Project_15_Titanic_Survival_Prediction_using_Machine_Learning.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zezY7SSIGrB2"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1IoiqIICdkD"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBcWmPL7HYg2"
      },
      "source": [
        "Data Collection & Processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YkwaUitoHW7v"
      },
      "source": [
        "# load the data from csv file to Pandas DataFrame\n",
        "titanic_data = pd.read_csv('/content/train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "DVLxglk9ISqM",
        "outputId": "6742b662-caa9-49a8-e8b3-88cc2f92b423"
      },
      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "titanic_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Flr1LhieIbfB",
        "outputId": "9b12cbdb-7fc0-482c-df7f-857c607ac012"
      },
      "source": [
        "# number of rows and Columns\n",
        "titanic_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eQ5cEAdJKNR",
        "outputId": "6bb7b6fb-d85c-4983-ae4e-8aab339ed248"
      },
      "source": [
        "# getting some informations about the data\n",
        "titanic_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgm9U8ZNJZrr",
        "outputId": "cf16be03-699c-4d13-c3df-34dc5ab99b9e"
      },
      "source": [
        "# check the number of missing values in each column\n",
        "titanic_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CI0lP-0YJ8p8"
      },
      "source": [
        "Handling the Missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Wzkn3lsJvW2"
      },
      "source": [
        "# drop the \"Cabin\" column from the dataframe\n",
        "titanic_data = titanic_data.drop(columns='Cabin', axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v31f_V2fKiZc"
      },
      "source": [
        "# replacing the missing values in \"Age\" column with mean value\n",
        "titanic_data['Age'].fillna(titanic_data['Age'].mean(), inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8VVLzAoLHiq",
        "outputId": "dee9d5cd-dce0-4871-88f1-a3253a209507"
      },
      "source": [
        "# finding the mode value of \"Embarked\" column\n",
        "print(titanic_data['Embarked'].mode())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    S\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQ97PEzRLjmh",
        "outputId": "02fc9ab1-cac9-414b-bdbb-aef289b2d2b9"
      },
      "source": [
        "print(titanic_data['Embarked'].mode()[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "S\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUKy-XlrLu0x"
      },
      "source": [
        "# replacing the missing values in \"Embarked\" column with mode value\n",
        "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8y1HhZ-MA7k",
        "outputId": "13113251-a6c6-41c3-d7a6-bb54c61ed027"
      },
      "source": [
        "# check the number of missing values in each column\n",
        "titanic_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUH1aG5EMMLr"
      },
      "source": [
        "Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "8o_2pOvOMGbX",
        "outputId": "f3f1b323-dc9b-4da1-d969-169ba5e8ce10"
      },
      "source": [
        "# getting some statistical measures about the data\n",
        "titanic_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>13.002015</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</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>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8319pWvFMWvZ",
        "outputId": "e70160fc-2a8c-4426-b8a5-8e0d1bc630dc"
      },
      "source": [
        "# finding the number of people survived and not survived\n",
        "titanic_data['Survived'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sG6CNAM_NG-n"
      },
      "source": [
        "Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5B5ryjJaM-Mw"
      },
      "source": [
        "sns.set()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "AwxEFDcSNKJJ",
        "outputId": "cfd558cd-ee0b-45c0-d23c-60f3cd4830dc"
      },
      "source": [
        "# making a count plot for \"Survived\" column\n",
        "sns.countplot('Survived', data=titanic_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6c77f16d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DLQ1ZzkNpPs",
        "outputId": "d8ea0c23-292c-4965-b092-5e0e6b03ad74"
      },
      "source": [
        "titanic_data['Sex'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "fnfGNW9rNbfq",
        "outputId": "1f95ffe5-fdcc-445b-e73f-3fb3f19f7192"
      },
      "source": [
        "# making a count plot for \"Sex\" column\n",
        "sns.countplot('Sex', data=titanic_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6cbeb1d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "f7khlwIZNleu",
        "outputId": "674a64ee-0342-4d62-dc65-e803e284c375"
      },
      "source": [
        "# number of survivors Gender wise\n",
        "sns.countplot('Sex', hue='Survived', data=titanic_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6c77d0dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "lejpLYdDOBM0",
        "outputId": "7ca80ea7-e7ab-4aee-c48d-7c6e69f36c52"
      },
      "source": [
        "# making a count plot for \"Pclass\" column\n",
        "sns.countplot('Pclass', data=titanic_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6c5f7bfd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AMEIwAABGCAYAwAjBAAAYIRgAACMEAwBghGAAAIwQDACAEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAACMEAwAgBGCAQAwQjAAAEYIBgDACMEAABghGAAAIwQDAGCEYAAAjBAMAIARggEAMEIwAABGCAYAwAjBAAAYIRgAACMhH4xr165p3bp1ysjI0Lp163T9+vVgjwQAE1LIB6OwsFDZ2dmqq6tTdna2CgoKgj0SAExIk4I9gD+dnZ1qbm7W4cOHJUmZmZnat2+furq6FBMTY/Q57Hab8f3NnDH1ieaEuZE8HiMRPj02IJ8XQwXq8Zs5zez7GU/O9LHzt5/N6/V6R2ug0dbU1KSdO3fq5MmTvm2rVq1SRUWFvvzlLwdxMgCYeEL+lBQAIDSEdDCcTqdu3rypwcFBSdLg4KDa29vldDqDPBkATDwhHYzY2FilpKSotrZWklRbW6uUlBTj5y8AAKMnpJ/DkKSPPvpIeXl5unPnjqZPn66ysjJ96UtfCvZYADDhhHwwAAChIaRPSQEAQgfBAAAYIRgAACMEAwBgJKQvDYLPKysrU11dnVpbW1VTU6Nnn3022CPBUHd3t3Jzc/Xvf/9b4eHh+uIXv6iioiJeJj6GbNmyRS0tLbLb7YqMjNTu3buVkpIS7LEsw6ukxpiGhgYlJydr/fr1qq6uJhhjSE9Pjy5fvqylS5dK+k/8b9++rdLS0iBPBlN3795VVFSUJOm9995TVVWVjh8/HuSprMMpqTHG5XLxl+5jlMPh8MVCkhYuXKi2trYgToSRehgLSert7ZXNFpiLMYYqTkkBQeDxePTOO+8oPT092KNghHbt2qVz587J6/Xq0KFDwR7HUhxhAEGwb98+RUZGasOGDcEeBSNUUlKiP//5z3r99ddVXl4e7HEsRTAAi5WVlenGjRv6+c9/Lrudb8Gxas2aNTp//ry6u7uDPYpl+N8KWOiNN95QU1OTqqqqFB4eHuxxMAL37t2T2+32fVxfX6/o6Gg5HI4gTmUtXiU1xhQXF+vMmTPq6OjQjBkz5HA4hrzBFELX1atXlZmZqTlz5mjy5MmSpFmzZqmqqirIk8FER0eHtmzZor6+PtntdkVHR2vnzp0T6s3cCAYAwAinpAAARggGAMAIwQAAGCEYAAAjBAMAYIRgAAF2/vx5paWlBXsM4KlxLSlghNLT09XR0aGwsDBNmTJFaWlp2r17t6ZOnRrs0YCA4ggDeALV1dW6ePGijh8/rqamJr355pvBHgkIOIIBPIWEhAS98MILunr1qnp6evTTn/5Uy5cv1/PPP68tW7Y88ja/+tWv9PLLL+srX/mKVq1apXfffde3duPGDW3YsEGLFy/W0qVL9eMf/1iS5PV6VVpaqmXLlmnRokV69dVXdeXKFUu+RuAhTkkBT8Htdusvf/mLVqxYodzcXEVGRurkyZOKjIzUxYsXH3mb2bNn6+jRo4qLi9Pp06e1Y8cOnTlzRvHx8aqsrNTXvvY1/frXv9bAwIA++OADSdLZs2fV0NCguro6RUVF6eOPPx7y3gyAFQgG8AR++MMfKiwsTFFRUXrxxReVnZ2ttLQ0nT9/XtHR0ZKkJUuWPPK2r7zyiu/fq1at0ltvvaXGxka9/PLLmjRpktra2tTe3q7ExES5XC5J0qRJk3Tv3j19/PHHWrBggebNmxf4LxL4fwgG8ASqqqr01a9+1fdxY2OjoqOjfbHw58SJEzp8+LBaW1slSZ999pnvEtk7duxQZWWl1q5dq+joaG3cuFFr167VsmXLtH79ehUVFam1tVXf+MY3tHPnTk2bNi0wXyDwCDyHAYyCxMRE3b59W3fu3PG7X2trq/Lz87V7926dP39eDQ0NeuaZZ3zrcXFxKi4u1tmzZ7V3717t3btXN27ckCR9+9vf1u9//3udOnVK169fn3Dv9obgIxjAKIiPj1daWpr27t2r27dva2BgQO+///7n9uvr65PNZlNMTIwk6Xe/+52uXr3qW//jH/+oTz/9VJIUHR0tm80mu92uxsZG/fOf/9TAwICmTJmi8PBw3nwJluOUFDBKysvLtX//fr3yyisaGBjQ0qVL9fzzzw/ZZ/78+crJyVFWVpZsNpvWrFmjRYsW+dY/+OADlZaWqre3V7Gxsdq1a5dmz56tlpYWlZaWqqWlReHh4Vq+fLk2bdpk9ZeICY73wwAAGOGYFgBghGAAAIwQDACAEYIBADBCMAAARggGAMAIwQAAGCEYAAAjBAMAYOT/AHrqmCMR/H8LAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "SNKaPGHuOZgR",
        "outputId": "67611410-c1a6-40ba-e0e4-904b6836242b"
      },
      "source": [
        "sns.countplot('Pclass', hue='Survived', data=titanic_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6c7286a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1KdqdYCPGqS"
      },
      "source": [
        "Encoding the Categorical Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yExAH-sGOjbk",
        "outputId": "73dada09-472f-4d39-ac51-1239e3ded2e9"
      },
      "source": [
        "titanic_data['Sex'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      577\n",
              "female    314\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5LhKZJ1PRhw",
        "outputId": "08de3ac7-787b-4404-8c48-fc42f73e87ad"
      },
      "source": [
        "titanic_data['Embarked'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMbFfslXPVn9"
      },
      "source": [
        "# converting categorical Columns\n",
        "\n",
        "titanic_data.replace({'Sex':{'male':0,'female':1}, 'Embarked':{'S':0,'C':1,'Q':2}}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "Ap4_stBRP1y1",
        "outputId": "23be5ff1-aee8-4d29-9991-0a5d956d940c"
      },
      "source": [
        "titanic_data.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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...            Ticket     Fare  Embarked\n",
              "0            1         0       3  ...         A/5 21171   7.2500         0\n",
              "1            2         1       1  ...          PC 17599  71.2833         1\n",
              "2            3         1       3  ...  STON/O2. 3101282   7.9250         0\n",
              "3            4         1       1  ...            113803  53.1000         0\n",
              "4            5         0       3  ...            373450   8.0500         0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOEeHLwzQFs6"
      },
      "source": [
        "Separating features & Target"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CW6a3AjFP8At"
      },
      "source": [
        "X = titanic_data.drop(columns = ['PassengerId','Name','Ticket','Survived'],axis=1)\n",
        "Y = titanic_data['Survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23Ui_YpbQtcp",
        "outputId": "5163c5f8-45db-45af-add1-544635e31dfb"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
            "0         3    0  22.000000      1      0   7.2500         0\n",
            "1         1    1  38.000000      1      0  71.2833         1\n",
            "2         3    1  26.000000      0      0   7.9250         0\n",
            "3         1    1  35.000000      1      0  53.1000         0\n",
            "4         3    0  35.000000      0      0   8.0500         0\n",
            "..      ...  ...        ...    ...    ...      ...       ...\n",
            "886       2    0  27.000000      0      0  13.0000         0\n",
            "887       1    1  19.000000      0      0  30.0000         0\n",
            "888       3    1  29.699118      1      2  23.4500         0\n",
            "889       1    0  26.000000      0      0  30.0000         1\n",
            "890       3    0  32.000000      0      0   7.7500         2\n",
            "\n",
            "[891 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxSP4zIeQumL",
        "outputId": "1e98089e-86c4-4825-a84e-0f38794f11fb"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0      0\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      0\n",
            "      ..\n",
            "886    0\n",
            "887    1\n",
            "888    0\n",
            "889    1\n",
            "890    0\n",
            "Name: Survived, Length: 891, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpQJXUhYQ0YO"
      },
      "source": [
        "Splitting the data into training data & Test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2zVx7kpQxiY"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Th_1wfmIRvTb",
        "outputId": "c1f3f5ae-5251-473f-a098-ec390e090054"
      },
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 7) (712, 7) (179, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OIC9lxtR8Eg"
      },
      "source": [
        "Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nH56TtyXR97T"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yf1FUj09R1FA"
      },
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIDWULfvTHMv",
        "outputId": "49686b7e-aaa0-40c1-9995-d28591978f3c"
      },
      "source": [
        "# training the Logistic Regression model with training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "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=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uhrXJj5Tgh3"
      },
      "source": [
        "Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKFfzHhUTixn"
      },
      "source": [
        "Accuracy Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZX3di-6TV0Y"
      },
      "source": [
        "# accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVmRjSq_T5C-",
        "outputId": "d98af9b3-38ba-4a05-d124-59bb29589dce"
      },
      "source": [
        "print(X_train_prediction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 1\n",
            " 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1\n",
            " 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0\n",
            " 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0\n",
            " 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1\n",
            " 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0\n",
            " 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0\n",
            " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0\n",
            " 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1\n",
            " 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 0\n",
            " 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0\n",
            " 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0\n",
            " 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0\n",
            " 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0\n",
            " 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0\n",
            " 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 1\n",
            " 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0\n",
            " 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0\n",
            " 0 0 0 1 1 0 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ftIkmimT8XU",
        "outputId": "c41d2ded-7916-478a-dab6-59f07f9ef0bf"
      },
      "source": [
        "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
        "print('Accuracy score of training data : ', training_data_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score of training data :  0.8075842696629213\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAMCqPcRUSOh"
      },
      "source": [
        "# accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4uEMVEkpUnAn",
        "outputId": "39b369a9-ff6f-410f-fb45-70d8869b6fde"
      },
      "source": [
        "print(X_test_prediction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
            " 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0\n",
            " 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0\n",
            " 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0\n",
            " 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLNGm0poUp8E",
        "outputId": "ea7affab-86d7-454a-9587-480c8288ac09"
      },
      "source": [
        "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
        "print('Accuracy score of test data : ', test_data_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy score of test data :  0.7821229050279329\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJGHBJgiU4ZB"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}