528</td>\n",
              "      <td>-0.195331</td>\n",
              "      <td>-0.287047</td>\n",
              "      <td>0.000784</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.107901</td>\n",
              "      <td>-0.172166</td>\n",
              "      <td>0.155669</td>\n",
              "      <td>-0.059120</td>\n",
              "      <td>-0.133527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tempo</th>\n",
              "      <td>-0.066541</td>\n",
              "      <td>0.224018</td>\n",
              "      <td>0.001154</td>\n",
              "      <td>0.169453</td>\n",
              "      <td>0.027053</td>\n",
              "      <td>0.036854</td>\n",
              "      <td>-0.179700</td>\n",
              "      <td>-0.053618</td>\n",
              "      <td>0.024861</td>\n",
              "      <td>0.107901</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.024318</td>\n",
              "      <td>0.007659</td>\n",
              "      <td>-0.056054</td>\n",
              "      <td>0.024905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration_ms</th>\n",
              "      <td>-0.063030</td>\n",
              "      <td>0.011830</td>\n",
              "      <td>0.015493</td>\n",
              "      <td>-0.049890</td>\n",
              "      <td>-0.074828</td>\n",
              "      <td>0.007631</td>\n",
              "      <td>-0.070271</td>\n",
              "      <td>0.106551</td>\n",
              "      <td>0.005336</td>\n",
              "      <td>-0.172166</td>\n",
              "      <td>-0.024318</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.018454</td>\n",
              "      <td>0.093875</td>\n",
              "      <td>0.888952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_signature</th>\n",
              "      <td>0.191664</td>\n",
              "      <td>0.196072</td>\n",
              "      <td>0.004757</td>\n",
              "      <td>0.165520</td>\n",
              "      <td>-0.009483</td>\n",
              "      <td>0.021085</td>\n",
              "      <td>-0.188139</td>\n",
              "      <td>-0.077980</td>\n",
              "      <td>0.004773</td>\n",
              "      <td>0.155669</td>\n",
              "      <td>0.007659</td>\n",
              "      <td>0.018454</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.011281</td>\n",
              "      <td>0.008424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chorus_hit</th>\n",
              "      <td>-0.036277</td>\n",
              "      <td>0.009620</td>\n",
              "      <td>0.010264</td>\n",
              "      <td>-0.013401</td>\n",
              "      <td>-0.022594</td>\n",
              "      <td>0.012972</td>\n",
              "      <td>-0.012409</td>\n",
              "      <td>0.048654</td>\n",
              "      <td>0.037981</td>\n",
              "      <td>-0.059120</td>\n",
              "      <td>-0.056054</td>\n",
              "      <td>0.093875</td>\n",
              "      <td>-0.011281</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.085920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sections</th>\n",
              "      <td>-0.058615</td>\n",
              "      <td>-0.040233</td>\n",
              "      <td>0.006784</td>\n",
              "      <td>-0.080065</td>\n",
              "      <td>-0.057095</td>\n",
              "      <td>-0.008636</td>\n",
              "      <td>-0.016824</td>\n",
              "      <td>0.081188</td>\n",
              "      <td>-0.011742</td>\n",
              "      <td>-0.133527</td>\n",
              "      <td>0.024905</td>\n",
              "      <td>0.888952</td>\n",
              "      <td>0.008424</td>\n",
              "      <td>-0.085920</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  danceability    energy  ...  chorus_hit  sections\n",
              "danceability          1.000000  0.206128  ...   -0.036277 -0.058615\n",
              "energy                0.206128  1.000000  ...    0.009620 -0.040233\n",
              "key                   0.015433  0.022523  ...    0.010264  0.006784\n",
              "loudness              0.274160  0.772628  ...   -0.013401 -0.080065\n",
              "mode                 -0.032743 -0.033780  ...   -0.022594 -0.057095\n",
              "speechiness           0.156452  0.122430  ...    0.012972 -0.008636\n",
              "acousticness         -0.261252 -0.715088  ...   -0.012409 -0.016824\n",
              "instrumentalness     -0.301915 -0.208153  ...    0.048654  0.081188\n",
              "liveness             -0.115251  0.157779  ...    0.037981 -0.011742\n",
              "valence               0.553858  0.341427  ...   -0.059120 -0.133527\n",
              "tempo                -0.066541  0.224018  ...   -0.056054  0.024905\n",
              "duration_ms          -0.063030  0.011830  ...    0.093875  0.888952\n",
              "time_signature        0.191664  0.196072  ...   -0.011281  0.008424\n",
              "chorus_hit           -0.036277  0.009620  ...    1.000000 -0.085920\n",
              "sections             -0.058615 -0.040233  ...   -0.085920  1.000000\n",
              "\n",
              "[15 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNEP9DgMIlwN",
        "outputId": "a974e685-4a81-4cb7-ded4-a14bd64d810c"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 12))\n",
        "\n",
        "plt.title(\"Correlation Plot\\nAbsolute value of Pearson's Correlation Coefficient\\n\\n\")\n",
        "sns.heatmap(pearson_corr.abs(),\n",
        "            cmap=sns.diverging_palette(230, 10, as_cmap=True),\n",
        "            square=True,\n",
        "            vmin=0,\n",
        "            vmax=1,\n",
        "            ax=ax,\n",
        "            annot=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "aPs97sKyI5Ng",
        "outputId": "9e2cca82-3c7b-4cd7-e21a-b3927488311c"
      },
      "source": [
        "spearman_corr = df.drop('target', axis=1).corr(method='spearman')\n",
        "spearman_corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>danceability</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.164115</td>\n",
              "      <td>0.015135</td>\n",
              "      <td>0.192920</td>\n",
              "      <td>-0.039545</td>\n",
              "      <td>0.126314</td>\n",
              "      <td>-0.177026</td>\n",
              "      <td>-0.258663</td>\n",
              "      <td>-0.151263</td>\n",
              "      <td>0.534031</td>\n",
              "      <td>-0.044877</td>\n",
              "      <td>0.039293</td>\n",
              "      <td>0.204459</td>\n",
              "      <td>-0.023284</td>\n",
              "      <td>0.030663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>energy</th>\n",
              "      <td>0.164115</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022282</td>\n",
              "      <td>0.755831</td>\n",
              "      <td>-0.037773</td>\n",
              "      <td>0.313679</td>\n",
              "      <td>-0.704296</td>\n",
              "      <td>-0.112258</td>\n",
              "      <td>0.125210</td>\n",
              "      <td>0.302188</td>\n",
              "      <td>0.224814</td>\n",
              "      <td>0.117404</td>\n",
              "      <td>0.204824</td>\n",
              "      <td>-0.001290</td>\n",
              "      <td>0.034705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key</th>\n",
              "      <td>0.015135</td>\n",
              "      <td>0.022282</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011473</td>\n",
              "      <td>-0.138723</td>\n",
              "      <td>0.034355</td>\n",
              "      <td>-0.025846</td>\n",
              "      <td>-0.003685</td>\n",
              "      <td>-0.003244</td>\n",
              "      <td>0.005408</td>\n",
              "      <td>0.001514</td>\n",
              "      <td>0.019078</td>\n",
              "      <td>0.007465</td>\n",
              "      <td>0.008298</td>\n",
              "      <td>0.005600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loudness</th>\n",
              "      <td>0.192920</td>\n",
              "      <td>0.755831</td>\n",
              "      <td>0.011473</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.016053</td>\n",
              "      <td>0.175412</td>\n",
              "      <td>-0.545557</td>\n",
              "      <td>-0.326982</td>\n",
              "      <td>0.082311</td>\n",
              "      <td>0.173122</td>\n",
              "      <td>0.151807</td>\n",
              "      <td>0.057817</td>\n",
              "      <td>0.152944</td>\n",
              "      <td>-0.010751</td>\n",
              "      <td>0.000460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mode</th>\n",
              "      <td>-0.039545</td>\n",
              "      <td>-0.037773</td>\n",
              "      <td>-0.138723</td>\n",
              "      <td>-0.016053</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.105583</td>\n",
              "      <td>0.055872</td>\n",
              "      <td>-0.079649</td>\n",
              "      <td>0.016884</td>\n",
              "      <td>0.033830</td>\n",
              "      <td>0.026574</td>\n",
              "      <td>-0.096990</td>\n",
              "      <td>-0.013926</td>\n",
              "      <td>-0.015232</td>\n",
              "      <td>-0.063743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>speechiness</th>\n",
              "      <td>0.126314</td>\n",
              "      <td>0.313679</td>\n",
              "      <td>0.034355</td>\n",
              "      <td>0.175412</td>\n",
              "      <td>-0.105583</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.207493</td>\n",
              "      <td>-0.024476</td>\n",
              "      <td>0.087085</td>\n",
              "      <td>0.041495</td>\n",
              "      <td>0.097644</td>\n",
              "      <td>0.035815</td>\n",
              "      <td>0.063520</td>\n",
              "      <td>0.007550</td>\n",
              "      <td>-0.007361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acousticness</th>\n",
              "      <td>-0.177026</td>\n",
              "      <td>-0.704296</td>\n",
              "      <td>-0.025846</td>\n",
              "      <td>-0.545557</td>\n",
              "      <td>0.055872</td>\n",
              "      <td>-0.207493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.060174</td>\n",
              "      <td>-0.017131</td>\n",
              "      <td>-0.114566</td>\n",
              "      <td>-0.190844</td>\n",
              "      <td>-0.224878</td>\n",
              "      <td>-0.187073</td>\n",
              "      <td>-0.009121</td>\n",
              "      <td>-0.119316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>instrumentalness</th>\n",
              "      <td>-0.258663</td>\n",
              "      <td>-0.112258</td>\n",
              "      <td>-0.003685</td>\n",
              "      <td>-0.326982</td>\n",
              "      <td>-0.079649</td>\n",
              "      <td>-0.024476</td>\n",
              "      <td>0.060174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.052351</td>\n",
              "      <td>-0.229456</td>\n",
              "      <td>-0.033247</td>\n",
              "      <td>0.122798</td>\n",
              "      <td>-0.061281</td>\n",
              "      <td>0.044166</td>\n",
              "      <td>0.082300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liveness</th>\n",
              "      <td>-0.151263</td>\n",
              "      <td>0.125210</td>\n",
              "      <td>-0.003244</td>\n",
              "      <td>0.082311</td>\n",
              "      <td>0.016884</td>\n",
              "      <td>0.087085</td>\n",
              "      <td>-0.017131</td>\n",
              "      <td>-0.052351</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.029925</td>\n",
              "      <td>0.020468</td>\n",
              "      <td>-0.068181</td>\n",
              "      <td>-0.002913</td>\n",
              "      <td>0.018235</td>\n",
              "      <td>-0.064969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>valence</th>\n",
              "      <td>0.534031</td>\n",
              "      <td>0.302188</td>\n",
              "      <td>0.005408</td>\n",
              "      <td>0.173122</td>\n",
              "      <td>0.033830</td>\n",
              "      <td>0.041495</td>\n",
              "      <td>-0.114566</td>\n",
              "      <td>-0.229456</td>\n",
              "      <td>-0.029925</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.110336</td>\n",
              "      <td>-0.171493</td>\n",
              "      <td>0.165699</td>\n",
              "      <td>-0.044105</td>\n",
              "      <td>-0.103174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tempo</th>\n",
              "      <td>-0.044877</td>\n",
              "      <td>0.224814</td>\n",
              "      <td>0.001514</td>\n",
              "      <td>0.151807</td>\n",
              "      <td>0.026574</td>\n",
              "      <td>0.097644</td>\n",
              "      <td>-0.190844</td>\n",
              "      <td>-0.033247</td>\n",
              "      <td>0.020468</td>\n",
              "      <td>0.110336</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.006204</td>\n",
              "      <td>0.011743</td>\n",
              "      <td>-0.046603</td>\n",
              "      <td>0.073670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>duration_ms</th>\n",
              "      <td>0.039293</td>\n",
              "      <td>0.117404</td>\n",
              "      <td>0.019078</td>\n",
              "      <td>0.057817</td>\n",
              "      <td>-0.096990</td>\n",
              "      <td>0.035815</td>\n",
              "      <td>-0.224878</td>\n",
              "      <td>0.122798</td>\n",
              "      <td>-0.068181</td>\n",
              "      <td>-0.171493</td>\n",
              "      <td>0.006204</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.048742</td>\n",
              "      <td>0.075265</td>\n",
              "      <td>0.785411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_signature</th>\n",
              "      <td>0.204459</td>\n",
              "      <td>0.204824</td>\n",
              "      <td>0.007465</td>\n",
              "      <td>0.152944</td>\n",
              "      <td>-0.013926</td>\n",
              "      <td>0.063520</td>\n",
              "      <td>-0.187073</td>\n",
              "      <td>-0.061281</td>\n",
              "      <td>-0.002913</td>\n",
              "      <td>0.165699</td>\n",
              "      <td>0.011743</td>\n",
              "      <td>0.048742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.007872</td>\n",
              "      <td>0.024288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chorus_hit</th>\n",
              "      <td>-0.023284</td>\n",
              "      <td>-0.001290</td>\n",
              "      <td>0.008298</td>\n",
              "      <td>-0.010751</td>\n",
              "      <td>-0.015232</td>\n",
              "      <td>0.007550</td>\n",
              "      <td>-0.009121</td>\n",
              "      <td>0.044166</td>\n",
              "      <td>0.018235</td>\n",
              "      <td>-0.044105</td>\n",
              "      <td>-0.046603</td>\n",
              "      <td>0.075265</td>\n",
              "      <td>-0.007872</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.161139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sections</th>\n",
              "      <td>0.030663</td>\n",
              "      <td>0.034705</td>\n",
              "      <td>0.005600</td>\n",
              "      <td>0.000460</td>\n",
              "      <td>-0.063743</td>\n",
              "      <td>-0.007361</td>\n",
              "      <td>-0.119316</td>\n",
              "      <td>0.082300</td>\n",
              "      <td>-0.064969</td>\n",
              "      <td>-0.103174</td>\n",
              "      <td>0.073670</td>\n",
              "      <td>0.785411</td>\n",
              "      <td>0.024288</td>\n",
              "      <td>-0.161139</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  danceability    energy  ...  chorus_hit  sections\n",
              "danceability          1.000000  0.164115  ...   -0.023284  0.030663\n",
              "energy                0.164115  1.000000  ...   -0.001290  0.034705\n",
              "key                   0.015135  0.022282  ...    0.008298  0.005600\n",
              "loudness              0.192920  0.755831  ...   -0.010751  0.000460\n",
              "mode                 -0.039545 -0.037773  ...   -0.015232 -0.063743\n",
              "speechiness           0.126314  0.313679  ...    0.007550 -0.007361\n",
              "acousticness         -0.177026 -0.704296  ...   -0.009121 -0.119316\n",
              "instrumentalness     -0.258663 -0.112258  ...    0.044166  0.082300\n",
              "liveness             -0.151263  0.125210  ...    0.018235 -0.064969\n",
              "valence               0.534031  0.302188  ...   -0.044105 -0.103174\n",
              "tempo                -0.044877  0.224814  ...   -0.046603  0.073670\n",
              "duration_ms           0.039293  0.117404  ...    0.075265  0.785411\n",
              "time_signature        0.204459  0.204824  ...   -0.007872  0.024288\n",
              "chorus_hit           -0.023284 -0.001290  ...    1.000000 -0.161139\n",
              "sections              0.030663  0.034705  ...   -0.161139  1.000000\n",
              "\n",
              "[15 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "U457t_ZKI_zN",
        "outputId": "ef2fb677-8a74-4040-f1ba-d05c99966009"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(12, 12))\n",
        "\n",
        "plt.title(\"Correlation Plot\\nAbsolute value of Spearman's Correlation Coefficient\\n\\n\")\n",
        "sns.heatmap(spearman_corr.abs(),\n",
        "            cmap='GnBu',\n",
        "            square=True,\n",
        "            vmin=0,\n",
        "            vmax=1,\n",
        "            ax=ax,\n",
        "            annot=True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6jFuXI5tLywN"
      },
      "source": [
        "df = df.drop(['duration_ms'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9lN1tAULBwa",
        "outputId": "74f91a4e-9399-437b-de89-86a97b714481"
      },
      "source": [
        "from sklearn.feature_selection import f_classif\n",
        "anova_f_values = f_classif(df.drop(['target'], axis=1), df['target'])[0]\n",
        "\n",
        "linear_corr = pd.Series(anova_f_values, index=df.drop(['target'], axis=1).columns)\n",
        "linear_corr.sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "instrumentalness    8191.354832\n",
              "danceability        5593.567971\n",
              "loudness            3662.603077\n",
              "valence             2767.155200\n",
              "acousticness        2648.497971\n",
              "energy              1331.603763\n",
              "time_signature       457.200615\n",
              "mode                 262.192995\n",
              "duration_ms          225.216243\n",
              "sections             148.494372\n",
              "liveness             109.074262\n",
              "chorus_hit            88.718788\n",
              "speechiness           68.656857\n",
              "tempo                 43.861277\n",
              "key                    4.014785\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utNGRqtbM6nj"
      },
      "source": [
        "Since the correlation values between any two variables is not greater than 90%, none of the attributes were removed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnPcqypkBq3b"
      },
      "source": [
        "## Model Training & Tuning\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9NOChJP23ax9"
      },
      "source": [
        "### Splitting entire dataset into train/val/test ratio of 80/20/20 %"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6bAsP9SBsZ5",
        "outputId": "4dda88f1-2ecc-4ca7-f5d2-b841cdcaf6db"
      },
      "source": [
        "df_full_train, df_test = train_test_split(df, test_size=0.2, random_state=11)\n",
        "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=11)\n",
        "print(len(df), len(df_full_train), len(df_train), len(df_val), len(df_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "41106 32884 24663 8221 8222\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gklHxIALW7B"
      },
      "source": [
        "df_train.reset_index(drop=True, inplace=True)\n",
        "df_val.reset_index(drop=True, inplace=True)\n",
        "df_test.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnHi-VgRL0Uu"
      },
      "source": [
        "y_train = df_train.target.values\n",
        "y_val = df_val.target.values\n",
        "y_test = df_test.target.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0xkP6vsL98y"
      },
      "source": [
        "del df_train['target']\n",
        "del df_val['target']\n",
        "del df_test['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "WlJ4JWbHMMm8",
        "outputId": "1485477b-0892-4c57-cab7-2dce14a49f64"
      },
      "source": [
        "df_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>track</th>\n",
              "      <th>artist</th>\n",
              "      <th>uri</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Protector of the Common Folk</td>\n",
              "      <td>Howard Shore</td>\n",
              "      <td>spotify:track:3wlVdDCUCO1SS3xND0pBBc</td>\n",
              "      <td>0.1960</td>\n",
              "      <td>0.0765</td>\n",
              "      <td>0</td>\n",
              "      <td>-22.721</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0380</td>\n",
              "      <td>0.904000</td>\n",
              "      <td>0.805000</td>\n",
              "      <td>0.1110</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>111.672</td>\n",
              "      <td>215065</td>\n",
              "      <td>4</td>\n",
              "      <td>47.23415</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mega Force</td>\n",
              "      <td>707</td>\n",
              "      <td>spotify:track:3LQwO6kRKpAUxHunIJHDAh</td>\n",
              "      <td>0.4190</td>\n",
              "      <td>0.8750</td>\n",
              "      <td>9</td>\n",
              "      <td>-6.584</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0732</td>\n",
              "      <td>0.012400</td>\n",
              "      <td>0.001150</td>\n",
              "      <td>0.2110</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>140.783</td>\n",
              "      <td>241333</td>\n",
              "      <td>4</td>\n",
              "      <td>32.75766</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Peau d'âne arrive à la ferme</td>\n",
              "      <td>Michel Legrand</td>\n",
              "      <td>spotify:track:2Q8gJke42fYWvZPPzGaFCH</td>\n",
              "      <td>0.3790</td>\n",
              "      <td>0.3120</td>\n",
              "      <td>7</td>\n",
              "      <td>-14.960</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0320</td>\n",
              "      <td>0.971000</td>\n",
              "      <td>0.782000</td>\n",
              "      <td>0.0911</td>\n",
              "      <td>0.1360</td>\n",
              "      <td>75.594</td>\n",
              "      <td>96467</td>\n",
              "      <td>1</td>\n",
              "      <td>62.04693</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>We'll Sing In The Sunshine</td>\n",
              "      <td>Gale Garnett</td>\n",
              "      <td>spotify:track:3WYYzUlLrl4NVNeoKd7MnG</td>\n",
              "      <td>0.5360</td>\n",
              "      <td>0.3510</td>\n",
              "      <td>11</td>\n",
              "      <td>-8.026</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0266</td>\n",
              "      <td>0.762000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2200</td>\n",
              "      <td>0.5320</td>\n",
              "      <td>106.730</td>\n",
              "      <td>179120</td>\n",
              "      <td>4</td>\n",
              "      <td>20.30345</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Looking at You</td>\n",
              "      <td>The Damned</td>\n",
              "      <td>spotify:track:55oYn88qoKKWZCI0W0xoVO</td>\n",
              "      <td>0.2710</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>2</td>\n",
              "      <td>-8.030</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1180</td>\n",
              "      <td>0.000178</td>\n",
              "      <td>0.361000</td>\n",
              "      <td>0.1130</td>\n",
              "      <td>0.3950</td>\n",
              "      <td>125.258</td>\n",
              "      <td>307060</td>\n",
              "      <td>4</td>\n",
              "      <td>30.19480</td>\n",
              "      <td>12</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8216</th>\n",
              "      <td>Shangri-La</td>\n",
              "      <td>Vic Dana</td>\n",
              "      <td>spotify:track:5uPKJXCJse6seJX3DegLDN</td>\n",
              "      <td>0.2550</td>\n",
              "      <td>0.2830</td>\n",
              "      <td>8</td>\n",
              "      <td>-9.941</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0294</td>\n",
              "      <td>0.848000</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.0822</td>\n",
              "      <td>0.1420</td>\n",
              "      <td>85.574</td>\n",
              "      <td>126347</td>\n",
              "      <td>3</td>\n",
              "      <td>41.17920</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8217</th>\n",
              "      <td>Binaural Beta Sinus Drone II</td>\n",
              "      <td>The Entrainment</td>\n",
              "      <td>spotify:track:0lkzynfIVpyaY80ARPIFwy</td>\n",
              "      <td>0.0733</td>\n",
              "      <td>0.0120</td>\n",
              "      <td>2</td>\n",
              "      <td>-28.885</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0567</td>\n",
              "      <td>0.996000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.0864</td>\n",
              "      <td>0.0307</td>\n",
              "      <td>176.654</td>\n",
              "      <td>212000</td>\n",
              "      <td>3</td>\n",
              "      <td>121.99132</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8218</th>\n",
              "      <td>Talk Talk</td>\n",
              "      <td>The Music Machine</td>\n",
              "      <td>spotify:track:3J9VPVRnoqyF0keO1nReTz</td>\n",
              "      <td>0.6840</td>\n",
              "      <td>0.8640</td>\n",
              "      <td>1</td>\n",
              "      <td>-6.838</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0461</td>\n",
              "      <td>0.038400</td>\n",
              "      <td>0.000233</td>\n",
              "      <td>0.1070</td>\n",
              "      <td>0.8410</td>\n",
              "      <td>107.028</td>\n",
              "      <td>118133</td>\n",
              "      <td>4</td>\n",
              "      <td>20.68042</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8219</th>\n",
              "      <td>L'homme et son chien</td>\n",
              "      <td>Vladimir Cosma</td>\n",
              "      <td>spotify:track:7M3Y6833GRH45yL4OH7Msk</td>\n",
              "      <td>0.3640</td>\n",
              "      <td>0.0929</td>\n",
              "      <td>2</td>\n",
              "      <td>-19.964</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0328</td>\n",
              "      <td>0.964000</td>\n",
              "      <td>0.838000</td>\n",
              "      <td>0.1220</td>\n",
              "      <td>0.2020</td>\n",
              "      <td>113.024</td>\n",
              "      <td>149427</td>\n",
              "      <td>4</td>\n",
              "      <td>24.80795</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8220</th>\n",
              "      <td>Johnny Have You Seen Her?</td>\n",
              "      <td>The Rembrandts</td>\n",
              "      <td>spotify:track:7uzrNM9kePQSlnNOxtEX4n</td>\n",
              "      <td>0.5970</td>\n",
              "      <td>0.5790</td>\n",
              "      <td>7</td>\n",
              "      <td>-6.165</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0241</td>\n",
              "      <td>0.051100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.2210</td>\n",
              "      <td>0.6680</td>\n",
              "      <td>91.193</td>\n",
              "      <td>255760</td>\n",
              "      <td>4</td>\n",
              "      <td>27.26899</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8221 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                             track             artist  ... chorus_hit  sections\n",
              "0     Protector of the Common Folk       Howard Shore  ...   47.23415        11\n",
              "1                       Mega Force                707  ...   32.75766        11\n",
              "2     Peau d'âne arrive à la ferme     Michel Legrand  ...   62.04693         5\n",
              "3       We'll Sing In The Sunshine       Gale Garnett  ...   20.30345        10\n",
              "4                   Looking at You         The Damned  ...   30.19480        12\n",
              "...                            ...                ...  ...        ...       ...\n",
              "8216                    Shangri-La           Vic Dana  ...   41.17920         6\n",
              "8217  Binaural Beta Sinus Drone II    The Entrainment  ...  121.99132        12\n",
              "8218                     Talk Talk  The Music Machine  ...   20.68042         7\n",
              "8219          L'homme et son chien     Vladimir Cosma  ...   24.80795         9\n",
              "8220     Johnny Have You Seen Her?     The Rembrandts  ...   27.26899        12\n",
              "\n",
              "[8221 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VdklIF53k2M"
      },
      "source": [
        "### Using a dict vectorizer \n",
        "\n",
        "(Note: the dataset consists of only Numerical variables, using a dict vectorizer will not have any effect, this is to show that the shape does not change.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptyknqdaNW9c"
      },
      "source": [
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.metrics import roc_auc_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7B57QohOM6N8"
      },
      "source": [
        "train_dicts = df_train.to_dict(orient='records')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppKdttcjNIZA"
      },
      "source": [
        "dv = DictVectorizer(sparse=False)\n",
        "X_train = dv.fit_transform(train_dicts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAjJAzXlNWtq"
      },
      "source": [
        "val_dicts = df_val.to_dict(orient='records')\n",
        "X_val = dv.transform(val_dicts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xaKOjKhgsNd",
        "outputId": "086d81dd-7067-4c56-a942-a115123db146"
      },
      "source": [
        "df_val.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8221, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvA6Cu0GgnTC",
        "outputId": "95598f3c-f0b7-44fa-e85b-be1b979eb332"
      },
      "source": [
        "X_val.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8221, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAhfaWmHB1z_"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBdYXLiRB10D"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aE1n-8BQcGdK",
        "outputId": "4c7966f0-1dde-46b0-87e8-e6e1457ef4cb"
      },
      "source": [
        "model = LogisticRegression(solver='lbfgs', random_state=11)\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=11, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5YEYZSFcG1r",
        "outputId": "4bd8e3cf-a4fb-444a-c767-ebe3aa29d8e6"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4828437283025032"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xk_uNYIeeiXI",
        "outputId": "3bec6b7c-b2e2-43f1-e82a-d70e1d4969ed"
      },
      "source": [
        "y_pred = model.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4900156738527594"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNGxjMF9NMm7"
      },
      "source": [
        "The untuned LR model has an accuracy of 48.28% on the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZrSgU4ddaWS"
      },
      "source": [
        "param_grid = {\n",
        "              'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
        "              'C' : np.logspace(0, 5, 10),\n",
        "              'solver' : ['liblinear', 'lbfgs', 'newton-cg']\n",
        "              }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MpvdH-9pdjw8"
      },
      "source": [
        "grid_search = GridSearchCV(LogisticRegression(random_state=11), param_grid=param_grid, cv=10, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anvzaqZbeDvG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b79533f7-d8a0-4691-f508-a625de485a80"
      },
      "source": [
        "grid_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 120 candidates, totalling 1200 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  90 tasks      | elapsed:   27.9s\n",
            "[Parallel(n_jobs=-1)]: Done 439 tasks      | elapsed:  3.3min\n",
            "[Parallel(n_jobs=-1)]: Done 861 tasks      | elapsed:  6.8min\n",
            "[Parallel(n_jobs=-1)]: Done 1200 out of 1200 | elapsed:  9.7min finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
              "                                          fit_intercept=True,\n",
              "                                          intercept_scaling=1, l1_ratio=None,\n",
              "                                          max_iter=100, multi_class='auto',\n",
              "                                          n_jobs=None, penalty='l2',\n",
              "                                          random_state=11, solver='lbfgs',\n",
              "                                          tol=0.0001, verbose=0,\n",
              "                                          warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'C': array([1.00000000e+00, 3.59381366e+00, 1.29154967e+01, 4.64158883e+01,\n",
              "       1.66810054e+02, 5.99484250e+02, 2.15443469e+03, 7.74263683e+03,\n",
              "       2.78255940e+04, 1.00000000e+05]),\n",
              "                         'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
              "                         'solver': ['liblinear', 'lbfgs', 'newton-cg']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YgNad857i5sQ",
        "outputId": "00e1e001-25db-41e0-b507-a004397ff08e"
      },
      "source": [
        "final_model = grid_search.best_estimator_\n",
        "final_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=3.5938136638046276, class_weight=None, dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l1',\n",
              "                   random_state=11, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0vvXLvHeGln",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ef44124-cd53-4f7d-ebbb-c5b73e87825e"
      },
      "source": [
        "y_pred = final_model.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8084125763648298"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0e_gA3Qed0H",
        "outputId": "5e6f4c6c-f69e-4fe9-fc96-19a08d2c6554"
      },
      "source": [
        "y_pred = final_model.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8030103843105169"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXeArHJuNUJN"
      },
      "source": [
        "Using a grid search with 10 fold cross validation helped in finding the right hyperparamaters to boost the accuracy to 80.84%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGkwot3PB_u9"
      },
      "source": [
        "### Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImSSYjnNB_vE"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier, export_text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkBgZcIXNI9w",
        "outputId": "73d6164d-6664-4f16-f43f-816483cf7d7a"
      },
      "source": [
        "dt = DecisionTreeClassifier(random_state=11)\n",
        "dt.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=11, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0TGOVcANf0W",
        "outputId": "65e0c724-5a7e-4b49-ec2f-64cb2830fe7d"
      },
      "source": [
        "y_pred = dt.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6997740056812296"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pxJZhrHNw_2",
        "outputId": "db483f1b-196f-4947-ecf8-b1c21b25e37e"
      },
      "source": [
        "y_pred = dt.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999997303756941"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KgLzNa1NiTn"
      },
      "source": [
        "The untuned decision tree model has an accuracy of 69.97% on the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNyaJxSUQZO0"
      },
      "source": [
        "param_grid = {\n",
        "              'criterion' :['gini', 'entropy'],\n",
        "              'splitter': ['best', 'random'],\n",
        "              'max_depth' : [4, 5, 6, 7, 8, 9],\n",
        "              'min_samples_leaf': [1, 5, 10, 15, 20, 50],\n",
        "              'max_features': ['auto', 'sqrt', 'log2'],\n",
        "              'ccp_alpha': [0.1, .01, .001],\n",
        "             }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTRZuhywQt8X"
      },
      "source": [
        "grid_search = GridSearchCV(DecisionTreeClassifier(random_state=11), param_grid=param_grid, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kORHQ3R5Q3iu",
        "outputId": "363d2926-aab1-4452-caa6-c677234fb871"
      },
      "source": [
        "grid_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1296 candidates, totalling 6480 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done 216 tasks      | elapsed:    4.2s\n",
            "[Parallel(n_jobs=-1)]: Done 1416 tasks      | elapsed:   28.2s\n",
            "[Parallel(n_jobs=-1)]: Done 3416 tasks      | elapsed:  1.2min\n",
            "[Parallel(n_jobs=-1)]: Done 6216 tasks      | elapsed:  2.2min\n",
            "[Parallel(n_jobs=-1)]: Done 6480 out of 6480 | elapsed:  2.4min finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort='deprecated',\n",
              "                                              random_state=11,\n",
              "                                              splitter='best'),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'ccp_alpha': [0.1, 0.01, 0.001],\n",
              "                         'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [4, 5, 6, 7, 8, 9],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'min_samples_leaf': [1, 5, 10, 15, 20, 50],\n",
              "                         'splitter': ['best', 'random']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jCg29qbRz9F",
        "outputId": "849e7801-f83c-4b8a-c75c-be330be2ab11"
      },
      "source": [
        "final_model = grid_search.best_estimator_\n",
        "final_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.001, class_weight=None, criterion='entropy',\n",
              "                       max_depth=8, max_features='auto', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=50, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=11, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ancTENGaXO_s",
        "outputId": "340b8685-4662-4435-8d1c-27fac1d7b5b9"
      },
      "source": [
        "y_pred = final_model.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8190606201052075"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6SomamkSFnp",
        "outputId": "4d28f7de-33a4-48ea-dda6-d7f7821c1d4d"
      },
      "source": [
        "y_pred = final_model.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8198789094883174"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jse4G-9wNiTo"
      },
      "source": [
        "Using a grid search with 10 fold cross validation helped in finding the right hyperparamaters to boost the accuracy to 81.90%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L3Jrhy2XCAtu"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_9Mo0VDNHSV"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_9LbrUFCAty",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f58fa36c-7345-4e30-c7b4-1e722f039325"
      },
      "source": [
        "rf = RandomForestClassifier(random_state=11)\n",
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=11, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WydCZDQWl3Xv",
        "outputId": "a24bf073-857d-4768-d491-d373fb31b779"
      },
      "source": [
        "y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8694196480100009"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gy3PGE5mAj4",
        "outputId": "ffbd23d1-7cc7-443b-d8e0-5c1c0feb53f6"
      },
      "source": [
        "y_pred = rf.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999970209802301"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnU4MdG2NzON"
      },
      "source": [
        "The untuned random forest has an accuracy of 86.94% on the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5P7_RkLLmXt2"
      },
      "source": [
        "param_grid = {\n",
        "              'n_estimators' :[100, 110, 120, 130, 140, 150],\n",
        "              'max_depth' : [5, 10, 15],\n",
        "              'min_samples_leaf': [1, 3, 5, 10],\n",
        "             }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KFcfWLBm_bN"
      },
      "source": [
        "grid_search = GridSearchCV(RandomForestClassifier(random_state=11), param_grid=param_grid, cv=2, verbose=True, n_jobs=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wsjrtxxhm_bQ",
        "outputId": "4982b29e-f1a1-4191-aac2-176de3005ba1"
      },
      "source": [
        "grid_search.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 72 candidates, totalling 144 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   58.5s\n",
            "[Parallel(n_jobs=-1)]: Done 144 out of 144 | elapsed:  4.7min finished\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False, random_state=11,\n",
              "                                              verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'max_depth': [5, 10, 15],\n",
              "                         'min_samples_leaf': [1, 3, 5, 10],\n",
              "                         'n_estimators': [100, 110, 120, 130, 140, 150]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXfArqqem_bV",
        "outputId": "4340d69e-30d2-4ffd-a0d9-bb4a17ed7384"
      },
      "source": [
        "final_model = grid_search.best_estimator_\n",
        "final_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=15, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=130,\n",
              "                       n_jobs=None, oob_score=False, random_state=11, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3jrfyiHm_bY",
        "outputId": "0a4646bc-48dc-4294-8295-d54d0dcc6785"
      },
      "source": [
        "final_model.fit(X_train, y_train)\n",
        "y_pred = final_model.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8664044469620693"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJXPvEiXm_bb",
        "outputId": "d35a9fe5-edad-4d1e-c96c-89fb290932f7"
      },
      "source": [
        "y_pred = final_model.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9438656662789469"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iz3xABnJOEs-"
      },
      "source": [
        "Using a grid search did not boost the accuracy, this could be because the param_grid supplied to the search wasn't good enough so I'll try to find the best values for the following parameters manually: \n",
        "  \n",
        "  * n_estimators\n",
        "  * max_depth\n",
        "  * min_samples_leaf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Mhoo933OthV"
      },
      "source": [
        "For n_estimators, I chose [10, 130] as the initial range."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sC7v9pGWy1XO"
      },
      "source": [
        "scores = []\n",
        "\n",
        "for n in range(10, 131, 10):\n",
        "  rf = RandomForestClassifier(n_estimators=n, random_state=11, n_jobs=-1)\n",
        "  rf.fit(X_train, y_train)\n",
        "\n",
        "  y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "  score = roc_auc_score(y_val, y_pred)\n",
        "  scores.append((n, score))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "06Rm-u6BzRM-",
        "outputId": "9d35dabc-65b6-4a0d-cd97-b257701d75a5"
      },
      "source": [
        "df_scores = pd.DataFrame(scores, columns=['n_estimators', 'auc'])\n",
        "plt.plot(df_scores.n_estimators, df_scores.auc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd0d2ae0190>]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gch0B4y0OzEh"
      },
      "source": [
        "but the accuracy kept increasing, so I increased the range to [10, 1000] to find a stagnation in accuracy value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FodApcfrXdc"
      },
      "source": [
        "scores = []\n",
        "\n",
        "for n in range(10, 1001, 100):\n",
        "  rf = RandomForestClassifier(n_estimators=n, random_state=11, n_jobs=-1)\n",
        "  rf.fit(X_train, y_train)\n",
        "\n",
        "  y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "  score = roc_auc_score(y_val, y_pred)\n",
        "  scores.append((n, score))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "pBl-cbYpuGPX",
        "outputId": "b7cca96d-d9be-4ddc-f715-08e05f74d828"
      },
      "source": [
        "df_scores = pd.DataFrame(scores, columns=['n_estimators', 'auc'])\n",
        "plt.plot(df_scores.n_estimators, df_scores.auc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd0dd5e6150>]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFsdcZDhO_uK"
      },
      "source": [
        "The accuracy seems to stagnates around n=500 at about 87.2%. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig_jFuMn02rs"
      },
      "source": [
        "scores = []\n",
        "\n",
        "for d in range(1, 150, 15):\n",
        "    for n in range(10, 501, 100):\n",
        "        rf = RandomForestClassifier(n_estimators=n,\n",
        "                                    max_depth=d,\n",
        "                                    random_state=11, n_jobs=-1)\n",
        "        rf.fit(X_train, y_train)\n",
        "\n",
        "        y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "        auc = roc_auc_score(y_val, y_pred)\n",
        "\n",
        "        scores.append((d, n, auc))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-M65ayJB0_OE"
      },
      "source": [
        "columns = ['max_depth', 'n_estimators', 'auc']\n",
        "df_scores = pd.DataFrame(scores, columns=columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "i0dEBnpL1DeK",
        "outputId": "98576a5b-fbef-46cf-fc3a-8a0d2e8187f2"
      },
      "source": [
        "for d in range(1, 150, 15):\n",
        "    df_subset = df_scores[df_scores.max_depth == d]\n",
        "    \n",
        "    plt.plot(df_subset.n_estimators, df_subset.auc,\n",
        "             label='max_depth=%d' % d)\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd0d4a4d590>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HcttfclAMDeV",
        "outputId": "21ad7950-5cad-44ac-c9b0-0a9a9d5708bd"
      },
      "source": [
        "for d in range(1, 150, 15):\n",
        "  print('{} {}'.format(d, df_scores[df_scores.max_depth == d].auc.max()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 0.7959516947502194\n",
            "16 0.8678736468013044\n",
            "31 0.8693738631473586\n",
            "46 0.8698642376716783\n",
            "61 0.8699089570817966\n",
            "76 0.8699089570817966\n",
            "91 0.8699089570817966\n",
            "106 0.8699089570817966\n",
            "121 0.8699089570817966\n",
            "136 0.8699089570817966\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGl9gDpUPbb9"
      },
      "source": [
        "The best value of max_depth is 61. \n",
        "\n",
        "The aim is to maximize accuracy but also minimize max_depth value since then it will consume less time and computational power."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AL4XCfI1euk"
      },
      "source": [
        "max_depth = 61"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Ls3etVB1ldC"
      },
      "source": [
        "scores = []\n",
        "\n",
        "for s in range(1, 51, 10): # [1, 3, 5, 10, 50]:\n",
        "    for n in range(10, 501, 100):\n",
        "        rf = RandomForestClassifier(n_estimators=n,\n",
        "                                    max_depth=max_depth,\n",
        "                                    min_samples_leaf=s,\n",
        "                                    random_state=11, n_jobs=-1)\n",
        "        rf.fit(X_train, y_train)\n",
        "\n",
        "        y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "        auc = roc_auc_score(y_val, y_pred)\n",
        "\n",
        "        scores.append((s, n, auc))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeA3HbcA1wS1"
      },
      "source": [
        "columns = ['min_samples_leaf', 'n_estimators', 'auc']\n",
        "df_scores = pd.DataFrame(scores, columns=columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qZgsvwI61xNl",
        "outputId": "4aaf19fa-ee7f-41d4-e557-7a8ec708d56e"
      },
      "source": [
        "colors = ['black', 'blue', 'orange', 'red', 'grey']\n",
        "values = range(1, 51, 10) # [1, 3, 5, 10, 50]\n",
        "\n",
        "for s, col in zip(values, colors):\n",
        "    df_subset = df_scores[df_scores.min_samples_leaf == s]\n",
        "    \n",
        "    plt.plot(df_subset.n_estimators, df_subset.auc,\n",
        "             color=col,\n",
        "             label='min_samples_leaf=%d' % s)\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd0d495e390>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMxIbZQaPtTk"
      },
      "source": [
        "The value of min_samples_leaf = 1 gives the best accuracy on the validation dataset.\n",
        "\n",
        "Hence, the final parameter values are:\n",
        " * n_estimators=500,\n",
        " * max_depth=61,\n",
        " * min_samples_leaf=1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcOhZ9VH14RV"
      },
      "source": [
        "min_samples_leaf = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmhBmPgv14pu",
        "outputId": "01b2c83d-dd49-4f16-f1f9-c0f37f1d9e14"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=500,\n",
        "                            max_depth=61,\n",
        "                            min_samples_leaf=1,\n",
        "                            random_state=11, n_jobs=-1)\n",
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=61, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
              "                       n_jobs=-1, oob_score=False, random_state=11, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-VpoFVuBA4K",
        "outputId": "8ba309d2-486e-4047-9cf9-bb48e8b7e5ed"
      },
      "source": [
        "y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8723245747216654"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDssJYGGW9Bl",
        "outputId": "7b1365d1-24cb-4ef6-8209-857320c1d494"
      },
      "source": [
        "y_pred = rf.predict_proba(X_train)[:, 1]\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.999997527347829"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQAL1YbKQEj4"
      },
      "source": [
        "Tuning the hyperparamaters boosted the accuracy of random forest from 86.94% to 87.23% on the validation dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgyF8ufuCBx1"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_t99ZWHGCBx4"
      },
      "source": [
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xm7tvUnzqgf_"
      },
      "source": [
        "features = dv.get_feature_names()\n",
        "dtrain = xgb.DMatrix(X_train, label=y_train, feature_names=features)\n",
        "dval = xgb.DMatrix(X_val, label=y_val, feature_names=features)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOl8YDt9re14"
      },
      "source": [
        "xgb_params = {\n",
        "    'eta': 0.3, \n",
        "    'max_depth': 60,\n",
        "    'min_child_weight': 1,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "    'nthread': 8,\n",
        "    \n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWXAp60EXQ7c"
      },
      "source": [
        "watchlist = [(dtrain, 'train'), (dval, 'val')]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDmUrEMhrfkg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "370c527e-ee4f-4b2e-fbb8-fc1fe4cf8cad"
      },
      "source": [
        "model = xgb.train(xgb_params, dtrain, num_boost_round=200,\n",
        "                  verbose_eval=5,\n",
        "                  evals=watchlist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\ttrain-auc:0.95715\tval-auc:0.812824\n",
            "[5]\ttrain-auc:0.998246\tval-auc:0.847703\n",
            "[10]\ttrain-auc:0.999844\tval-auc:0.855626\n",
            "[15]\ttrain-auc:0.999997\tval-auc:0.859398\n",
            "[20]\ttrain-auc:0.999999\tval-auc:0.861318\n",
            "[25]\ttrain-auc:1\tval-auc:0.86263\n",
            "[30]\ttrain-auc:1\tval-auc:0.863969\n",
            "[35]\ttrain-auc:1\tval-auc:0.864549\n",
            "[40]\ttrain-auc:1\tval-auc:0.865306\n",
            "[45]\ttrain-auc:1\tval-auc:0.865413\n",
            "[50]\ttrain-auc:1\tval-auc:0.865806\n",
            "[55]\ttrain-auc:1\tval-auc:0.865856\n",
            "[60]\ttrain-auc:1\tval-auc:0.86614\n",
            "[65]\ttrain-auc:1\tval-auc:0.866648\n",
            "[70]\ttrain-auc:1\tval-auc:0.866519\n",
            "[75]\ttrain-auc:1\tval-auc:0.86646\n",
            "[80]\ttrain-auc:1\tval-auc:0.866526\n",
            "[85]\ttrain-auc:1\tval-auc:0.866888\n",
            "[90]\ttrain-auc:1\tval-auc:0.866955\n",
            "[95]\ttrain-auc:1\tval-auc:0.867287\n",
            "[100]\ttrain-auc:1\tval-auc:0.867376\n",
            "[105]\ttrain-auc:1\tval-auc:0.867545\n",
            "[110]\ttrain-auc:1\tval-auc:0.867743\n",
            "[115]\ttrain-auc:1\tval-auc:0.867774\n",
            "[120]\ttrain-auc:1\tval-auc:0.867703\n",
            "[125]\ttrain-auc:1\tval-auc:0.867693\n",
            "[130]\ttrain-auc:1\tval-auc:0.867812\n",
            "[135]\ttrain-auc:1\tval-auc:0.868037\n",
            "[140]\ttrain-auc:1\tval-auc:0.867937\n",
            "[145]\ttrain-auc:1\tval-auc:0.867826\n",
            "[150]\ttrain-auc:1\tval-auc:0.867818\n",
            "[155]\ttrain-auc:1\tval-auc:0.867765\n",
            "[160]\ttrain-auc:1\tval-auc:0.867713\n",
            "[165]\ttrain-auc:1\tval-auc:0.867769\n",
            "[170]\ttrain-auc:1\tval-auc:0.867728\n",
            "[175]\ttrain-auc:1\tval-auc:0.86774\n",
            "[180]\ttrain-auc:1\tval-auc:0.867689\n",
            "[185]\ttrain-auc:1\tval-auc:0.867717\n",
            "[190]\ttrain-auc:1\tval-auc:0.867741\n",
            "[195]\ttrain-auc:1\tval-auc:0.867878\n",
            "[199]\ttrain-auc:1\tval-auc:0.867841\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q4C-xO9yrnBa",
        "outputId": "34bb9016-0024-4a8c-e2be-e6a79b377ba7"
      },
      "source": [
        "y_pred = model.predict(dval)\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8678414168624435"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0X10qKKXrzKB",
        "outputId": "e93c9b6b-12be-496d-f298-9a12db1225c9"
      },
      "source": [
        "y_pred = model.predict(dtrain)\n",
        "roc_auc_score(y_train, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999997303756941"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HCq7P8hQbNC"
      },
      "source": [
        "The untuned XG Boost model has an accuracy of 86.78% on the validation dataset. \n",
        "\n",
        "Using grid search on XG Boost was very time-consuming so I am going to manually tune the following hyperparameters by plotting them and picking the best:\n",
        "\n",
        "* eta\n",
        "* max_depth\n",
        "* min_child_weight"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-qc2b-haMtA"
      },
      "source": [
        "def parse_xgb_output(output):\n",
        "    results = []\n",
        "\n",
        "    for line in output.stdout.strip().split('\\n'):\n",
        "        it_line, train_line, val_line = line.split('\\t')\n",
        "\n",
        "        it = int(it_line.strip('[]'))\n",
        "        train = float(train_line.split(':')[1])\n",
        "        val = float(val_line.split(':')[1])\n",
        "\n",
        "        results.append((it, train, val))\n",
        "    \n",
        "    columns = ['num_iter', 'train_auc', 'val_auc']\n",
        "    df_results = pd.DataFrame(results, columns=columns)\n",
        "    return df_results"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvzxwhY2AxKe"
      },
      "source": [
        "etas = {}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwtYRWLXAmDW"
      },
      "source": [
        "%%capture output\n",
        "\n",
        "xgb_params = {\n",
        "    'eta': 0.08, \n",
        "    'max_depth': 100,\n",
        "    'min_child_weight': 1,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "\n",
        "    'nthread': 8,\n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}\n",
        "\n",
        "model = xgb.train(xgb_params, dtrain, num_boost_round=200,\n",
        "                  verbose_eval=5,\n",
        "                  evals=watchlist)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "euhC2wJMAmDX",
        "outputId": "939214a3-f2ec-4d19-ae19-7626ac115d06"
      },
      "source": [
        "key = 'eta=%s' % (xgb_params['eta'])\n",
        "etas[key] = parse_xgb_output(output)\n",
        "key"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'eta=0.08'"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "WsOCfGyJAmDY",
        "outputId": "2844a2a0-83c0-488d-94e5-ab3084cda48d"
      },
      "source": [
        "for eta, df_score in etas.items():\n",
        "    plt.plot(df_score.num_iter, df_score.val_auc, label=eta)\n",
        "\n",
        "plt.ylim(0.8, 0.9)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbfcff6b4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGFRQj4dQywD"
      },
      "source": [
        "From the plot, it is clear that eta=0.08 gives the best accuracy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MeVZX-4Zwr7"
      },
      "source": [
        "scores = {}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSOu3umsZyCr"
      },
      "source": [
        "%%capture output\n",
        "\n",
        "xgb_params = {\n",
        "    'eta': 0.08, \n",
        "    'max_depth': 200,\n",
        "    'min_child_weight': 1,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "\n",
        "    'nthread': 8,\n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}\n",
        "\n",
        "model = xgb.train(xgb_params, dtrain, num_boost_round=200,\n",
        "                  verbose_eval=5,\n",
        "                  evals=watchlist)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FOT75FuvaKMs",
        "outputId": "ac7cbba4-6128-495a-85fd-fdd4fc55bb12"
      },
      "source": [
        "key = 'max_depth=%s' % (xgb_params['max_depth'])\n",
        "scores[key] = parse_xgb_output(output)\n",
        "key"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'max_depth=200'"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "rytHglMXaV6k",
        "outputId": "382ed520-5382-4853-c58d-f1d1381a344a"
      },
      "source": [
        "for max_depth, df_score in scores.items():\n",
        "    plt.plot(df_score.num_iter, df_score.val_auc, label=max_depth)\n",
        "\n",
        "plt.ylim(0.85, 0.9)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbfd49d7790>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kr9PxTSKQ8hb"
      },
      "source": [
        "max_depth=100 and max_depth=200 have overlapping plots, since lower value of max_depth saves time and computational power, max_depth=100 is selected."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSHh4BaCdXgc"
      },
      "source": [
        "weights = {}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rM2MLw_mdc8w"
      },
      "source": [
        "%%capture output\n",
        "\n",
        "xgb_params = {\n",
        "    'eta': 0.08, \n",
        "    'max_depth': 100,\n",
        "    'min_child_weight': 4,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "\n",
        "    'nthread': 8,\n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}\n",
        "\n",
        "model = xgb.train(xgb_params, dtrain, num_boost_round=200,\n",
        "                  verbose_eval=5,\n",
        "                  evals=watchlist)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KE1fn5YLde32",
        "outputId": "5c4174f3-39fc-40f4-8abb-222929fcd6bc"
      },
      "source": [
        "key = 'min_child_weight=%s' % (xgb_params['min_child_weight'])\n",
        "weights[key] = parse_xgb_output(output)\n",
        "key"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'min_child_weight=4'"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "7BZg3gUNdhmG",
        "outputId": "9cb8cb25-6812-4e13-fa0c-90e641c1df6c"
      },
      "source": [
        "for min_child_weight, df_score in weights.items():\n",
        "    plt.plot(df_score.num_iter, df_score.val_auc, label=min_child_weight)\n",
        "\n",
        "plt.ylim(0.86, 0.88)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbfcf52ae10>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrJ3h7fDRN8D"
      },
      "source": [
        "From the plot, it is clear the min_child_weight=2 gives the best accuracy on validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uxADmR2cR04v",
        "outputId": "77dcc841-f168-4c92-a5f0-ed572d53f07d"
      },
      "source": [
        "xgb_params = {\n",
        "    'eta': 0.08, \n",
        "    'max_depth': 100,\n",
        "    'min_child_weight': 2,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "\n",
        "    'nthread': 8,\n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}\n",
        "\n",
        "model = xgb.train(xgb_params, dtrain, num_boost_round=200,\n",
        "                  verbose_eval=20,\n",
        "                  evals=watchlist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\ttrain-auc:0.935605\tval-auc:0.814424\n",
            "[20]\ttrain-auc:0.995004\tval-auc:0.859923\n",
            "[40]\ttrain-auc:0.999436\tval-auc:0.866614\n",
            "[60]\ttrain-auc:0.999971\tval-auc:0.869505\n",
            "[80]\ttrain-auc:0.999998\tval-auc:0.870606\n",
            "[100]\ttrain-auc:0.999999\tval-auc:0.871581\n",
            "[120]\ttrain-auc:1\tval-auc:0.872051\n",
            "[140]\ttrain-auc:1\tval-auc:0.872413\n",
            "[160]\ttrain-auc:1\tval-auc:0.87288\n",
            "[180]\ttrain-auc:1\tval-auc:0.873194\n",
            "[199]\ttrain-auc:1\tval-auc:0.873063\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1BY9UmFR3pd",
        "outputId": "6d563399-3da8-4b28-c5b2-6bbe5d15e07f"
      },
      "source": [
        "y_pred = model.predict(dval)\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8730628149374076"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6HzM5wIRUWa"
      },
      "source": [
        "Tuning the hyperparameters manually boosted the accuracy from 86.78% to 87.30% on the validation dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMGujZjJGzV6"
      },
      "source": [
        "## Selecting the best model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5iZKEHt0G2zM"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OR_0H8r0G13b",
        "outputId": "ef553c34-1332-4861-8337-ba976d3bc2f7"
      },
      "source": [
        "lr = LogisticRegression(C=3.5938136638046276, class_weight=None, dual=False,\n",
        "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
        "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l1',\n",
        "                   random_state=11, solver='liblinear', tol=0.0001, verbose=0,\n",
        "                   warm_start=False)\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=3.5938136638046276, class_weight=None, dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l1',\n",
              "                   random_state=11, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxvW3CwBIWNo",
        "outputId": "fe03a365-4ca4-4b9f-c8fc-bda4d8f4df8c"
      },
      "source": [
        "y_pred = lr.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8084125763648298"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDJCPSGiG8Ub"
      },
      "source": [
        "### Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hv0-snZzG8Uc",
        "outputId": "fae19d9c-ec3f-4756-d03c-cb67c6348ec5"
      },
      "source": [
        "dt = DecisionTreeClassifier(ccp_alpha=0.001, class_weight=None, criterion='entropy',\n",
        "                       max_depth=8, max_features='auto', max_leaf_nodes=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=50, min_samples_split=2,\n",
        "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
        "                       random_state=11, splitter='best')\n",
        "dt.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.001, class_weight=None, criterion='entropy',\n",
              "                       max_depth=8, max_features='auto', max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=50, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=11, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVxt75aUIGTM",
        "outputId": "6623b67c-d55b-4b8b-a601-8bdd8f377cd4"
      },
      "source": [
        "y_pred = dt.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8190606201052075"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hM8_gdtqG8hR"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyPWMdZ9Hd8h",
        "outputId": "db2ea7e8-ce30-49ca-eb8f-1a51d76c948c"
      },
      "source": [
        "rf = RandomForestClassifier(n_estimators=500,\n",
        "                            max_depth=61,\n",
        "                            min_samples_leaf=1,\n",
        "                            random_state=11, n_jobs=-1)\n",
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=61, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
              "                       n_jobs=-1, oob_score=False, random_state=11, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kScyFbHRHd8j",
        "outputId": "03a0cde8-c5a2-471f-f462-279d9467c0b9"
      },
      "source": [
        "y_pred = rf.predict_proba(X_val)[:, 1]\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8723245747216654"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqxUGCLWG8p0"
      },
      "source": [
        "### XG Boost\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9qEComHG8p1"
      },
      "source": [
        "xgb_params = {\n",
        "    'eta': 0.08, \n",
        "    'max_depth': 100,\n",
        "    'min_child_weight': 2,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "\n",
        "    'nthread': 8,\n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}\n",
        "\n",
        "model = xgb.train(xgb_params, dtrain, num_boost_round=200) # ,evals=watchlist)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LUaebKcHKVj",
        "outputId": "5b9ad57a-8dea-4336-e04e-8c4ff07b0c24"
      },
      "source": [
        "y_pred = model.predict(dval)\n",
        "roc_auc_score(y_val, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8730628149374076"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Os_PaYdTRjda"
      },
      "source": [
        "The best performing model is XG Boost with an accuracy of 87.30% on the validation dataset, it will be exported to a Pickle file to be used with the Flask application. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jBqJpD3CLhG"
      },
      "source": [
        "## Store best model as Pickle file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9Vgbv5eCO0y"
      },
      "source": [
        "df_full_train = df_full_train.reset_index(drop=True)\n",
        "y_full_train = df_full_train.target.values\n",
        "del df_full_train['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGEyKlPl43D2"
      },
      "source": [
        "dv = DictVectorizer(sparse=False)\n",
        "\n",
        "dicts_full_train = df_full_train.to_dict(orient='records')\n",
        "X_full_train = dv.fit_transform(dicts_full_train)\n",
        "\n",
        "dicts_test = df_test.to_dict(orient='records')\n",
        "X_test = dv.transform(dicts_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gXRkPsBy47gP"
      },
      "source": [
        "dfulltrain = xgb.DMatrix(X_full_train, label=y_full_train,\n",
        "                    feature_names=dv.get_feature_names())\n",
        "\n",
        "dtest = xgb.DMatrix(X_test, feature_names=dv.get_feature_names())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g473S3Eb4-vm"
      },
      "source": [
        "xgb_params = {\n",
        "    'eta': 0.08, \n",
        "    'max_depth': 100,\n",
        "    'min_child_weight': 2,\n",
        "    \n",
        "    'objective': 'binary:logistic',\n",
        "    'eval_metric': 'auc',\n",
        "\n",
        "    'nthread': 8,\n",
        "    'seed': 1,\n",
        "    'verbosity': 1,\n",
        "}\n",
        "\n",
        "model = xgb.train(xgb_params, dfulltrain, num_boost_round=200)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "otNFM1_X5CeO",
        "outputId": "f2472525-3ecf-407a-8b85-e3522967dc57"
      },
      "source": [
        "y_pred = model.predict(dtest)\n",
        "roc_auc_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8644872551344663"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsT_eTVkRwRL"
      },
      "source": [
        "The accuracy of the full_train dataset is 86.44% which is close to the validation dataset accuracy. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZG5otXeT66zp"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4u1GSllx67YR"
      },
      "source": [
        "output_file = 'final_model_xgb.bin'\n",
        "\n",
        "f_out = open(output_file, 'wb') \n",
        "pickle.dump((dv, model), f_out)\n",
        "f_out.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QASsjw0R6_kc",
        "outputId": "5b2df55f-e857-45bd-c2ff-12e87bd95382"
      },
      "source": [
        "!ls -lh *.bin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw-r--r-- 1 root root 11M Oct 30 12:20 final_model_xgb.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1i8J_JO7FJJ"
      },
      "source": [
        "with open(output_file, 'wb') as f_out: \n",
        "    pickle.dump((dv, model), f_out)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l2LFy8Py7MeC"
      },
      "source": [
        "## Load best model using"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4bkDR8U7LfA"
      },
      "source": [
        "input_file = 'final_model_xgb.bin'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6wma5I_7H4a"
      },
      "source": [
        "with open(input_file, 'rb') as f_in: \n",
        "    dv, model = pickle.load(f_in)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_Mjh_O07KeY"
      },
      "source": [
        "song = df_full_train.to_dict(orient='records')[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q01u3uHb7bIN"
      },
      "source": [
        "X = dv.transform([song])\n",
        "dX = xgb.DMatrix(X, feature_names=dv.get_feature_names())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jtkg_9fh7eeU"
      },
      "source": [
        "y_pred = model.predict(dX)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sm832Q6y7e_b",
        "outputId": "e0d0a3cc-5ed2-4b4d-f52c-a1e88a8112a5"
      },
      "source": [
        "print('input:', song)\n",
        "print('output:', y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input: {'danceability': 0.369, 'energy': 0.94, 'key': 9, 'loudness': -3.6719999999999997, 'mode': 1, 'speechiness': 0.086, 'acousticness': 0.00048499999999999997, 'instrumentalness': 0.0006389999999999999, 'liveness': 0.14, 'valence': 0.517, 'tempo': 110.021, 'duration_ms': 148867, 'time_signature': 4, 'chorus_hit': 31.05358, 'sections': 7}\n",
            "output: [0.00519087]\n"
          ]
        }
      ]
    }
  ]
}