Assignment_2_ML_DecisionTrees.ipynb

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        "<a href=\"https://colab.research.google.com/github/sp1999/SHALA2020/blob/master/Assignment_2_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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        "id": "RNGCt-t9hq7H",
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      "source": [
        "# Assignment 2: **Machine learning with tree based models** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlmhCLRrfoG4",
        "colab_type": "text"
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      "source": [
        "In this assignment, you will work on the **Titanic** dataset and use machine learning to create a model that predicts which passengers survived the **Titanic** shipwreck. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysVvT5atiUCf",
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      "source": [
        "---\n",
        "## About the dataset:\n",
        "---\n",
        "* The column named  `Survived` is the label and the remaining columns are features. \n",
        "* The features can be described as given below:\n",
        "  <table>\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th>Variable</th>\n",
        "      <th>Definition </th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td>pclass</td>\n",
        "      <td>Ticket class\t</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>SibSp</td>\n",
        "      <td>Number of siblings / spouses aboard the Titanic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Parch</td>\n",
        "      <td>Number of parents / children aboard the Titanic</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Ticket</td>\n",
        "      <td>Ticket number</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td>Embarked</td>\n",
        "      <td>Port of Embarkation: C = Cherbourg, Q = Queenstown, S = Southampton</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table> \t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XYBBIcKkXtu",
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      "source": [
        "---\n",
        "## Instructions\n",
        "---\n",
        "* Apply suitable data pre-processing techniques, if needed. \n",
        "* Implement a few classifiers to create your model and compare the performance metrics by plotting the curves like roc_auc, confusion matrix, etc. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KE79bLl6iCx2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# data analysis \n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random as rnd\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# machine learning\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier,BaggingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eoqpA6qmZ_JT",
        "colab_type": "code",
        "outputId": "b55dfe52-a315-4d99-ceea-56eb08748a75",
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          "base_uri": "https://localhost:8080/",
          "height": 204
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      },
      "source": [
        "# importing data\n",
        "url = \"https://raw.githubusercontent.com/shala2020/shala2020.github.io/master/Lecture_Materials/Assignments/MachineLearning/L2/titanic.csv\"\n",
        "titanic_data = pd.read_csv(url)\n",
        "titanic_data.head()"
      ],
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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": []
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          "execution_count": 323
        }
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    {
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        "colab_type": "code",
        "outputId": "66b3dec5-dce7-4416-91f7-c87a5711f2fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# analyzing by describing data\n",
        "titanic_data.shape"
      ],
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 324
        }
      ]
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      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# analyzing by describing data\n",
        "print(titanic_data.isna().sum())"
      ],
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRPHkHLzQsVe",
        "colab_type": "code",
        "outputId": "4f11bf70-2f1e-4f21-d89c-545e9b74c52b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# analyzing by describing data\n",
        "titanic_data.describe()"
      ],
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "</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>714.000000</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>14.526497</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>20.125000</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>28.000000</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>38.000000</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": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXQxT0VkQ6Sv",
        "colab_type": "code",
        "outputId": "9d61c8ee-7542-4e86-a3c9-56f85a7b767f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "# analyzing by describing data\n",
        "titanic_data.info()"
      ],
      "execution_count": 327,
      "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": {
        "id": "6ZvSE9wiRDEw",
        "colab_type": "code",
        "outputId": "66c75d57-ed70-4536-c040-a7cb8804eb20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "# analyzing by pivoting Pclass feature against Survived label\n",
        "titanic_data[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
        "# We observe that there is significant correlation (>0.5) among Pclass=1 and Survived"
      ],
      "execution_count": 328,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.629630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.472826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.242363</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Survived\n",
              "0       1  0.629630\n",
              "1       2  0.472826\n",
              "2       3  0.242363"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7nLzKgCVnj7",
        "colab_type": "code",
        "outputId": "e518233d-e606-48af-cd56-da4a7f701ba3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "# analyzing by pivoting Parch feature against Survived label\n",
        "titanic_data[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
        "# We observe that Parch has zero correlation with survived for certain values"
      ],
      "execution_count": 329,
      "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>Parch</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.550847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.343658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Parch  Survived\n",
              "3      3  0.600000\n",
              "1      1  0.550847\n",
              "2      2  0.500000\n",
              "0      0  0.343658\n",
              "5      5  0.200000\n",
              "4      4  0.000000\n",
              "6      6  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 329
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhC9GBYDVoFL",
        "colab_type": "code",
        "outputId": "7ef034d5-4462-4ba3-dfa6-6f80c89c0a5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "# analyzing by pivoting Sex feature against Survived label\n",
        "titanic_data[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
        "# We observe that female passengers have very high survival rate at 74%"
      ],
      "execution_count": 330,
      "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>Sex</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>female</td>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>male</td>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Sex  Survived\n",
              "0  female  0.742038\n",
              "1    male  0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AglW9mNnVoYY",
        "colab_type": "code",
        "outputId": "90e12946-d006-45b6-bfe0-517b0320213a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "# analyzing by pivoting SibSp feature against Survived label\n",
        "titanic_data[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
        "# We observe that SibSp has zero correlation with survived for certain values"
      ],
      "execution_count": 331,
      "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>SibSp</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.535885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.464286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.345395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>8</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SibSp  Survived\n",
              "1      1  0.535885\n",
              "2      2  0.464286\n",
              "0      0  0.345395\n",
              "3      3  0.250000\n",
              "4      4  0.166667\n",
              "5      5  0.000000\n",
              "6      8  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3jWbL8zWrIM",
        "colab_type": "code",
        "outputId": "d8728edf-69a9-4ecd-e1e2-aaef6240bbdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "# analyzing by visualising Age data by plotting its histogram \n",
        "g = sns.FacetGrid(titanic_data, col='Survived', height=5)\n",
        "g.map(plt.hist, 'Age', bins=20)\n",
        "# We observe that infants (Age <=4) have high survival rate and oldest passengers (Age = 80) also survived.\n",
        "# Also large number of 15-25 year olds did not survive.\n",
        "# Most passengers are in 15-35 age range."
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fcc2c8a9240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 332
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgX8NNX9Ym82",
        "colab_type": "code",
        "outputId": "94111e12-cb20-49b1-9afc-128bb895d346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "# analyzing by visualising Age data by plotting its histogram for each Pclass value\n",
        "grid = sns.FacetGrid(titanic_data, col='Survived', row='Pclass', height=3, aspect=2)\n",
        "grid.map(plt.hist, 'Age', alpha=0.75, bins=20)\n",
        "grid.add_legend()\n",
        "# We observe that Pclass=3 has most passengers and most of them did not survive.\n",
        "# Most passengers in Pclass=1 survived.\n",
        "# Infant passengers in Pclass=2 and Pclass=3 mostly survived."
      ],
      "execution_count": 333,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fcc2c8197b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 333
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 872x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OW0odrrdZpV2",
        "colab_type": "code",
        "outputId": "1658f2ba-9a97-4540-c9ae-3c265e420009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "# analyzing by visualising Pclass, Survived and Sex data by plotting their pointplots for each Embarked value\n",
        "grid = sns.FacetGrid(titanic_data, row='Embarked', height=3, aspect=2)\n",
        "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=['blue', 'purple'], order=None, hue_order=None)\n",
        "grid.add_legend()\n",
        "# We observe that Female passengers had much better survival rate than males for Embarked=S and Embarked=Q\n",
        "# There is an exception in Embarked=C where male passengers have higher survival rate. "
      ],
      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fcc2c4e3b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 334
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 502.5x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0Z08Vj9dpQT",
        "colab_type": "code",
        "outputId": "a84ce85d-b6e9-49b2-ef9c-70e61e09994a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "# analyzing by visualising Fare and Sex data by plotting their barplots for each pair of Embarked and Survived value\n",
        "grid = sns.FacetGrid(titanic_data, row='Embarked', col='Survived', height=3, aspect=2)\n",
        "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None, order=None)\n",
        "grid.add_legend()\n",
        "# We observe that higher fare paying passengers have better survival rate. "
      ],
      "execution_count": 335,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fcc2c4e4780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 335
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 872x648 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YoyID5T1eYcy",
        "colab_type": "code",
        "outputId": "267dcfdd-955f-4ddb-a629-1b3ad5a99b44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Dropping the Ticket and Cabin features as they dont have correlation with Survived\n",
        "print(\"Before\", titanic_data.shape)\n",
        "titanic_data = titanic_data.drop(['Ticket', 'Cabin'], axis=1)\n",
        "print(\"After\", titanic_data.shape)"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before (891, 12)\n",
            "After (891, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4hrGgpDfoDz",
        "colab_type": "code",
        "outputId": "8fc2b754-d823-48f7-e9e9-25949ba6fd4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "# Analyzing if Name feature can be engineered to extract titles and test correlation between titles and survival, \n",
        "# before dropping Name and PassengerId features.\n",
        "titanic_data['Title'] = titanic_data[\"Name\"].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "pd.crosstab(titanic_data['Title'], titanic_data['Sex'])"
      ],
      "execution_count": 337,
      "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>Sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Capt</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Countess</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Don</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dr</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonkheer</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lady</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Major</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master</th>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss</th>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mlle</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mme</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mr</th>\n",
              "      <td>0</td>\n",
              "      <td>517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mrs</th>\n",
              "      <td>125</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ms</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rev</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sir</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Sex       female  male\n",
              "Title                 \n",
              "Capt           0     1\n",
              "Col            0     2\n",
              "Countess       1     0\n",
              "Don            0     1\n",
              "Dr             1     6\n",
              "Jonkheer       0     1\n",
              "Lady           1     0\n",
              "Major          0     2\n",
              "Master         0    40\n",
              "Miss         182     0\n",
              "Mlle           2     0\n",
              "Mme            1     0\n",
              "Mr             0   517\n",
              "Mrs          125     0\n",
              "Ms             1     0\n",
              "Rev            0     6\n",
              "Sir            0     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 337
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GoZX3vkIgu6T",
        "colab_type": "code",
        "outputId": "ccc4c24c-41e7-44b4-a5b6-cac0f5575a46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Replacing many titles with a more common name or classifying them as Rare.\n",
        "titanic_data['Title'] = titanic_data['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
        " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "titanic_data['Title'] = titanic_data['Title'].replace('Mlle', 'Miss')\n",
        "titanic_data['Title'] = titanic_data['Title'].replace('Ms', 'Miss')\n",
        "titanic_data['Title'] = titanic_data['Title'].replace('Mme', 'Mrs')\n",
        "titanic_data[['Title', 'Survived']].groupby(['Title'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 338,
      "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>Title</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mrs</td>\n",
              "      <td>0.793651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Miss</td>\n",
              "      <td>0.702703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Master</td>\n",
              "      <td>0.575000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rare</td>\n",
              "      <td>0.347826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mr</td>\n",
              "      <td>0.156673</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Title  Survived\n",
              "3     Mrs  0.793651\n",
              "1    Miss  0.702703\n",
              "0  Master  0.575000\n",
              "4    Rare  0.347826\n",
              "2      Mr  0.156673"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHxI9ukOhxl1",
        "colab_type": "code",
        "outputId": "4cc7f09f-57e6-4b57-8050-52135d94167d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Converting the categorical titles to ordinal values\n",
        "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
        "titanic_data['Title'] = titanic_data['Title'].map(title_mapping)\n",
        "titanic_data['Title'] = titanic_data['Title'].fillna(0)\n",
        "titanic_data.head()"
      ],
      "execution_count": 339,
      "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>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</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>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</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>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</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>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</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>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</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>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Embarked  Title\n",
              "0            1         0       3  ...   7.2500        S      1\n",
              "1            2         1       1  ...  71.2833        C      3\n",
              "2            3         1       3  ...   7.9250        S      2\n",
              "3            4         1       1  ...  53.1000        S      3\n",
              "4            5         0       3  ...   8.0500        S      1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MclpBKC1iKfn",
        "colab_type": "code",
        "outputId": "b04cee1d-7ef6-4964-a62c-e3bf90442e90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Dropping the Name and PassengerId features\n",
        "titanic_data = titanic_data.drop(['Name', 'PassengerId'], axis=1)\n",
        "titanic_data.shape"
      ],
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiCwApwNien9",
        "colab_type": "code",
        "outputId": "f4c4c02d-5e45-4eb6-f8db-9fb26ea809f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        " # Converting Sex feature to a new feature called Gender where female=1 and male=0.\n",
        " titanic_data['Sex'] = titanic_data['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
        " titanic_data.head()"
      ],
      "execution_count": 341,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
              "0         0       3    0  22.0      1      0   7.2500        S      1\n",
              "1         1       1    1  38.0      1      0  71.2833        C      3\n",
              "2         1       3    1  26.0      0      0   7.9250        S      2\n",
              "3         1       1    1  35.0      1      0  53.1000        S      3\n",
              "4         0       3    0  35.0      0      0   8.0500        S      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 341
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRRIfRN5imb1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "7e827f55-dcde-4df5-df02-7738421b58ee"
      },
      "source": [
        "# Completing Age feature by guessing missing or null values by using other correlated features\n",
        "# Preparing an empty array to contain guessed Age values based on Pclass and Gender combinations\n",
        "guess_ages = np.zeros((2,3))\n",
        "\n",
        "# Iterating over Sex (0 or 1) and Pclass (1, 2, 3) features to calculate guessed values of Age for the six combinations.\n",
        "for i in range(0, 2):\n",
        "  for j in range(0, 3):\n",
        "    guess_df = titanic_data[(titanic_data['Sex'] == i) & (titanic_data['Pclass'] == j+1)]['Age'].dropna()\n",
        "    age_guess = guess_df.median()\n",
        "    # Converting random age float to nearest .5 age\n",
        "    guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
        "for i in range(0, 2):\n",
        "  for j in range(0, 3):\n",
        "    titanic_data.loc[(titanic_data[\"Age\"].isnull()) & (titanic_data[\"Sex\"] == i) & (titanic_data[\"Pclass\"] == j+1),\"Age\"] = guess_ages[i,j]\n",
        "titanic_data['Age'] = titanic_data['Age'].astype(int)\n",
        "print(titanic_data.isna().sum())\n",
        "titanic_data.head()"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Survived    0\n",
            "Pclass      0\n",
            "Sex         0\n",
            "Age         0\n",
            "SibSp       0\n",
            "Parch       0\n",
            "Fare        0\n",
            "Embarked    2\n",
            "Title       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
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              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
              "0         0       3    0   22      1      0   7.2500        S      1\n",
              "1         1       1    1   38      1      0  71.2833        C      3\n",
              "2         1       3    1   26      0      0   7.9250        S      2\n",
              "3         1       1    1   35      1      0  53.1000        S      3\n",
              "4         0       3    0   35      0      0   8.0500        S      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lNOqjOARR_m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9ec3ce85-c703-412e-9882-fb89d21c0255"
      },
      "source": [
        "# Creating Age bands and determining correlations with Survived\n",
        "titanic_data['AgeBand'] = pd.cut(titanic_data['Age'], 5)\n",
        "titanic_data[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
      ],
      "execution_count": 343,
      "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",
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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>AgeBand</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(-0.08, 16.0]</td>\n",
              "      <td>0.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "      <td>0.337374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "      <td>0.412037</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(48.0, 64.0]</td>\n",
              "      <td>0.434783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(64.0, 80.0]</td>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         AgeBand  Survived\n",
              "0  (-0.08, 16.0]  0.550000\n",
              "1   (16.0, 32.0]  0.337374\n",
              "2   (32.0, 48.0]  0.412037\n",
              "3   (48.0, 64.0]  0.434783\n",
              "4   (64.0, 80.0]  0.090909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f12KJS1bUrR9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a6546e31-ea22-45f0-cefb-3d5c0fc3b05e"
      },
      "source": [
        "# Replacing Age with ordinal values based on these bands\n",
        "titanic_data.loc[titanic_data['Age'] <= 16, 'Age'] = 0\n",
        "titanic_data.loc[(titanic_data['Age'] > 16) & (titanic_data['Age'] <= 32), 'Age'] = 1\n",
        "titanic_data.loc[(titanic_data['Age'] > 32) & (titanic_data['Age'] <= 48), 'Age'] = 2\n",
        "titanic_data.loc[(titanic_data['Age'] > 48) & (titanic_data['Age'] <= 64), 'Age'] = 3\n",
        "titanic_data.loc[titanic_data['Age'] > 64, 'Age']\n",
        "titanic_data.head()"
      ],
      "execution_count": 344,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>(16.0, 32.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>(32.0, 48.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  ...     Fare  Embarked  Title       AgeBand\n",
              "0         0       3    0    1  ...   7.2500         S      1  (16.0, 32.0]\n",
              "1         1       1    1    2  ...  71.2833         C      3  (32.0, 48.0]\n",
              "2         1       3    1    1  ...   7.9250         S      2  (16.0, 32.0]\n",
              "3         1       1    1    2  ...  53.1000         S      3  (32.0, 48.0]\n",
              "4         0       3    0    2  ...   8.0500         S      1  (32.0, 48.0]\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 344
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0sEhs_4hVG8S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "59d0a339-9a3f-4cfd-ef7f-e21f36345128"
      },
      "source": [
        "# Removing the AgeBand feature\n",
        "titanic_data = titanic_data.drop(['AgeBand'], axis=1)\n",
        "titanic_data.head()"
      ],
      "execution_count": 345,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
              "0         0       3    0    1      1      0   7.2500        S      1\n",
              "1         1       1    1    2      1      0  71.2833        C      3\n",
              "2         1       3    1    1      0      0   7.9250        S      2\n",
              "3         1       1    1    2      1      0  53.1000        S      3\n",
              "4         0       3    0    2      0      0   8.0500        S      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 345
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GomZ84tgVawk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "outputId": "76c8d08b-9760-4709-dc2b-2a4027b51062"
      },
      "source": [
        "# Completing the Embarked feature by filling the missing values with the most common occurrence\n",
        "freq_port = titanic_data[\"Embarked\"].dropna().mode()[0]\n",
        "print(freq_port)\n",
        "titanic_data['Embarked'] = titanic_data['Embarked'].fillna(freq_port)\n",
        "titanic_data[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "S\n"
          ],
          "name": "stdout"
        },
        {
          "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>Embarked</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C</td>\n",
              "      <td>0.553571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Q</td>\n",
              "      <td>0.389610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S</td>\n",
              "      <td>0.339009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Embarked  Survived\n",
              "0        C  0.553571\n",
              "1        Q  0.389610\n",
              "2        S  0.339009"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 346
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOQbyBfhWo2I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e6b3d8f3-3f3a-494d-8664-3f65277a6b22"
      },
      "source": [
        "# Converting the Embarked feature by creating a new numeric Port feature\n",
        "titanic_data['Embarked'] = titanic_data['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
        "titanic_data.head()"
      ],
      "execution_count": 347,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  Title\n",
              "0         0       3    0    1      1      0   7.2500         0      1\n",
              "1         1       1    1    2      1      0  71.2833         1      3\n",
              "2         1       3    1    1      0      0   7.9250         0      2\n",
              "3         1       1    1    2      1      0  53.1000         0      3\n",
              "4         0       3    0    2      0      0   8.0500         0      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMcQe2fXW-Sy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = titanic_data.drop(\"Survived\", axis=1)\n",
        "y = titanic_data[\"Survived\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7V9gDHiYDaG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d216b72c-2cd0-4bde-fe0d-77f142c7cff6"
      },
      "source": [
        "SEED=1\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size=0.2,random_state=SEED)\n",
        "X_train.shape, Y_train.shape, X_test.shape, Y_test.shape"
      ],
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((712, 8), (712,), (179, 8), (179,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pR46dPZZF2J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f99edaa-ee0b-49cc-f702-2b0dc6da0d0f"
      },
      "source": [
        "# Logistic Regression\n",
        "\n",
        "logreg = LogisticRegression(random_state=SEED)\n",
        "logreg.fit(X_train, Y_train)\n",
        "Y_pred = logreg.predict(X_test)\n",
        "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_log = accuracy_score(Y_test,Y_pred)\n",
        "acc_log"
      ],
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 350
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVwOu9A7ZkdO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3e5db7a4-d8f1-4bcf-e052-f2876a51d325"
      },
      "source": [
        "# Support Vector Machines\n",
        "\n",
        "svc = SVC(random_state=SEED)\n",
        "svc.fit(X_train, Y_train)\n",
        "Y_pred = svc.predict(X_test)\n",
        "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_svc = accuracy_score(Y_test,Y_pred)\n",
        "acc_svc"
      ],
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69.38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 351
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWeZwOk9Zla3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6233cb3b-afe2-4bec-9e73-72efcbb979c2"
      },
      "source": [
        "# k-Nearest Neighbours\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 3)\n",
        "knn.fit(X_train, Y_train)\n",
        "Y_pred = knn.predict(X_test)\n",
        "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_knn = accuracy_score(Y_test,Y_pred)\n",
        "acc_knn"
      ],
      "execution_count": 352,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "87.22"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 352
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyHviWIjZy9L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1fd05add-2fe4-4edd-aca1-51d72fa1f485"
      },
      "source": [
        "# Gaussian Naive Bayes\n",
        "\n",
        "gaussian = GaussianNB()\n",
        "gaussian.fit(X_train, Y_train)\n",
        "Y_pred = gaussian.predict(X_test)\n",
        "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_gaussian = accuracy_score(Y_test,Y_pred)\n",
        "acc_gaussian"
      ],
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81.32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 353
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUODveV8Z5By",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8ae3bf4d-79df-4ea7-b435-3aa658c60a6f"
      },
      "source": [
        "# Perceptron\n",
        "\n",
        "perceptron = Perceptron(random_state=SEED)\n",
        "perceptron.fit(X_train, Y_train)\n",
        "Y_pred = perceptron.predict(X_test)\n",
        "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_perceptron = accuracy_score(Y_test,Y_pred)\n",
        "acc_perceptron"
      ],
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "72.47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 354
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kg2dSs3jZ9sm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "893d6225-e688-4ea2-f37b-4c310ec6c143"
      },
      "source": [
        "# Linear SVC\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "linear_svc = LinearSVC(max_iter=100000, random_state=SEED)\n",
        "linear_svc.fit(X_train, Y_train)\n",
        "Y_pred = linear_svc.predict(X_test)\n",
        "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_linear_svc = accuracy_score(Y_test,Y_pred)\n",
        "acc_linear_svc"
      ],
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "79.78"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdH1syGfaBsJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eaf3197b-563c-4955-f992-41174fbf9549"
      },
      "source": [
        "# Stochastic Gradient Descent\n",
        "\n",
        "sgd = SGDClassifier(random_state=SEED)\n",
        "sgd.fit(X_train, Y_train)\n",
        "Y_pred = sgd.predict(X_test)\n",
        "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_sgd = accuracy_score(Y_test,Y_pred)\n",
        "acc_sgd"
      ],
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78.79"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1qu5EW8dRsP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a6071886-4d50-437f-d31c-07e2ed4f2c26"
      },
      "source": [
        "# Decision Tree\n",
        "\n",
        "decision_tree = DecisionTreeClassifier(random_state=SEED)\n",
        "decision_tree.fit(X_train, Y_train)\n",
        "Y_pred = decision_tree.predict(X_test)\n",
        "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_decision_tree = accuracy_score(Y_test,Y_pred)\n",
        "acc_decision_tree"
      ],
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KV9E8voedb2C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "110fcd2b-30d7-48f1-d54d-87c71a65c873"
      },
      "source": [
        "# Random Forest\n",
        "\n",
        "random_forest = RandomForestClassifier(n_estimators=100, random_state=SEED)\n",
        "random_forest.fit(X_train, Y_train)\n",
        "Y_pred = random_forest.predict(X_test)\n",
        "random_forest.score(X_train, Y_train)\n",
        "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
        "accuracy_score_random_forest = accuracy_score(Y_test,Y_pred)\n",
        "acc_random_forest\n"
      ],
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 358
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npeeg8Lsdcfw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "outputId": "48feeed2-888a-4283-a673-31998e151954"
      },
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
        "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
        "              'Stochastic Gradient Decent', 'Linear SVC', \n",
        "              'Decision Tree'],\n",
        "    'Score': [acc_svc, acc_knn, acc_log, \n",
        "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
        "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
        "models.sort_values(by='Score', ascending=False)"
      ],
      "execution_count": 359,
      "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>Model</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>94.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>94.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN</td>\n",
              "      <td>87.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>81.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>80.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Linear SVC</td>\n",
              "      <td>79.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Stochastic Gradient Decent</td>\n",
              "      <td>78.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Perceptron</td>\n",
              "      <td>72.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Support Vector Machines</td>\n",
              "      <td>69.38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Model  Score\n",
              "3               Random Forest  94.80\n",
              "8               Decision Tree  94.80\n",
              "1                         KNN  87.22\n",
              "4                 Naive Bayes  81.32\n",
              "2         Logistic Regression  80.06\n",
              "7                  Linear SVC  79.78\n",
              "6  Stochastic Gradient Decent  78.79\n",
              "5                  Perceptron  72.47\n",
              "0     Support Vector Machines  69.38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 359
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UXOfkISdiQr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "2e962034-426f-4fb0-c77c-4341432717a4"
      },
      "source": [
        "classifiers = ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
        "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
        "              'Stochastic Gradient Decent', 'Linear SVC', \n",
        "              'Decision Tree']\n",
        "scores = [accuracy_score_svc, accuracy_score_knn, accuracy_score_log, accuracy_score_random_forest,\n",
        "          accuracy_score_gaussian, accuracy_score_perceptron, accuracy_score_sgd,\n",
        "          accuracy_score_linear_svc, accuracy_score_decision_tree]\n",
        "for i in range(9):\n",
        "  print('{:s}: {:f}'.format(classifiers[i],scores[i]))"
      ],
      "execution_count": 388,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Support Vector Machines: 0.653631\n",
            "KNN: 0.782123\n",
            "Logistic Regression: 0.776536\n",
            "Random Forest: 0.787709\n",
            "Naive Bayes: 0.765363\n",
            "Perceptron: 0.703911\n",
            "Stochastic Gradient Decent: 0.776536\n",
            "Linear SVC: 0.765363\n",
            "Decision Tree: 0.759777\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__GQmKAcmucL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0caadbae-a41e-4fa3-9d5c-232eb11aca58"
      },
      "source": [
        "# Using Boosting Techniques to improve accuracy\n",
        "from sklearn.ensemble import AdaBoostClassifier,GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "ad=AdaBoostClassifier(base_estimator=decision_tree,n_estimators=100)\n",
        "gd=GradientBoostingClassifier(n_estimators=100)\n",
        "xgb=XGBClassifier(base_estimator=decision_tree,n_estimators=100)\n",
        "boosters=[('Adaboost',ad),('GradientBoost',gd),('XGBoost',xgb)]\n",
        "for booster_name,booster in boosters:\n",
        "   booster.fit(X_train,Y_train)\n",
        "   Y_pred=booster.predict(X_test)\n",
        "   print('{:s}:{:.3f}'.format(booster_name,accuracy_score(Y_test,Y_pred)))"
      ],
      "execution_count": 392,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Adaboost:0.760\n",
            "GradientBoost:0.788\n",
            "XGBoost:0.788\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1F_RoqdjnZF-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1fb01ec2-b82a-43fe-bbec-0044c032863d"
      },
      "source": [
        "# Metrics\n",
        "from sklearn.metrics import confusion_matrix,roc_auc_score,roc_curve,classification_report\n",
        "# Calculating the metrics for all the models\n",
        "models=[('Support Vector Machines',svc), ('KNN',knn), ('Logistic Regression',logreg), \n",
        "        ('Random Forest',random_forest), ('Naive Bayes', gaussian), ('Perceptron', perceptron), \n",
        "        ('Stochastic Gradient Decent', sgd), ('Linear SVC', linear_svc), \n",
        "        ('Decision Tree', decision_tree)]\n",
        "for model_name,model in models:\n",
        "  print(model_name)\n",
        "  Y_pred=model.predict(X_test)\n",
        "  print(confusion_matrix(Y_test,Y_pred))\n",
        "  print('*'*30)\n",
        "  print(classification_report(Y_test,Y_pred))\n",
        "  print('ROC_AUC: {:.2f}'.format(roc_auc_score(Y_test,Y_pred)))\n",
        "  print('\\n')"
      ],
      "execution_count": 394,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Support Vector Machines\n",
            "[[97  9]\n",
            " [53 20]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.92      0.76       106\n",
            "           1       0.69      0.27      0.39        73\n",
            "\n",
            "    accuracy                           0.65       179\n",
            "   macro avg       0.67      0.59      0.57       179\n",
            "weighted avg       0.66      0.65      0.61       179\n",
            "\n",
            "ROC_AUC: 0.59\n",
            "\n",
            "\n",
            "KNN\n",
            "[[96 10]\n",
            " [29 44]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.91      0.83       106\n",
            "           1       0.81      0.60      0.69        73\n",
            "\n",
            "    accuracy                           0.78       179\n",
            "   macro avg       0.79      0.75      0.76       179\n",
            "weighted avg       0.79      0.78      0.77       179\n",
            "\n",
            "ROC_AUC: 0.75\n",
            "\n",
            "\n",
            "Logistic Regression\n",
            "[[91 15]\n",
            " [25 48]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.86      0.82       106\n",
            "           1       0.76      0.66      0.71        73\n",
            "\n",
            "    accuracy                           0.78       179\n",
            "   macro avg       0.77      0.76      0.76       179\n",
            "weighted avg       0.78      0.78      0.77       179\n",
            "\n",
            "ROC_AUC: 0.76\n",
            "\n",
            "\n",
            "Random Forest\n",
            "[[94 12]\n",
            " [26 47]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.89      0.83       106\n",
            "           1       0.80      0.64      0.71        73\n",
            "\n",
            "    accuracy                           0.79       179\n",
            "   macro avg       0.79      0.77      0.77       179\n",
            "weighted avg       0.79      0.79      0.78       179\n",
            "\n",
            "ROC_AUC: 0.77\n",
            "\n",
            "\n",
            "Naive Bayes\n",
            "[[86 20]\n",
            " [22 51]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.81      0.80       106\n",
            "           1       0.72      0.70      0.71        73\n",
            "\n",
            "    accuracy                           0.77       179\n",
            "   macro avg       0.76      0.75      0.76       179\n",
            "weighted avg       0.76      0.77      0.76       179\n",
            "\n",
            "ROC_AUC: 0.75\n",
            "\n",
            "\n",
            "Perceptron\n",
            "[[62 44]\n",
            " [ 9 64]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.58      0.70       106\n",
            "           1       0.59      0.88      0.71        73\n",
            "\n",
            "    accuracy                           0.70       179\n",
            "   macro avg       0.73      0.73      0.70       179\n",
            "weighted avg       0.76      0.70      0.70       179\n",
            "\n",
            "ROC_AUC: 0.73\n",
            "\n",
            "\n",
            "Stochastic Gradient Decent\n",
            "[[79 27]\n",
            " [13 60]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.75      0.80       106\n",
            "           1       0.69      0.82      0.75        73\n",
            "\n",
            "    accuracy                           0.78       179\n",
            "   macro avg       0.77      0.78      0.77       179\n",
            "weighted avg       0.79      0.78      0.78       179\n",
            "\n",
            "ROC_AUC: 0.78\n",
            "\n",
            "\n",
            "Linear SVC\n",
            "[[90 16]\n",
            " [26 47]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.85      0.81       106\n",
            "           1       0.75      0.64      0.69        73\n",
            "\n",
            "    accuracy                           0.77       179\n",
            "   macro avg       0.76      0.75      0.75       179\n",
            "weighted avg       0.76      0.77      0.76       179\n",
            "\n",
            "ROC_AUC: 0.75\n",
            "\n",
            "\n",
            "Decision Tree\n",
            "[[93 13]\n",
            " [30 43]]\n",
            "******************************\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.88      0.81       106\n",
            "           1       0.77      0.59      0.67        73\n",
            "\n",
            "    accuracy                           0.76       179\n",
            "   macro avg       0.76      0.73      0.74       179\n",
            "weighted avg       0.76      0.76      0.75       179\n",
            "\n",
            "ROC_AUC: 0.73\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPb95ifioxJU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "51ea121a-0b67-4272-8c99-6eae7caaedf9"
      },
      "source": [
        "# Plotting ROC curve for Decision Tree\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "decision_tree_disp = plot_roc_curve(decision_tree,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Decision Tree Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-f2f8y_pT7B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "105a7c05-a202-4dc0-a7c6-5587bc964c99"
      },
      "source": [
        "# Plotting ROC curve for Random Forest\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "random_forest_disp = plot_roc_curve(random_forest,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Random Forest Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Fh5zoMXphuD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3ef12c97-9b64-40cb-ccf7-d9ea1cd7840d"
      },
      "source": [
        "# Plotting ROC curve for Stochastic Gradient Descent\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "sgd_disp = plot_roc_curve(sgd,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Stochastic Gradient Descent Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 400,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSyDnwIRpshA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a1671a30-31e5-4936-8aac-b140f67ea0e4"
      },
      "source": [
        "# Plotting ROC curve for k-Nearest Neighbours\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "knn_disp = plot_roc_curve(knn,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('k-Nearest Neighbours Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 402,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_oND4jbwp-UK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "33f540fd-385e-47fc-af4e-9c2696a58047"
      },
      "source": [
        "# Plotting ROC curve for Support Vector Machines\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "svc_disp = plot_roc_curve(svc,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Support Vector Machine ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 404,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_7YVcMXqJ3j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "75d30583-3511-46bb-c546-f7a143b44a25"
      },
      "source": [
        "# Plotting ROC curve for Linear SVC\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "linear_svc_disp = plot_roc_curve(linear_svc,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Linear SVC ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 406,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EarIE1WJqUm9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "758a848e-131f-467d-d7d7-314315c7cefe"
      },
      "source": [
        "# Plotting ROC curve for Gaussian Naive Bayes\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "gaussian_disp = plot_roc_curve(gaussian,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Gaussian Naive Bayes Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 408,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkZC15PPqwyn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a3ebc475-5555-4c48-bae6-f8e8d9b36fbe"
      },
      "source": [
        "# Plotting ROC curve for Perceptron\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "perceptron_disp = plot_roc_curve(perceptron,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Perceptron Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 409,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NnGJO0Pr1rjP466+hSxfo3t3riIodOyMwxpRckye74aC//AIvvwzffefOCsxR7IzAGFNy1anjykS/+qqbNMbkyRKB8cyKbfvYWMAUjb8npYYwGlMipKe70hCZmW66yPPOcw9TIEsExjNXjPuFlIPpx2xXKdp+TU0hzJ8P//gHLFoEgwb9WSTOHJP9DzOeOZSeycWn1ecfZ+d/F2eVmDLUqRy547tNIRw6BCNHuvpAtWq5UhFhPG2kF4KaCESkD/A8EAWMV9XRubY3AiYBVX1tHlDVmcGMyRQvtSqVI65+Fa/DMOFs7VoYMwauuw6eeioiisQFWtBGDYlIFDAWOB9oA1wpIm1yNfsnME1VTwOuAF4OVjzGmBJk716YONE9b9sWVq2C8eMtCZygYA4fPQNYraprVfUIMBUYmKuNAtnlGqsAW4IYjzGmJJg50w0JHTwYli9360rQtJFeCGYiqA9s8ltO8q3zNwL4u4gkATOBW/N6IxEZIiKJIpKYnJwcjFiNMcXdzp2uTPQFF0ClSjB7dsQWiQs0r28ouxKYqKoNgL7AZBH5S0yqOk5VE1Q1oVatWiEP0hjjsewicVOnumGh8+fDmWd6HVWJEczO4s1AQ7/lBr51/gYDfQBU9RcRiQZqAjuCGJcxJlxs3+5GAkVFuVFBjRvDKad4HVWJE8wzgrlAcxFpIiJlcZ3BM3K12Qj0ABCR1kA0YNd+jIl0qvDGG9CyJYwb59b1729JIEiClghUNQMYDnwJLMeNDloqIo+KyABfs7uBG0VkETAFuE5VNVgxGWPCwNq10LMn3HCDqw7as6fXEZV4Qb2PwHdPwMxc6x72e74M6BzMGIwxYWTSJBg2zF0KevVVuPFGKOV1V2bJZ3cWG2OKj3r14Nxz4ZVXoEEDr6OJGJYIjDHeOXIERo+GrCwYMQJ69XIPE1KWCEzA/J60h1GfLyMzq3DdPLmnoDQRZu5cVyRuyRJ3f4AVifOMXXwzAfPrul38um43UaWEcmVKHfNxdrOanNuqttdhm1A7eBDuucfdB5CSAjNmwFtvWRLwkJ0RmCI7dCST9Kws0tIzARh/bQKVost4HJUpttatgxdfdB3BTz4JVazooNcsEZgiWbgplUte+fmoy0FRpewvO5PLnj0wfTpcf70rErd6NTRseOzXmZCwRGCKZNueNDKzlBu7NKFO5WjqV42hfFn7tTJ+Pv8cbroJtm6FTp2gVStLAsWM/Y81AXHRaQ1oU6/ysRuayJGcDHfcAe++66qFTp/ukoApdiwRGGMCLzMTzj7b9QeMHAkPPABly3odlcmHJQJjTOBs2wa1a7s7g595BmJj3dmAKdZs+KgxpuiysuC116BFC/cvQL9+lgTChCUCY0zRrF4NPXrA0KFw+unQu7fXEZnjZInAGHPi3nwT2rVzE8W8/jp88w00bep1VOY4WR+BKdD1b/7GvA0p+W5Pz3T3D9hNoRGqUSN3BjB2LNTPPROtCReWCEyB5m9MpV7VGM5sWiPfNlViytC8dsUQRmU8c/gw/Pvfrk/g0UfdJaEePbyOyhSRJQIDwIHDGfyxbe9f1mdkZnFm0xqMGNDWg6hMsfLrrzB4MCxdCtdea0XiShBLBAaAETOW8v68pDy3lS8bFeJoTLFy4AD861/w3HPu8s9nn8EFF3gdlQkgSwQGgP2HM6hfNYZ/X9zuqPUi0L5RNY+iMsXChg3w8stuVNDo0VDZ7iAvaSwRmBwVykVxTotaXodhioPUVPjgAzdvcJs2boiozRhWYtnwUWPM0T75xH35Dx0Kf/zh1lkSKNEsERhjnB074Ior4MILoVYtmDPHisRFCLs0FMG+XbGDZ75aQVYWbEo5SN0q0V6HZLySmQmdO8PGjTBqFNx3H5SxyYUihSWCCPbLml0s27KXc1vVoV7VGLq1tP6BiLNlC5x0kisS9/zzrkhcmzZeR2VCzBJBhEg9eOQvk8UfOJxBudJRjL82waOojGeyi8Tdf78bCTRsGPTt63VUxiOWCCLAks176P/ST6j+dVvlaPsViDgrV7r5gn/4AXr2hPPP9zoi4zH7FogAyfsPowpDu55Mo+rlj9p2cq0KHkVlPPHGGzB8OERHw4QJcN11dnewsUQQSXq3rcNpdnNYZIuNdWcAY8dC3bpeR2OKCUsExpRkhw/DY4+556NGWZE4kye7j8CYkurnnyE+Hh5/HLZuJc9OImOwM4IS60hGFv/8+HdSDqazc/9hr8MxobR/Pzz0ELz4IjRsCP/9r80aZgoU1DMCEekjIitEZLWIPJBPm8tEZJmILBWRd4MZTyTZuPsA0xKTWLZlL2npWZzRpDpNalrHcETYuNENDb3lFliyxJKAOaagnRGISBQwFugFJAFzRWSGqi7za9Mc+D+gs6qmiEjtYMUTqR44vxX9T63ndRgm2FJS4P33YcgQd0PY2rVQz37upnCCeUZwBrBaVdeq6hFgKjAwV5sbgbGqmgKgqjuCGI8xJdNHH7kv/2HDYMUKt86SgDkOwUwE9YFNfstJvnX+WgAtRGS2iMwRkT55vZGIDBGRRBFJTE5ODlK4xoSZbdvg0kvh4otdmYjffoOWLb2OyoQhrzuLSwPNgW5AA+AHEWmnqqn+jVR1HDAOICEhwYY+GJOZCV26wKZN8MQTcM89ViTOnLBgJoLNQEO/5Qa+df6SgF9VNR1YJyIrcYlhbhDjMiZ8JSW5yz5RUfDCC9CkiZWKNkUWzEtDc4HmItJERMoCVwAzcrX5GHc2gIjUxF0qWhvEmIwJT1lZbjhoq1bwyitu3fnnWxIwARG0MwJVzRCR4cCXQBQwQVWXisijQKKqzvBtO09ElgGZwL2quitYMUWCibPXsWH3QfYcTPc6FBMof/zhpoycPdsNBe3Xz+uITAkT1D4CVZ0JzMy17mG/5wrc5XuYIjqckcmIT5dRtnQpypUuRa1K5ezegXA3frwrEle+PEyaBFdfbUXiTMB53VlsAii7gsAdPZszrFszb4MxgXHyydC/P7z0EtSp43U0poSyRGBMcZKWBo8+6p4/8QR07+4exgSRFZ0zpriYPdsVifv3vyE52YrEmZCxRGCM1/btg1tvdfcFHD4MX34Jr79ufQEmZCwRGOO1pCTXKXzrrfD773DeeV5HZCKM9REY44Vdu2DaNLj5Zmjd2hWJsxnDjEeOOxGISCngSlV9JwjxmBPw85qdLNuyl4wsu6Zc7KnChx+6EtG7d8O557r6QJYEjIfyTQQiUhm4BVcobgbwNTAcuBtYBFgiKCbumbaILXvScpbrV43xMBqTr61bXQL46CPo0AG++sqKxJlioaAzgslACvALcAPwICDAhaq6MASxmUJKz1Iuad+ARwa0IUqECuXsil+xk10kbvNm+M9/4M47obT9nEzxUNBvYlNVbQcgIuOBrUAjVU0r4DUmRN75dQNf/L4NgNSDRyhbuhSVo636ZLGzaRPUr++KxI0d64rEtWjhdVTGHKWgUUM5xWpUNRNIsiRQfHw4L4lFSakcSs/k1AZV6dHKJncrVjIzXXVQ/yJxvXtbEjDFUkFnBKeKyF7c5SCAGL9lVdXKQY8ugmVkZlFQ32+WQnzDqkwe3DF0QZnCWb4cBg+GX35xFUL79/c6ImMKlG8iUNWoUAZi/jR79U6umfAbmccYBdS1Ra0QRWQKbdw4dz9ApUoweTJcdZXdGGaKvYJGDUUDQ4FmwGJcGemMUAUWyZJSDpKZpdx0TlMqx+R/3f+c5pYIip3mzeGii9xlodp2uc6Eh4IuDU3C9RP8CPQF2gK3hyIo41x7Viz1bCho8XboEIwY4f7qHz3aisSZsFRQImjjN2roDeC30IRkTJj44Qc3YcyqVTB0qLtZzC4DmTBU2FFDdknImGx798KwYdC1qxsdNGuWGxlkScCEqYLOCOJ9o4TAjRSyUUPGAGzZAhMnwl13ubkDKtgscCa8FZQIFqnqaSGLxJjibOdOVyRu2DB3b8C6dTZjmCkxCro0ZBXMjFGF996DNm3gjjtg5Uq33pKAKUEKOiOoLSL5TiqvqmOCEI8xxceWLa5M9IwZkJDg+gLszmBTAhWUCKKAivx5Z7ExkSMzE845xxWJe/ppuP12KxJnSqyCfrO3quqjIYvEsG7nAQ4czmBzyiGvQ4lcGzZAgwauSNzLL0PTptCsmddRGRNUBSUCOxMIoSWb99DvxZ+OWleutM0kGjKZmfD88/DPf7oy0cOH25SRJmIUlAh6hCwKw95D7raNe3u3pHntitSsVI4aFct5HFWEWLLEFYn77Tfo1w8uvNDriIwJqYKKzu0OZSCR5LPFW1iXfOCodRt2HwQgoXE1Ojat4UVYkenVV+G226BKFXj3XbjiCrsxzEQc6/3ywG1TFuRZYjqmTBQnVYkOfUCRKLscROvWcOml8NxzUMuK+JnIZInAA1kKt53bjNt6ND9qvYgQVcr+Gg2qgwfh4YddZ/CTT7oyEV27eh2VMZ6yRBAiT3+5gtlrduYsiwilo6wzOKS++84ViVuzxt0hbEXijAEKvrPYBNDHCzezOeUQFcuVplvLWnS3qSVDZ88euOmmP8tD/+9/bv5gSwLGAHZGEFRZWcretPSc52c3r8mYy+I9jioCbd0Kb78N99wDI0dC+fJeR2RMsRLURCAifYDncXcpj1fV0fm0uwT4ADhdVRODGVMo3fvBYj6cn5SzXKaUnYCFTHIyTJ3qpo1s1QrWr7fOYGPyEbREICJRwFigF5AEzBWRGaq6LFe7SriZz34NVixe2ZJ6iEbVy3N951gAerSyQmVBpwpTprghoXv3Qu/erj6QJQFj8hXMP1HPAFar6lpVPQJMBQbm0e4x4EkgLYixeOakytFc37kJ13duQqMadkkiqDZtgv793YTxzZrBggVWJM6YQghmIqgPbPJbTvKtyyEi7YGGqvp5QW8kIkNEJFFEEpOTkwMfqQl/GRnQrRt8+y08+yzMng1t23odlTFhwbPOYhEpBYwBrjtWW1UdB4wDSEhIsHkSzJ/Wr4eGDV1l0Ndec0Ximjb1Oipjwkowzwg2Aw39lhv41mWrBMQB34nIeuBMYIaIJAQxJlNSZGS48tCtW7sqoQA9e1oSMOYEBPOMYC7QXESa4BLAFcCg7I2qugeomb0sIt8B95SkUUMmSBYvdkXiEhNh4EC45BKvIzImrAXtjEBVM4DhwJfAcmCaqi4VkUdFZECw9mtKuJdfhg4d3LwB770HH30E9ep5HZUxYS2ofQSqOhOYmWvdw/m07RbMWEyYyy4HERfnKoQ++yzUrHns1xljjsnuLDbF24EDbrKY0qXhqafc9JHnnON1VMaUKHarqym+Zs2Cdu1ciejDh91ZgTEm4CwRmOInNdVVCe3Z050J/PADvPCCFYkzJkgsEZjiZ/t2Vyfo/vth0SLo0sXriIwp0ayPwBQP2V/+t98OLVu6G8WsM9iYkLAzAuMtVVciuk0buO8+WLXKrbckYEzIWCIw3tm4ES64AK6+2p0FLFwIzZsf+3XGmICyS0PGG9lF4nbscB3Bw4a5eYSNMSFnicCE1tq10LixGw30+utw8skQG+t1VMZENLs0FGBZWcqW1ENsST3E4YxMr8MpPjIy4MknXV/A2LFuXY8elgSMKQbsjCDAnvl6BWO/XZOzfHYz6/Rk4UJXJG7+fLjoIrj0Uq8jMsb4sUQQYMn7DlM5ujQPXdAagA6Nq3sckcdeegnuvBNq1IAPPrBKocYUQ5YIgqBCudJcfnojr8PwVnaRuFNOcVNHjhkD1SM8KRpTTFkiMIG1fz889BCUKeMmjrEiccYUe9ZZbALnq69cmegXX4T0dCsSZ0yYsERgii4lBa6/Hnr3huhoVyTu+eetSJwxYcISgSm6HTtcR/D//Z8bIXT22V5HZIw5DtZHYE7Mtm0wZYobEZRdJK5GDa+jMsacAEsEAfD54q3cPnUBmaqoQv2qMV6HFDyq8NZbLgEcPAj9+rn6QJYEjAlblggCYG3yfjKylOHdm1FK4JQGVb0OKTjWr4ebbnKdwp07w/jxViTOmBLAEsEJysjMYu76FA5nZLJu5wEA7ujZnNJRJbTbJSMDuneHnTtdiYihQ6FUCT1WYyKMJYITNOuPHdw0eV7OcoWyUZQqiVuAieIAABV1SURBVKNkVq+GJk1ckbgJE6BpU1c0zhhTYlgiOEGHjriCcmMHtadu1WjqVI6mVKkSlAjS0+Gpp2DkSPfvbbe5MwJjTIljiaCI2tSrTJOaFbwOI7Dmz3dF4hYudAXiLr/c64iMMUFkF3nN0V54Ac44ww0PnT4dpk2DOnW8jsoYE0SWCIyTXQ7itNPgmmtg2TJXMtoYU+LZpaFIt2+fuyO4XDl45hno0sU9jDERw84IItl//+uKxL38sjsjsCJxxkQkOyM4Tlv3HOLA4Qy27U3zOpQTt2sX3HWXu0O4dWuYPRs6dfI6KmOMRywRHIfVO/bRc8wPR60rVzoMT6p27YKPPoJ//cvNHVCunNcRGWM8FNREICJ9gOeBKGC8qo7Otf0u4AYgA0gG/qGqG4IZU1GkHkwH4JbuJ9PqpMpUr1CWeuFSV2jrVnjnHbj7bmjRAjZsgGrVvI7KGFMMBO3PWRGJAsYC5wNtgCtFpE2uZguABFU9BfgA+E+w4gmkM5vWoP+p9egcDhPTq7o7glu3dmcAq1e79ZYEjDE+wbyucQawWlXXquoRYCow0L+Bqn6rqgd9i3OABkGMJ/KsWwfnneduDjv1VFi0yIrEGWP+IpiXhuoDm/yWk4COBbQfDHyR1wYRGQIMAWjUKMInhS+sjAw491zXH/DKKzBkiBWJM8bkqVh0FovI34EEoGte21V1HDAOICEhwcY4FmTVKlcYrnRpePNNOPlkaNjQ66iMMcVYMP9E3Az4fwM18K07ioj0BB4CBqjq4SDGU7Klp8OoUe6+gJdecuu6dbMkYIw5pmCeEcwFmotIE1wCuAIY5N9ARE4DXgP6qOqOIMZy3DKzlPTMrKPWHcnIyqe1xxITXT/A4sVwxRVw5ZVeR2SMCSNBSwSqmiEiw4EvccNHJ6jqUhF5FEhU1RnAU0BF4H1xtfw3quqAYMV0PHo/9wOrd+zPc1tUcSo3/fzz7uawk06CTz6BAcXi4zPGhJGg9hGo6kxgZq51D/s97xnM/RfFxl0H6dikOt1a1j5qfYVyUXRoXAyGXqqCCCQkuLOB//wHqpbQKTKNMUFVLDqLi6v2jatxc7eTvQ7jaHv3wv33Q3Q0PPusmzu4c2evozLGhDEbTxhOZs6Etm1h3Dg3KsiKxBljAsASQTjYuRP+/ne44AKoUgV+/tlNH1kS50g2xoScXRryM31+Ej+v2QVAelYxGiGUkgKffgqPPAIPPghly3odkTGmBLFE4Oelb1ezJfUQNSqUo37VGOIbetj5unmzKxJ3772uLMSGDdYZbIwJCksEufRsXYeXBrX3LgBVGD8e7rnH3SR28cXQrJklAWNM0ER8Inhh1ire+mU9ALsPHKFtvSreBbNmDdx4I3z7rbsr+PXXXRIwxpggivhEMG9DCqrQJ+4kAC46rb43gWRkQI8esHs3vPYa3HCDFYkzxoRExCcCgAbVy/P4Re282fmKFa4wXOnSMGmSe97AqnEbY0LH/uT0ypEjMHIktGsHY8e6dV27WhIwxoScnRF44bffXFmIJUtg0CC46iqvIzLGRDA7Iwi1556DTp3+vDfgnXegZhhMeWmMKbEsEYRKdjmIM85wI4OWLoV+/byNyRhjsEtDwbdnD9x3H8TEuLOBs85yD2OMKSYiMhF8vWw7L327GlRZm3yAprUrBmdHn34KQ4fCtm3uBrHs0tHGGFOMROSloe9X7mDZlj1Uq1CWDrHVuOL0AE/nmJzsOoEHDIAaNWDOHHjySUsCxphiKSLPCAAqR5dh4vVnBOfN9+xxJaNHjoQHHrAiccaYYi1iE0HAbdoEb7/tvvibNXNF4qp4WK7CGGMKKSIvDQVUVha8+qqbMGbUKFcvCCwJGGPCRsScEexLS2fW8h1kZClrdhwIzJuuWuWGgn7/vasTNG4cNG0amPc2xpgQiZhE8MG8JEZ+uixnuUWdIo4UysiAXr0gNRXeeAOuv946g40xYSliEsGRDDfj2Fd3nkNMmShqVDzBDtzly91EMaVLw+TJrkhcvXoBjNSUJOnp6SQlJZGWluZ1KCZCREdH06BBA8qUKVPo10RMIsjWoFoM5cuewGEfPgxPPOEeTz0Fd9wBXboEPkBToiQlJVGpUiViY2MRO2M0Qaaq7Nq1i6SkJJo0aVLo11lncWHMmQPt28Ojj8KVV8LVV3sdkQkTaWlp1KhRw5KACQkRoUaNGsd9BmqJ4FieecaVhNi3z90b8NZb7iYxYwrJkoAJpRP5fbNEkJ8s16dAp06uTMSSJXD++d7GZIwxQWCJILfUVDdXwO23u+WzzoKXX4bKlb2Ny5gTFBUVRXx8PHFxcVx66aUcPHgw5DF8/PHHLFu27NgNT9CCBQsYPHjwUesuvPBCzjzzzKPWXXfddXzwwQdHratY8c8RhCtXrqRv3740b96c9u3bc9lll7F9+/YixbZ792569epF8+bN6dWrFykpKX9p8+233xIfH5/ziI6O5uOPPwZg1qxZtG/fnvj4eM4++2xWr14NwEsvvcSECROKFFs2SwT+Pv4Y2rRxU0ZWqvRn6WhjwlhMTAwLFy5kyZIllC1blldffbVQr8vIyAhYDAUlgkDs54knnuC2227LWU5NTWXevHns2bOHtWvXFuo90tLSuOCCC7j55ptZtWoV8+fPZ9iwYSQnJxcpttGjR9OjRw9WrVpFjx49GD169F/adO/enYULF7Jw4UL+97//Ub58ec477zwAbr75Zt555x0WLlzIoEGDGDVqFAD/+Mc/ePHFF4sUW7aIGzWUpx07YPhweP99iI+Hzz5zncPGBNDIT5eybMvegL5nm3qVeaR/20K379KlC4sXL+bAgQPceuutLFmyhPT0dEaMGMHAgQOZOHEi06dPZ//+/WRmZvL5559z6623kpiYiIjwyCOPcMkll/DVV1/xyCOPcPjwYU4++WTefPNNKlasSGxsLJdddhlffPEFMTExvPvuu+zYsYMZM2bw/fffM2rUKD788EMGDx5MfHw8P/30E1deeSXx8fHcc889ZGRkcPrpp/PKK69Qrlw5YmNjufbaa/n0009JT0/n/fffp1WrVkcd0759+1i8eDGnnnpqzrrp06fTv39/6tSpw9SpU3nwwQeP+dm8++67dOrUif79++es69atW6E/2/x88sknfPfddwBce+21dOvWjSeffDLf9h988AHnn38+5cuXB9w1/7173e/Nnj17qOcbrl6+fHliY2P57bffOOOMotVNs0QAsHcvfP01PP443HsvHMf4W2PCRUZGBl988QV9+vTh8ccf59xzz2XChAmkpqZyxhln0LNnTwDmz5/P4sWLqV69Ovfffz9VqlTh999/ByAlJYWdO3cyatQovvnmGypUqMCTTz7JmDFjePjhhwFy2r/11lvccccdfPbZZwwYMIB+/frxt7/9LSeeI0eOkJiYSFpaGs2bN2fWrFm0aNGCa665hldeeYU77rgDgJo1azJ//nxefvllnn76acaPH3/UcSUmJhIXF3fUuilTpvDwww9Tp04dLrnkkkIlgiVLltChQ4djttu3bx9d8hk6/u6779KmTZuj1m3fvp26desCcNJJJx3zUtPUqVO56667cpbHjx9P3759iYmJoXLlysyZMydnW0JCAj/++KMlghO2caO7IezBB12RuI0b3eUgY4LkeP5yD6RDhw4RHx8PuDOCwYMHc9ZZZzFjxgyefvppwF0W2bhxIwC9evWievXqAHzzzTdMnTo1572qVavGZ599xrJly+jcuTPgvtA7deqU0+bKK6/M+ffOO+/MN67LL78cgBUrVtCkSRNatGgBuL+ax44dm5MILr74YgA6dOjA9OnT//I+W7dupVatWjnL27dvZ9WqVZx99tmICGXKlGHJkiXExcXlOaLmeEfZVKpUiYULFx7Xa/z3VdD+tm7dyu+//07v3r1z1j377LPMnDmTjh078tRTT3HXXXflJMPatWvzxx9/nFAs/oKaCESkD/A8EAWMV9XRubaXA94COgC7gMtVdX0wYyIry3X+3n+/e3755S4RWBIwJVR2H4E/VeXDDz+kZcuWR63/9ddfqVChQoHvp6r06tWLKVOm5Lnd/4uuoC+9Y+0nW7ly5QDX6Z1Xf0JMTMxR4+anTZtGSkpKzg1Ve/fuZcqUKTz++OPUqFHjqM7a3bt3U9M3Z3jbtm35/vvvjxnP8Z4R1KlTh61bt1K3bl22bt1K7dq1833vadOmcdFFF+XcFZycnMyiRYvo2LEj4JJnnz59ctqnpaURExNzzJiPJWidxSISBYwFzgfaAFeKSJtczQYDKaraDHgWyP/CWQA03ZVEuV494JZb3LDQpUtdEjAmwvTu3ZsXX3wR9Q2IWLBgQZ7tevXqxdixY3OWU1JSOPPMM5k9e3bO6JUDBw6wcuXKnDbvvfdezr/ZZwqVKlVi3759ee6jZcuWrF+/Puf9Jk+eTNeuXQt9LK1bt855LbjLQv/9739Zv34969evZ968eTlnNd26deO9997jyJEjAEycOJHu3bsDMGjQIH7++Wc+//zznPf64YcfWLJkyVH7yz4jyOuROwkADBgwgEmTJgEwadIkBg4cmO+xTJkyJeeMCtwZ2J49e3I+36+//prWrVvnbF+5cuVfLoudEFUNygPoBHzpt/x/wP/lavMl0Mn3vDSwE5CC3rdDhw56Il775g/dVLm2ZlWtqvrmm6pZWSf0PsYcj2XLlnkdglaoUOEv6w4ePKhDhgzRuLg4bdOmjV5wwQWqqvrmm2/qLbfcktNu3759es0112jbtm31lFNO0Q8//FBVVWfNmqUJCQnarl07bdeunX7yySeqqtq4cWO97777tF27dpqQkKCrVq1SVdWffvpJW7durfHx8bp69Wrt2rWrzp07N2c/33zzjcbHx2tcXJxef/31mpaWlvN+ycnJqqo6d+5c7dq1a57HGBcXp3v37tV169ZpvXr1NCvX/+/TTjtN58yZo6qqI0aM0Li4OD311FP14osv1h07duS0W758ufbu3VubNWumrVu31ssvv1y3bdtW+A87Dzt37tRzzz1XmzVrpj169NBdu3blHM/gwYNz2mXHnpmZedTrp0+frnFxcXrKKado165ddc2aNUcd186dO/+yz7x+74BEzed7VTRIQyRF5G9AH1W9wbd8NdBRVYf7tVnia5PkW17ja7Mz13sNAYYANGrUqMOGDRuOO56vlm7j9/c+55YbehPdqMGJHpYxx2X58uVH/QVX0sXGxpKYmJhzuSVUnn32WSpVqsQNN9wQ0v16acGCBYwZM4bJkyf/ZVtev3ciMk9VE/J6r7C4j0BVx6lqgqom+HcKHY/z2p7E3Y8OtiRgTAl088035/QlRIqdO3fy2GOPBeS9gtlZvBnwnxW+gW9dXm2SRKQ0UAXXaWyMCUPr16/3ZL/R0dFcHWHFIHv16hWw9wrmGcFcoLmINBGRssAVwIxcbWYA1/qe/w34nwbrWpUxHrFfaRNKJ/L7FrREoKoZwHBch/ByYJqqLhWRR0VkgK/ZG0ANEVkN3AU8EKx4jPFCdHQ0u3btsmRgQkJ98xFER0cf1+uC1lkcLAkJCZqYmOh1GMYUis1QZkItvxnKCuosjtw7i40JgTJlyhzXTFHGeCEsRg0ZY4wJHksExhgT4SwRGGNMhAu7zmIRSQaO/9ZipyaujEUksWOODHbMkaEox9xYVfO8IzfsEkFRiEhifr3mJZUdc2SwY44MwTpmuzRkjDERzhKBMcZEuEhLBOO8DsADdsyRwY45MgTlmCOqj8AYY8xfRdoZgTHGmFwsERhjTIQrkYlARPqIyAoRWS0if6loKiLlROQ93/ZfRSQ29FEGViGO+S4RWSYii0Vklog09iLOQDrWMfu1u0REVETCfqhhYY5ZRC7z/ayXisi7oY4x0Arxu91IRL4VkQW+3+++XsQZKCIyQUR2+GZwzGu7iMgLvs9jsYi0L/JO85vDMlwfQBSwBmgKlAUWAW1ytRkGvOp7fgXwntdxh+CYuwPlfc9vjoRj9rWrBPwAzAESvI47BD/n5sACoJpvubbXcYfgmMcBN/uetwHWex13EY/5HKA9sCSf7X2BLwABzgR+Leo+S+IZwRnAalVdq6pHgKnAwFxtBgKTfM8/AHqIiIQwxkA75jGr6reqetC3OAc3Y1w4K8zPGeAx4EmgJNSBLswx3wiMVdUUAFXdEeIYA60wx6xAZd/zKsCWEMYXcKr6A7C7gCYDgbfUmQNUFZG6RdlnSUwE9YFNfstJvnV5tlE3gc4eoEZIoguOwhyzv8G4vyjC2TGP2XfK3FBVPw9lYEFUmJ9zC6CFiMwWkTki0idk0QVHYY55BPB3EUkCZgK3hiY0zxzv//djsvkIIoyI/B1IALp6HUswiUgpYAxwncehhFpp3OWhbrizvh9EpJ2qpnoaVXBdCUxU1WdEpBMwWUTiVDXL68DCRUk8I9gMNPRbbuBbl2cbESmNO53cFZLogqMwx4yI9AQeAgao6uEQxRYsxzrmSkAc8J2IrMddS50R5h3Ghfk5JwEzVDVdVdcBK3GJIVwV5pgHA9MAVPUXIBpXnK2kKtT/9+NREhPBXKC5iDQRkbK4zuAZudrMAK71Pf8b8D/19cKEqWMes4icBryGSwLhft0YjnHMqrpHVWuqaqyqxuL6RQaoajjPc1qY3+2PcWcDiEhN3KWitaEMMsAKc8wbgR4AItIalwiSQxplaM0ArvGNHjoT2KOqW4vyhiXu0pCqZojIcOBL3IiDCaq6VEQeBRJVdQbwBu70cTWuU+YK7yIuukIe81NAReB9X7/4RlUd4FnQRVTIYy5RCnnMXwLnicgyIBO4V1XD9my3kMd8N/C6iNyJ6zi+Lpz/sBORKbhkXtPX7/EIUAZAVV/F9YP0BVYDB4Hri7zPMP68jDHGBEBJvDRkjDHmOFgiMMaYCGeJwBhjIpwlAmOMiXCWCIwxJsJZIjCmkEQkU0QW+j1iRaSbiOzxLS8XkUd8bf3X/yEiT3sdvzH5KXH3ERgTRIdUNd5/ha+E+Y+q2k9EKgALReRT3+bs9THAAhH5SFVnhzZkY47NzgiMCRBVPQDMA5rlWn8IWEgRC4MZEyyWCIwpvBi/y0If5d4oIjVwNY2W5lpfDVfv54fQhGnM8bFLQ8YU3l8uDfl0EZEFQBYw2lcCoZtv/SJcEnhOVbeFMFZjCs0SgTFF96Oq9stvvYg0AeaIyDRVXRjq4Iw5Frs0ZEyQ+cpBjwbu9zoWY/JiicCY0HgVOMc3ysiYYsWqjxpjTISzMwJjjIlwlgiMMSbCWSIwxpgIZ4nAGGMinCUCY4yJcJYIjDEmwlkiMMaYCPf/Klpckngz6ycAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q8yIXd0dq4k-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6b3d4947-101c-4c79-911f-12c3934237e9"
      },
      "source": [
        "# Plotting ROC curve for Logistic Regression\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "ax=plt.gca()\n",
        "logreg_disp = plot_roc_curve(logreg,X_test,Y_test,ax=ax)\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "plt.title('Logistic Regression Classifier ROC')\n",
        "plt.plot([0,1],[0,1],'r--')\n",
        "plt.show()"
      ],
      "execution_count": 410,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}