Day 30/ML_ASST_II.ipynb

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      "name": "ML_ASST_II.ipynb",
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      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFNeT85jxnGe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "from sklearn import feature_selection\n",
        "from sklearn import model_selection\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko5SQ2mLxsxi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "53593ce4-d94d-4129-ba01-55f63ec52d99"
      },
      "source": [
        "data_url = \"https://raw.githubusercontent.com/shala2020/shala2020.github.io/master/Lecture_Materials/Assignments/MachineLearning/L2/titanic.csv\"\n",
        "df = pd.read_csv(data_url)\n",
        "df"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\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",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0sDzUfbxwQB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "28382998-28d0-46cd-9990-82232db5de62"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwCFylsQyRLY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "239ef172-05db-4719-8721-25958b9efe43"
      },
      "source": [
        "'''\n",
        "We have high number of null values in Age and Cabin columns\n",
        "'''\n",
        "print(df.isna().sum())"
      ],
      "execution_count": 108,
      "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": "AxH9DSSWyelA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "outputId": "a5af1d54-6326-4877-d2bf-3d7c4630656d"
      },
      "source": [
        "plt.figure(figsize=(50,20))\n",
        "sns.countplot(x='Age', data=df)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5943187c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 3600x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rk_bdKIGyqkL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "6b9bab15-f0d5-4444-869c-22d3943f3cca"
      },
      "source": [
        "# We can observe an almost normal distribution\n",
        "df.hist(column=\"Age\")"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5942f13b70>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        },
        {
          "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": "w2-bE2skzm43",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "bd143ff7-fd80-4546-d03f-969d75a81717"
      },
      "source": [
        "df.Name"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                Braund, Mr. Owen Harris\n",
              "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
              "2                                 Heikkinen, Miss. Laina\n",
              "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
              "4                               Allen, Mr. William Henry\n",
              "                             ...                        \n",
              "886                                Montvila, Rev. Juozas\n",
              "887                         Graham, Miss. Margaret Edith\n",
              "888             Johnston, Miss. Catherine Helen \"Carrie\"\n",
              "889                                Behr, Mr. Karl Howell\n",
              "890                                  Dooley, Mr. Patrick\n",
              "Name: Name, Length: 891, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Yq89RD8E4d_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "55fd2507-cb00-4efe-9c9c-f16c5587e35f"
      },
      "source": [
        "# Make title separately\n",
        "df['Title'] = df.Name.apply(lambda name: name.split(',')[1].split('.')[0].strip())\n",
        "df.Title.value_counts()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr              517\n",
              "Miss            182\n",
              "Mrs             125\n",
              "Master           40\n",
              "Dr                7\n",
              "Rev               6\n",
              "Mlle              2\n",
              "Major             2\n",
              "Col               2\n",
              "Sir               1\n",
              "Ms                1\n",
              "Jonkheer          1\n",
              "Don               1\n",
              "Lady              1\n",
              "Mme               1\n",
              "Capt              1\n",
              "the Countess      1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_TmFFN1NQwi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "408f47eb-b1fb-4800-fc67-023988a9651e"
      },
      "source": [
        "# Let us normalize the titles properly\n",
        "normalize_titles = {\n",
        "    \"Capt\":       \"Army\",\n",
        "    \"Col\":        \"Army\",\n",
        "    \"Major\":      \"Army\",\n",
        "    \"Jonkheer\":   \"Monarch\",\n",
        "    \"Don\":        \"Monarch\",\n",
        "    \"Sir\" :       \"Monarch\",\n",
        "    \"Dr\":         \"Army\",\n",
        "    \"Rev\":        \"Army\",\n",
        "    \"the Countess\":\"Monarch\",\n",
        "    \"Dona\":       \"Monarch\",\n",
        "    \"Mme\":        \"Mrs\",\n",
        "    \"Mlle\":       \"Miss\",\n",
        "    \"Ms\":         \"Mrs\",\n",
        "    \"Mr\" :        \"Mr\",\n",
        "    \"Mrs\" :       \"Mrs\",\n",
        "    \"Miss\" :      \"Miss\",\n",
        "    \"Master\" :    \"Master\",\n",
        "    \"Lady\" :      \"Monarch\"\n",
        "}\n",
        "df.Title = df.Title.map(normalize_titles)\n",
        "# view value counts for the normalized titles\n",
        "print(df.Title.value_counts())"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mr         517\n",
            "Miss       184\n",
            "Mrs        127\n",
            "Master      40\n",
            "Army        18\n",
            "Monarch      5\n",
            "Name: Title, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuWqMajPPAFv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "11534a3f-f7d4-457b-dd0f-a8b93acf23fa"
      },
      "source": [
        "corr = df.corr()\n",
        "sns.heatmap(corr, cmap=\"viridis\")"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5942e95fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yK7XLPTdPQ76",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "786d779d-0384-42fd-a514-03d093855677"
      },
      "source": [
        "groups = df.groupby(['Sex','Pclass', 'Title'])  \n",
        "\n",
        "# view the median Age by the grouped features \n",
        "print(groups.Age.median())\n",
        "df.Age = groups.Age.apply(lambda x: x.fillna(x.median()))\n",
        "df"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sex     Pclass  Title  \n",
            "female  1       Army       49.0\n",
            "                Miss       30.0\n",
            "                Monarch    40.5\n",
            "                Mrs        40.0\n",
            "        2       Miss       24.0\n",
            "                Mrs        31.5\n",
            "        3       Miss       18.0\n",
            "                Mrs        31.0\n",
            "male    1       Army       51.0\n",
            "                Master      4.0\n",
            "                Monarch    40.0\n",
            "                Mr         40.0\n",
            "        2       Army       46.5\n",
            "                Master      1.0\n",
            "                Mr         31.0\n",
            "        3       Master      4.0\n",
            "                Mr         26.0\n",
            "Name: Age, dtype: float64\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>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",
              "      <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>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</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",
              "      <td>Mrs</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",
              "      <td>Miss</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",
              "      <td>Mrs</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",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Army</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0              1         0       3  ...   NaN        S     Mr\n",
              "1              2         1       1  ...   C85        C    Mrs\n",
              "2              3         1       3  ...   NaN        S   Miss\n",
              "3              4         1       1  ...  C123        S    Mrs\n",
              "4              5         0       3  ...   NaN        S     Mr\n",
              "..           ...       ...     ...  ...   ...      ...    ...\n",
              "886          887         0       2  ...   NaN        S   Army\n",
              "887          888         1       1  ...   B42        S   Miss\n",
              "888          889         0       3  ...   NaN        S   Miss\n",
              "889          890         1       1  ...  C148        C     Mr\n",
              "890          891         0       3  ...   NaN        Q     Mr\n",
              "\n",
              "[891 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iN053_UHP2wL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "c1b6bab4-9d52-4efe-c44f-b27735272498"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTMfDVqpW0yl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "fee688b0-6887-4de0-c0dd-d3c127dc096d"
      },
      "source": [
        "df.Age.value_counts()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.00    108\n",
              "18.00     59\n",
              "40.00     41\n",
              "31.00     35\n",
              "24.00     32\n",
              "        ... \n",
              "55.50      1\n",
              "66.00      1\n",
              "70.50      1\n",
              "23.50      1\n",
              "0.42       1\n",
              "Name: Age, Length: 88, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQZto6SYXQMe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "6d398ad0-2c5c-43cd-a0f2-1b6c30e76ca6"
      },
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.boxplot(df.Age)\n",
        "plt.show()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1yMG9siXYoO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "9688d80b-806f-4907-ff05-4ea55176461e"
      },
      "source": [
        "df.Embarked.value_counts()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghsF6zutYDPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97305ebb-c1d6-4bd3-b7ab-cf9884e58eab"
      },
      "source": [
        "df[\"Embarked\"].isna().sum()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLrem20mYIRv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "6be2b7ba-e7b7-4b97-8720-1513262b52ec"
      },
      "source": [
        "highest_count = df.Embarked.value_counts().index[0]\n",
        "df.Embarked = df.Embarked.fillna(highest_count)\n",
        "df.Embarked.value_counts()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    646\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3d2IiGibYgGP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c3b0aeba-7eec-444d-e147-125e34187e35"
      },
      "source": [
        "df.Fare.isna().sum()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gp7Fd7p9Y7yx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "9c9905c9-1b12-4c89-c658-12b283f24894"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 13 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          891 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     891 non-null    object \n",
            " 12  Title        891 non-null    object \n",
            "dtypes: float64(2), int64(5), object(6)\n",
            "memory usage: 90.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7NDexMxZBYT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9823fcab-95eb-4134-e233-4b0ce699016c"
      },
      "source": [
        "df.Cabin.isna().sum()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "687"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4i9QdMs9ZJae",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.Cabin = df.Cabin.fillna('U')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Te_lJMBZK9G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "58e86a86-8553-4626-933a-700ea614cc99"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 13 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          891 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        891 non-null    object \n",
            " 11  Embarked     891 non-null    object \n",
            " 12  Title        891 non-null    object \n",
            "dtypes: float64(2), int64(5), object(6)\n",
            "memory usage: 90.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVflb5J8ZPNL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "12038479-3585-496d-840c-a0cb4000da13"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 127,
      "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",
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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>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",
              "      <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>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>U</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</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",
              "      <td>Mrs</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>U</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</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",
              "      <td>Mrs</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>U</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0            1         0       3  ...     U        S     Mr\n",
              "1            2         1       1  ...   C85        C    Mrs\n",
              "2            3         1       3  ...     U        S   Miss\n",
              "3            4         1       1  ...  C123        S    Mrs\n",
              "4            5         0       3  ...     U        S     Mr\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD4RwLrdvDkN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "be2ba526-56c5-4ed6-ca51-d1e8f4677360"
      },
      "source": [
        "label = LabelEncoder()\n",
        "df[\"Sex_Code\"] = label.fit_transform(df['Sex'])\n",
        "df[\"Embarked_Code\"] = label.fit_transform(df[\"Embarked\"])\n",
        "df['Title_Code'] = label.fit_transform(df['Title'])\n",
        "df['Cabin'] = label.fit_transform(df['Cabin'])\n",
        "y = df['Survived']\n",
        "df.columns\n",
        "final = df[['PassengerId', 'Pclass', 'Age', 'SibSp',\n",
        "       'Parch', 'Fare', 'Cabin', 'Sex_Code',\n",
        "       'Embarked_Code', 'Title_Code']]\n",
        "final"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
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              "      <td>3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>147</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</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>55</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <td>3</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>147</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>2</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>147</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>147</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>3</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>147</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass   Age  ...  Sex_Code  Embarked_Code  Title_Code\n",
              "0              1       3  22.0  ...         1              2           4\n",
              "1              2       1  38.0  ...         0              0           5\n",
              "2              3       3  26.0  ...         0              2           2\n",
              "3              4       1  35.0  ...         0              2           5\n",
              "4              5       3  35.0  ...         1              2           4\n",
              "..           ...     ...   ...  ...       ...            ...         ...\n",
              "886          887       2  27.0  ...         1              2           0\n",
              "887          888       1  19.0  ...         0              2           2\n",
              "888          889       3  18.0  ...         0              2           2\n",
              "889          890       1  26.0  ...         1              0           4\n",
              "890          891       3  32.0  ...         1              1           4\n",
              "\n",
              "[891 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M2EeMkmFy039",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(final, y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjM4uS5g0Zw3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "outputId": "2bc62720-6580-44f6-906c-1181987920ea"
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "plt.boxplot(x=df[\"Fare\"], showmeans=True, meanline=True)\n",
        "plt.title(\"Fare Boxplot\")\n",
        "plt.ylabel('Fare')\n",
        "plt.show()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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okNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDAADo0NBir6omqur/qqr/p6o+XVX/YrD+vVX1yao6WlW/W1VbBuvjg/dHB/uvHtZsAAAAvRvmmb3TSV7dWvuBJD+Y5Mer6hVJfj3Jb7TWXpzka0l2D47fneRrg/XfGBwHAADAGgwt9toZ3xy8vWTwakleneQDg/Vbk7xhsL1r8D6D/a+pqhrWfAAAAD0b6j17VbWpqv4syVeS3JHkL5Mcb619a3DIvUmuHGxfmeSLSTLYfyLJ84c5HwAAQK+GGnuttUdaaz+Y5KokP5Lk+7/dz6yqN1fVQlUtLC4uftszAgAA9OiCPI2ztXY8yZ1J/k6Sy6pq82DXVUm+NNj+UpIXJslg/3OTfPUcn/W+1tpUa21q27ZtQ58dAABgIxrm0zi3VdVlg+3nJHltkntyJvr+0eCwNyU5ONieH7zPYP9HWmttWPMBAAD0bPPTH7Jm35Pk1qralDNR+XuttT+oqs8k+U9V9atJ/jTJLYPjb0ny76vqaJIHkvzMEGcDAADo2tBir7V2OMkPnWP9szlz/94T108l+alhzQMAAHAxuSD37AEAAHBhiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AAIAOiT0AWIPJyclU1fJrcnJy1CMBwApiDwBWaXJyMkeOHMnY2Jl/RsfGxnLkyBHBB8C6IvYAYJWOHDmSqspNN92UpaWl3HTTTamqHDlyZNSjAcAysQcAa/COd7wjN9xwQy699NLccMMNecc73jHqkQBgBbEHAGvwyU9+8rzvAWDUnnHsVdWrquoXBtvbqup7hzcWAKxfVZXbb789u3btyrFjx7Jr167cfvvtqapRjwYAy55R7FXVryTZm+Rtg6VLkvyHYQ0FAOvZW97yliTJ/Px8tm3blvn5+RXrALAebH6Gx/2DJD+U5E+SpLV2X1V959CmAoB17MCBA0mS97///Tl9+nTGx8ezZ8+e5XUAWA+eaew91FprVdWSpKq2DnEmAFj3Dhw4IO4AWNee6T17v1dV/ybJZVW1J8n/keT9wxsLAACAb8fTntmrM3eb/26S70/y9SR/M8kvt9buGPJsAAAArNHTxt7g8s3bW2s7kgg8AACADeCZXsb5J1X1t4c6CQAAAM+aZ/qAlh9N8nNV9fkkS0kqZ076TQ5tMgAAANbsmcbe9FCnAAAA4Fn1jGKvtfb5JKmq70oyMdSJAAAA+LY9o3v2qmpnVf1Fkv8vyceSfC7JHw5xLgAAAL4Nz/QBLf9Tklck+fPW2vcmeU2STwxtKgAAAL4tzzT2Hm6tfTXJWFWNtdbuTDI1xLkAAAD4NjzTB7Qcr6rvSPLHSf5jVX0lZ57KCQAAwDp03jN7VfWiweauJCeT/A9J/ijJXyZ5/XBHAwAAYK2e7szeB5P8cGttqap+v7X23ya59QLMBQAAwLfh6e7Zq7O2/8YwBwEAAODZ83Sx155iGwAuarOzs5mYmEhVZWJiIrOzs6MeCQBWeLrY+4Gq+npVfSPJ5GD761X1jar6+oUYEADWm9nZ2czNzWX//v1ZWlrK/v37Mzc3J/gAWFeqtY17wm5qaqotLCyMegwALjITExPZv39/brjhhuW1m2++Ofv27cupU6dGOBkAF5uquru1ds4/iyf2AGCVqipLS0u59NJLl9dOnjyZrVu3ZiP/uwrAxnO+2Humf1QdABgYHx/P3NzcirW5ubmMj4+PaCIAeLJn+kfVAYCBPXv2ZO/evUmSmZmZzM3NZe/evZmZmRnxZADwOLEHAKt04MCBJMm+ffty4403Znx8PDMzM8vrALAeuGcPAABgg3LPHgAAwEVG7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AEAAHRI7AHAGkxOTqaqll+Tk5OjHgkAVhB7ALBKk5OTOXLkSHbu3JnFxcXs3LkzR44cEXwArCtiDwBW6bHQO3jwYK644oocPHhwOfgAYL0QewCwBrfccst53wPAqIk9AFiD3bt3n/c9AIya2AOAVdqxY0fm5+eza9euHDt2LLt27cr8/Hx27Ngx6tEAYNnmUQ8AABvN4cOHMzk5mfn5+Wzbti3JmQA8fPjwiCcDgMeJPQBYA2EHwHrnMk4AWIPbbrstL3/5y7Np06a8/OUvz2233TbqkQBgBWf2AGCVbrvttrz97W/PLbfckle96lX5+Mc/vvyAlp/92Z8d8XQAcEa11kY9w5pNTU21hYWFUY8BwEXm5S9/eQ4cOJAf+7EfW1678847Mzs7m0996lMjnAyAi01V3d1amzrnPrEHAKuzadOmnDp1Kpdccsny2sMPP5yJiYk88sgjI5wMgIvN+WLPPXsAsErXXnttPv7xj69Y+/jHP55rr712RBMBwJOJPQBYpbe//e3ZvXt37rzzzjz88MO58847s3v37rz97W8f9WgAsMwDWgBglR57CMvs7GzuueeeXHvttfm1X/s1D2cBYF1xzx4AAMAG5Z49AACAi4zYAwAA6JDYAwAA6JDYAwAA6JDYAwAA6JDYAwAA6JDYAwAA6JDYA4A1mJycTFUtvyYnJ0c9EgCsIPYAYJUmJydz5MiR7Ny5M4uLi9m5c2eOHDki+ABYV8QeAKzSY6F38ODBXHHFFTl48OBy8AHAeiH2AGANbrnllvO+B4BRE3sAsAa7d+8+73sAGDWxBwCrtGPHjszPz2fXrl05duxYdu3alfn5+ezYsWPUowHAss2jHgAANprDhw9ncnIy8/Pz2bZtW5IzAXj48OERTwYAjxN7ALAGwg6A9c5lnAAAAB0SewAAAB0SewAAAB0SewAAAB0SewAAAB0SewAAAB0SewAAAB0SewAAAB0aWuxV1Qur6s6q+kxVfbqq/tlg/XlVdUdV/cXg5+WD9aqq36qqo1V1uKp+eFizAQAA9G6YZ/a+leTG1tpLk7wiyVuq6qVJfjHJh1trL0ny4cH7JPmJJC8ZvN6c5L1DnA0AAKBrQ4u91tpftdb+ZLD9jST3JLkyya4ktw4OuzXJGwbbu5L8djvjE0kuq6rvGdZ8AAAAPbsg9+xV1dVJfijJJ5Nsb6391WDXl5NsH2xfmeSLZ/3avYO1J37Wm6tqoaoWFhcXhzYzAADARjb02Kuq70jy+0n+eWvt62fva621JG01n9dae19rbaq1NrVt27ZncVIAAIB+DDX2quqSnAm9/9ha+98Hy/c/dnnm4OdXButfSvLCs379qsEaAAAAqzTMp3FWkluS3NNau/msXfNJ3jTYflOSg2et//zgqZyvSHLirMs9AQAAWIXNQ/zsVyZ5Y5IjVfVng7V9Sf5Vkt+rqt1JPp/kpwf7bk/yk0mOJjmZ5BeGOBsAAEDXhhZ7rbWPJ6mn2P2acxzfkrxlWPMAAABcTC7I0zgBAAC4sMQeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAKzB7OxsJiYmUlWZmJjI7OzsqEcCgBXEHgCs0uzsbObm5rJ///4sLS1l//79mZubE3wArCvVWhv1DGs2NTXVFhYWRj0GABeZiYmJ7N+/PzfccMPy2s0335x9+/bl1KlTI5wMgItNVd3dWps65z6xBwCrU1VZWlrKpZdeurx28uTJbN26NRv531UANp7zxZ7LOAFglcbHxzM3N7dibW5uLuPj4yOaCACebPOoBwCAjWbPnj3Zu3dvkmRmZiZzc3PZu3dvZmZmRjwZADxO7AHAKh04cCBJsm/fvtx4440ZHx/PzMzM8joArAfu2QMAANig3LMHAABwkRF7AAAAHRJ7AAAAHRJ7AAAAHRJ7AAAAHRJ7AAAAHRJ7AAAAHRJ7AAAAHRJ7AAAAHRJ7ALAG09PTGRsbS1VlbGws09PTox4JAFYQewCwStPT0zl06FBmZmZy/PjxzMzM5NChQ4IPgHWlWmujnmHNpqam2sLCwqjHAOAiMzY2lssvvzwPPPDA8trznve8fO1rX8ujjz46wskAuNhU1d2ttalz7XNmDwBWqbWWBx54IDt37szi4mJ27tyZBx54IBv5f6AC0B+xBwBrcPXVV+fgwYO54oorcvDgwVx99dWjHgkAVhB7ALAGn/vc53L99dfnxIkTuf766/O5z31u1CMBwAru2QOAVaqqXHHFFfnqV7+a1lqqKs9//vNz7Ngxl3ICcEG5Zw8AnkU7duzIsWPH8vrXvz6Li4t5/etfn2PHjmXHjh2jHg0Alm0e9QAAsNEcPnw4k5OTmZ+fz7Zt25KcCcDDhw+PeDIAeJzYA4A1EHYArHcu4wSANZidnc3ExESqKhMTE5mdnR31SACwgtgDgFWanZ3N3Nxc9u/fn6Wlpezfvz9zc3OCD4B1xdM4AWCVJiYmsn///txwww3LazfffHP27duXU6dOjXAyAC4253sap9gDgFWqqiwtLeXSSy9dXjt58mS2bt3qTy8AcEH50wsA8CwaHx/P3NzcirW5ubmMj4+PaCIAeDJP4wSAVdqzZ0/27t2bJJmZmcnc3Fz27t2bmZmZEU8GAI8TewCwSgcOHEiS7Nu3LzfeeGPGx8czMzOzvA4A64F79gAAADYo9+wBAABcZMQeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAABAh8QeAKzB9PR0xsbGUlUZGxvL9PT0qEcCgBXEHgCs0vT0dA4dOpSZmZkcP348MzMzOXTokOADYF2p1tqoZ1izqamptrCwMOoxALjIjI2N5aUvfWmOHj2a06dPZ3x8PC9+8ToR+rcAAA/8SURBVIvzmc98Jo8++uioxwPgIlJVd7fWps61b/OFHgYANrrWWj796U9n06ZNSZJvfetb+fSnPz3iqQBgJZdxAsAavfOd78zS0lLe+c53jnoUAHgSsQcAa3T06NE8/PDDOXr06KhHAYAncc8eAKxSVeXFL35x/vIv/zKttVRVvu/7vi9Hjx7NRv53FYCN53z37DmzBwCrtGnTpnz2s5/Nu971riwtLeVd73pXPvvZzy7fwwcA64HYA4BVuu6669Jay1vf+tZs3bo1b33rW9Nay3XXXTfq0QBgmadxAsAqHThwIEny/ve/P4888kg2b96cPXv2LK8DwHrgnj0AAIANyj17AAAAFxmxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwAA0CGxBwBrMDk5mapafk1OTo56JABYQewBwCpNTk7myJEj2blzZxYXF7Nz584cOXJE8AGwrog9AFilx0Lv4MGDueKKK3Lw4MHl4AOA9ULsAcAa3HLLLed9DwCjJvYAYA1279593vcAMGpiDwBWaceOHZmfn8+uXbty7Nix7Nq1K/Pz89mxY8eoRwOAZZtHPQAAbDSHDx/O5ORk5ufns23btiRnAvDw4cMjngwAHif2AGANhB0A653LOAEAADok9gAAADok9gBgDaanpzM2NpaqytjYWKanp0c9EgCsIPYAYJWmp6dz6NChzMzM5Pjx45mZmcmhQ4cEHwDrige0AMAq3XHHHbnuuuvynve8J0mWf87NzY1yLABYQewBwCq11vLRj340VbW8du2116a1NsKpAGAll3ECwBrcc8892blzZxYXF7Nz587cc889ox4JAFYQewCwRldeeWUuueSSXHnllaMeBQCepDbyJSdTU1NtYWFh1GMAcJGpqrzgBS/Ifffdt7z22PuN/O8qABtPVd3dWps61z5n9gBgDe677768+93vztLSUt797nevCD8AWA/EHgCs0cc+9rGcPHkyH/vYx0Y9CgA8iadxAsAavPSlL838/Hy2bdu2/P4zn/nMiKcCgMc5swcAqzQ+Pp7LL7884+Pj53wPAOuB2AOAVbrmmmty1113ZXp6OouLi5mens5dd92Va665ZtSjAcAyl3ECwCr9+Z//eV75ylfmQx/6ULZt25bx8fG88pWvjCdEA7CeiD0AWKXTp0/n0KFDufTSS5fXTp48ma1bt45wKgBYyWWcALBK4+PjmZubW7E2Nzfnnj0A1hVn9gBglfbs2ZO9e/cmSWZmZjI3N5e9e/dmZmZmxJMBwOPEHgCs0oEDB5Ik+/bty4033pjx8fHMzMwsrwPAelCttVHPsGZTU1PNzfAAAMDFqqrubq1NnWufe/YAAAA6NLTYq6r/taq+UlWfOmvteVV1R1X9xeDn5YP1qqrfqqqjVXW4qn54WHMBAABcDIZ5Zu/fJfnxJ6z9YpIPt9ZekuTDg/dJ8hNJXjJ4vTnJe4c4FwAAQPeGFnuttT9O8sATlncluXWwfWuSN5y1/tvtjE8kuayqvmdYswEAAPTuQt+zt7219leD7S8n2T7YvjLJF8867t7B2pNU1ZuraqGqFhYXF4c3KQAAwAY2sge0tDOPAV31o0Bba+9rrU211qa2bds2hMkAAAA2vgsde/c/dnnm4OdXButfSvLCs467arAGAADAGlzo2JtP8qbB9puSHDxr/ecHT+V8RZITZ13uCQAAwCptHtYHV9VtSf5ukiuq6t4kv5LkXyX5varaneTzSX56cPjtSX4yydEkJ5P8wrDmAgAAuBgMLfZaaz/7FLtec45jW5K3DGsWAACAi83IHtACABvZ7OxsJiYmUlWZmJjI7OzsqEcCgBXEHgCs0uzsbObm5rJ///4sLS1l//79mZubE3wArCt15grKjWlqaqotLCyMegwALjITExPZv39/brjhhuW1m2++Ofv27cupU6dGOBkAF5uquru1NnXOfWIPAFanqrK0tJRLL710ee3kyZPZunVrNvK/qwBsPOeLPZdxAsAqjY+P53Wve92Ke/Ze97rXZXx8fNSjAcAysQcAq3TNNdfkrrvuyvT0dBYXFzM9PZ277ror11xzzahHA4BlLuMEgFWamJjI2NhYHnzwweW15zznOXn00UfdswfABXW+yziH9nf2AKBXp0+fTpKMjY3l0UcffVL4AcB64DJOAFijm266KUtLS7nppptGPQoAPInYA4A1qKrzvgeAUXPPHgCsUlXlRS96Ue6///6cPn064+Pj2b59e77whS/40wsAXFDu2QOAZ1FV5Qtf+MLy+9OnT+cLX/iCs3sArCsu4wSAVbrqqqtWtQ4AoyD2AGCV7r333rzsZS9b/iPq4+PjednLXpZ77713xJMBwONcxgkAq9Ray1133ZXnPve5y2snTpzIZZddNsKpAGAlsQcAq1RVufLKK7O0tLS8tnXrVvfsAbCuuIwTAFZpy5YtWVpayvbt23PPPfdk+/btWVpaypYtW0Y9GgAsc2YPAFbp9OnT2bRpU+6///5ce+21SZJNmzbl9OnTI54MAB7nzB4ArMEjjzyS6667LsePH891112XRx55ZNQjAcAK/qg6AKxSVWX79u358pe/vLz23d/93bn//vv9UXUALih/VB0AnmX333+/B7IAsK65jBMAAKBDYg8AAKBDYg8AAKBDYg8A1mhsbGzFTwBYT/zrBABr9Oijj674CQDridgDAADokNgDAADokNgDAADokNgDAADokNgDAADokNgDgDXYsmXLed8DwKiJPQBYg+3bt+cjH/lIHnrooXzkIx/J9u3bRz0SAKywedQDAMBGdOzYsbz61a9efv+c5zxnhNMAwJM5swcAqzQ+Pp4HH3ww27dvzz333JPt27fnwQcfzPj4+KhHA4BlzuwBwCo99NBDSZL7778/11577ZPWAWA9cGYPAFaptZYkufzyy3P48OFcfvnlK9YBYD1wZg8A1uCSSy7JAw88kCR54IEHsmXLljz88MMjngoAHufMHgCswcMPP5zrr78+J06cyPXXXy/0AFh3aiNfcjI1NdUWFhZGPQYAF5mqesp9G/nfVQA2nqq6u7U2da59zuwBwCo9FnsTExP5xCc+kYmJiRXrALAeuGcPAFZpy5Yteeihh3Lq1Km84hWvSHIm9LZs2TLiyQDgcc7sAcAqnT59Ot/85jfTWlt+ffOb38zp06dHPRoALBN7ALBK4+PjmZubW7E2Nzfnj6oDsK64jBMAVmnPnj3Zu3dvkmRmZiZzc3PZu3dvZmZmRjwZADzO0zgB2NAu9oeibOR/xwH49p3vaZzO7AGwoY06dqpq5DMAwLm4Zw8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8AAKBDYg8A/v/27ubFzvIO4/j1w1DoJmVis6pNcWHFQLCLoaIbIdmkoHQhCXHlQggoyd6Vjv4LvgSyCHbVkOBGS6CLBHRRF0ao+EbbIEjTTdPOQDeFUri7qUOQUeNMmPuc33w+u3OfA3MdZnH48jwzBwAaEnsAAAANiT0AAICGxB4AAEBDYg8AAKAhsQcAANCQ2AMAAGhI7AEAADQk9gAAABoSewAAAA2JPQAAgIbEHgAAQENiDwAAoCGxBwAA0JDYAwAAaGjf7AEALL83/vhGzn10bvPxxScuJklO/e7U5tlzDz+X53/xfI5eOppb/76VJHnowEO59OSlrP1hLW/95a3N1149cTWf/fOznL12dvPsxUdfzImfn8iR3xzZPHv8vsfz2rHXcubqmbx7893N84+f+TiX/3w5r7z/yubZq0dfzeF7D+fY5WObZ0898FTWHlvLyXdO5vP1z5MkB394MNdOXrvj95Sk3Xu6k9/TV68DYHHVGGP2hm1bXV0d169fnz0DgD2sqrLMn6UALLeq+nCMsbrVc67sAbBjBw4cyMbGxuwZ01TV7Am7bmVlJevr67NnAPAtxB4AO7axseHq1h6zFwMXYNn4By0AAAANiT0AAICGxB4AAEBDYg8AAKAhsQcAANCQ2AMAAGhI7AEAADQk9gAAABoSewAAAA2JPQAAgIbEHgAAQENiDwAAoCGxBwAA0JDYAwAAaEjsAQAANCT2AAAAGhJ7AAAADYk9AACAhsQeAABAQ2IPAACgIbEHAADQkNgDAABoaN/sAQAsv/HS/mTtR7NnsIvGS/tnTwDgO4g9AHasXv5XxhizZ7CLqipjbfYKAL6N2zgBAAAaEnsAAAANiT0AAICGxB4AAEBDYg8AAKAhsQcAANCQ2AMAAGjI9+wBcFdU1ewJ7KKVlZXZEwD4DmIPgB3by1+oXlV7+v0DsLjcxgkAANCQ2AMAAGhI7AEAADQk9gAAABoSewAAAA2JPQAAgIbEHgAAQEMLFXtVdbyq/lRVN6rqhdl7AAAAltXCxF5V3ZPk9SS/SnI4ydNVdXjuKgAAgOW0MLGX5JdJbowxvhhj/CfJxSS/nrwJAABgKe2bPeA2P0ny19se30zyyNdfVFWnk5xOkkOHDu3OMgAWVlXNnjB1wxhj2s8GYLEtUuzdkTHG+STnk2R1ddUnHMAeJ3YAYGuLdBvn35L89LbH9/3/DAAAgO9pkWLvgyQPVNX9VfWDJKeSvD15EwAAwFJamNs4xxj/raozSX6f5J4kF8YYn06eBQAAsJQWJvaSZIxxJcmV2TsAAACW3SLdxgkAAMBdIvYAAAAaEnsAAAANiT0AAICGxB4AAEBDYg8AAKAhsQcAANCQ2AMAAGhI7AEAADQk9gAAABoSewAAAA2JPQAAgIbEHgAAQENiDwAAoCGxBwAA0JDYAwAAaEjsAQAANCT2AAAAGhJ7AAAADYk9AACAhsQeAABAQ2IPAACgIbEHAADQkNgDAABoSOwBAAA0JPYAAAAaEnsAAAANiT0AAICGxB4AAEBDYg8AAKAhsQcAANBQjTFmb9i2qrqV5MvZOwDY036c5B+zRwCwZ/1sjHFwqyeWOvYAYLaquj7GWJ29AwC+zm2cAAAADYk9AACAhsQeAOzM+dkDAGAr/mYPAACgIVf2AAAAGhJ7AAAADYk9ANiGqrpQVX+vqk9mbwGArYg9ANieN5Mcnz0CAL6J2AOAbRhjvJdkffYOAPgmYg8AAKAhsQcAANCQ2AMAAGhI7AEAADQk9gBgG6rqt0neT/JgVd2sqmdnbwKA29UYY/YGAAAA7jJX9gAAABoSewAAAA2JPQAAgIbEHgAAQENiDwAAoCGxBwAA0JDYAwAAaOh/8l8StKSgFCgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LU-BZnyCsXw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "outputId": "79666dcf-25cd-4996-e53e-5cd1914733e2"
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "plt.boxplot(x=df[\"Age\"], showmeans=True, meanline=True)\n",
        "plt.title(\"Age Boxplot\")\n",
        "plt.ylabel('Age (Years)')\n",
        "plt.show()"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAMAAOiYqAOAOWzdujWrV6/O0qVLs3r16mzdunXUIwHAwywb9QAAsBht3bo169evz+bNm3Puuedm586dmZycTJJcfPHFI54OAL6jWmujnuGoJiYm2q5du0Y9BgAnkdWrV2fTpk0577zzDi3bsWNHpqamcvvtt49wMgBORlV1S2ttYtZ1og4AHm7p0qXZv39/li9ffmjZgQMHMjY2lgcffHCEkwFwMpov6pxTBwCzGB8fz86dO49YtnPnzoyPj49oIgCYnagDgFmsX78+k5OT2bFjRw4cOJAdO3ZkcnIy69evH/VoAHAEF0oBgFkcvBjK1NRUdu/enfHx8WzYsMFFUgBYdJxTBwAAsMg5pw4AAOAEJeoAYA5r167NkiVLUlVZsmRJ1q5dO+qRAOBhRB0AzGLt2rW56aabsm7duuzduzfr1q3LTTfdJOwAWHRcKAUAZrF9+/a85jWvyTXXXJMkh56np6dHORYAPIwLpQDALKoqe/fuzamnnnpo2X333ZfTTjstPfzbCcCJxYVSAOARqqpceeWVRyy78sorU1UjmggAZifqAGAWa9asybXXXpvLL7889913Xy6//PJce+21WbNmzahHA4AjOPwSAOawdu3abN++Pa21VFXWrFmTj3zkI6MeC4CT0HyHX7pQCgDMQcAB0AOHXwIAAHRM1AEAAHRM1AHAHKampjI2NpaqytjYWKampkY9EgA8jKgDgFlMTU1leno6GzduzL59+7Jx48ZMT08LOwAWHVe/BIBZjI2NZePGjXn9619/aNnVV1+dq666Kvv37x/hZACcjOa7+qWoA4BZVFX27duXU0455dCy+++/PytXrkwP/3YCcGKZL+ocfgkAs1ixYkWmp6ePWDY9PZ0VK1aMaCIAmJ371AHALC699NJcccUVSZJ169Zleno6V1xxRdatWzfiyQDgSKIOAGaxadOmJMlVV12VN7zhDVmxYkXWrVt3aDkALBbOqQMAAFjknFMHAABwghJ1ADAHNx8HoAeiDgBm4ebjAPTCOXUAMAs3HwdgMXHzcQB4hNx8HIDFxIVSAOARcvNxAHrhPnUAMAs3HwegF6IOAGbh5uMA9MI5dQAAAIucc+oAAABOUKIOAOZwzjnnpKoOPc4555xRjwQADyPqAGAW55xzTm677ba8/OUvz549e/Lyl788t912m7ADYNERdQAwi4NBt23btjz+8Y/Ptm3bDoUdACwmog4A5rB58+Z53wPAYiDqAGAOk5OT874HgMVA1AHALJ773OfmxhtvzAUXXJCvfe1rueCCC3LjjTfmuc997qhHA4AjuPk4AMzi1ltvzTnnnJMbb7wxq1atSjITerfeeuuIJwOAI4k6AJiDgAOgBw6/BAAA6JioA4A5rF27NkuWLElVZcmSJVm7du2oRwKAhxF1ADCLtWvX5qabbsq6deuyd+/erFu3LjfddJOwA2DRcU4dAMxi+/btec1rXpNrrrkmSQ49T09Pj3IsAHiYaq2NeoajmpiYaLt27Rr1GACcRKoqe/fuzamnnnpo2X333ZfTTjstPfzbCcCJpapuaa1NzLbO4ZcAMIuqypVXXnnEsiuvvDJVNaKJAGB2og4AZrFmzZpce+21ufzyy3Pffffl8ssvz7XXXps1a9aMejQAOILDLwFgDmvXrs327dvTWktVZc2aNfnIRz4y6rEAOAnNd/ilC6UAwBwEHAA9cPglAABAx0QdAABAx4YadVV1WlV9oKr+sqp2V9WLq+qMqtpeVZ8dPJ8+zBkAAABOZMPeU/cbSf6otfbsJM9LsjvJm5Lc3Fp7ZpKbB+8BAAA4BkOLuqo6Nck/T7I5SVpr326t7U1yQZItg49tSXLhsGYAAAA40Q3z6pdPT7InyW9X1fOS3JLkF5Oc2Vr76uAzdyY5c7YvV9VlSS5LkrPOOmuIYwLQg5P9pt893IIIgNEY5uGXy5J8b5JrW2svSLIvDznUss38CzXrv1KttetaaxOttYlVq1YNcUwAetBaG9lj1NsXdADMZ5hR96UkX2qtfXzw/gOZiby7qupJSTJ4vnuIMwAAAJzQhhZ1rbU7k/xtVX3PYNH5ST6T5MYklwyWXZJk27BmAAAAONEN85y6JJlK8rtV9agkn0/yrzITkjdU1WSSO5JcNOQZAAAATlhDjbrW2qeSTMyy6vxhbhcAAOBkMez71AEAADBEog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjog4AAKBjy4b541X1hSTfSPJgkgdaaxNVdUaS9yU5O8kXklzUWrt3mHMAAACcqI7HnrrzWmvPb61NDN6/KcnNrbVnJrl58B4AAIBjMIrDLy9IsmXwekuSC0cwAwAAwAlh2FHXktxUVbdU1WWDZWe21r46eH1nkjNn+2JVXVZVu6pq1549e4Y8JgAAQJ+Gek5dknNba1+uqick2V5Vf3n4ytZaq6o22xdba9cluS5JJiYmZv0MAADAyW6oe+paa18ePN+d5ENJXpjkrqp6UpIMnu8e5gwAAAAnsqFFXVWtrKrvOvg6yY8muT3JjUkuGXzskiTbhjUDAADAiW6Yh1+emeRDVXVwO+9prf1RVf2PJDdU1WSSO5JcNMQZAAAATmhDi7rW2ueTPG+W5f87yfnD2i4AAMDJZBS3NAAAAOAfiagDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADomKgDAADo2LL5VlbVWJKXJfnBJE9O8ndJbk/yX1prnx7+eAAAAMxnzqirqrdlJug+muTjSe5OMpbkWUl+dRB8b2it3Xoc5gQAAGAW8+2p+++ttbfMse7qqnpCkrOGMBMAAAALNGfUtdb+y0OXVdWSJI9prX29tXZ3ZvbeAQAAMCJHvVBKVb2nqh5bVSszcz7dZ6rqjcMfDQAAgKNZyNUvn9Na+3qSC5P8YZKnJ3n1UKcCAABgQRYSdcuranlmou7G1tqBJG24YwEAALAQC4m66SRfSLIyyZ9V1Xcn+fowhwIAAGBhjnafuiVJ7mqtPeWwZV9Mct6wBwMAAODo5t1T11r7+yT/5iHLWmvtgaFOBQAAwIIs5PDLP66qX66qp1XVGQcfQ58MAACAo5r38MuBnx48//xhy1qSf/KPPw4AAACPxFGjrrX29OMxCAAAAI/cQvbUpapWJ3lOkrGDy1pr/3FYQwEAALAwR426qnpLkh/OTNT91yQ/nmRnElEHAAAwYgu5UMork5yf5M7W2r9K8rwkpw51KgAAABZkIVH3d4NbGzxQVY9NcneSpw13LAAAABZiIefU7aqq05Jcn+SWJN9M8hdDnQoAAIAFWcjVLy8fvJyuqj9K8tjW2q3DHQsAAICFOOrhlzXjZ6vqza21LyTZW1UvHP5oAAAAHM1Czqm7JsmLk1w8eP+NJL85tIkAAABYsIWcU/f9rbXvrapPJklr7d6qetSQ5wIAAGABFrKn7kBVLU3SkqSqViX5+6FOBQAAwILMGXVVdfbg5b9P8qEkT6iqDZm58fjGoU8GAADAUc13+OUfV9V/SPJvM3Mrg/OTVJILW2u7j8dwAAAAzG++wy9fkOTMzATdqtbab7bW3v1Ig66qllbVJ6vqw4P3T6+qj1fV56rqfc7PAwAAOHZzRl1r7RuttV9K8v8m+YOqur2qbq2q26rqkdyn7heTHB6C70zya621ZyS5N8nksQwOAADAUS6UUlUvTfI7Sf5Dkp8YPF42eD6qqnpqkn8x+H6qqpK8NMkHBh/ZkuTCY5gbAACAzHNOXVW9N8lTk/zL1tptx/j7v57k3yT5rsH7xyXZ21p7YPD+S0meMsf2L0tyWZKcddZZx7h5AACAE9t8e+r+uLV27rEGXVW9LMndrbVbjuX7rbXrWmsTrbWJVatWHctPAAAAnPDmu/rl/qqq1lqbbWVV/dMkT2qt7Zzj+z+Q5OVV9X8nGUvy2CS/keS0qlo22Fv31CRfPvbxAQAATm7zRd3jknyqqm7JzBUw92Qmzp6R5IeSfC3Jm+b6cmvtyiRXJklV/XCSX26t/UxVvT/JK5O8N8klSbb9w/8MAACAk9N8V7/8jSTfm2RrklWZuU/d92Zmz9qrW2s/2Vr77DFs84okr6+qz2UmHDcfw28AAACQ+ffUpbX2YJLtg8cxa619NMlHB68/n+SF/5DfAwAAYMa8tzQAAABgcRN1AAAAHRN1AAAAHTtq1FXVmVW1uar+cPD+OVU1OfzRAAAAOJqF7Kn7nSQfSfLkwfu/TvK6YQ0EAADAwi0k6h7fWrshyd8nyeCm4Q8OdSoAAAAWZCFRt6+qHpekJUlVvSjJfUOdCgAAgAWZ9z51A69PcmOSf1pV/y0zNyJ/5VCnAgAAYEGOGnWttU9U1Q8l+Z4kleSvWmsHhj4ZAAAAR3XUqKuqVzxk0bOq6r4kt7XW7h7OWAAAACzEQg6/nEzy4iQ7Bu9/OMktSZ5eVW9vrf2nIc0GAADAUSwk6pYlGW+t3ZXM3LcuyX9M8v1J/iyJqAMAABiRhVz98mkHg27g7sGye5I4tw4AAGCEFrKn7qNV9eEk7x+8f2WSP62qlUn2Dm0yAAAAjmohUffzSV6R5NzB+y2ttQ8MXp83lKkAAABYkIXc0qAl+eDgkar6war6zdbazw97OAAAAOa3kD11qaoXJLk4yUVJ/leS3xvmUAAAACzMnFFXVc/KTMhdnORrSd6XpFprDrkEAABYJObbU/eXSf48yctaa59Lkqr6peMyFQAAAAsy3y0NXpHkq0l2VNX1VXV+kjo+YwEAALAQc0Zda+33W2uvSvLsJDuSvC7JE6rq2qr60eM1IAAAAHM76s3HW2v7Wmvvaa39RJKnJvlkkiuGPhkAAABHddSoO1xr7d7W2nWttfOHNRAAAAAL94iiDgAAgMVlQfepA4AkOeOMM3LvvfeOeoyRqDo5rxV2+umn55577hn1GADMQ9QBsGD33ntvWmujHoPj6GSNWYCeOPwSAACgY9cQo1QAAA51SURBVKIOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY6IOAACgY0OLuqoaq6r/XlX/s6o+XVVvGyx/elV9vKo+V1Xvq6pHDWsGAACAE90w99R9K8lLW2vPS/L8JD9WVS9K8s4kv9Zae0aSe5NMDnEGAACAE9rQoq7N+Obg7fLBoyV5aZIPDJZvSXLhsGYAAAA40Q31nLqqWlpVn0pyd5LtSf4myd7W2gODj3wpyVPm+O5lVbWrqnbt2bNnmGMCAAB0a6hR11p7sLX2/CRPTfLCJM9+BN+9rrU20VqbWLVq1dBmBAAA6Nlxufpla21vkh1JXpzktKpaNlj11CRfPh4zAAAAnIiGefXLVVV12uD1o5OsSbI7M3H3ysHHLkmybVgzAAAAnOiWHf0jx+xJSbZU1dLMxOMNrbUPV9Vnkry3qt6R5JNJNg9xBgAAgBPa0KKutXZrkhfMsvzzmTm/DgAAgH+g43JOHQAAAMMh6gAAADo2zHPqADjBPOHCJ+S5W5576P17X/beJMmrPvyqQ8te87zX5PLnX56X3vDS7Pm7mfuMjp8xnht+4oa89WNvzQc/+8FDn735p27OZ/73ZzL1J1OHlr35xW/OTz3rp47Yzg899Yfy7vPfndfe/Nr86Zf+9NDy2y65Le//6/fn7X/x9kPLNr10U57zuOfk/Peff2jZTz7zJ/PWl7w1F/3BRdl9z+4kyapHr8qfXPQnueZT1+Ta/3mtv2mOv+kJFz4hACxu1Vob9QxHNTEx0Xbt2jXqMQBOelWVHv7d4B+P/+cAi0NV3dJam5htncMvAQAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOibqAAAAOrZs1AMA0I/2lscmbz111GNwHLW3PHbUIwBwFKIOgAWrt309rbVRj8FxVFVpbx31FADMx+GXAAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHVs26gEA6EtVjXoEjqPTTz991CMAcBSiDoAFa62NeoSRqKqT9m8HYPFz+CUAAEDHRB0AAEDHRB0AAEDHRB0AAEDHhhZ1VfW0qtpRVZ+pqk9X1S8Olp9RVdur6rODZ5fVAgAAOEbD3FP3QJI3tNaek+RFSX6+qp6T5E1Jbm6tPTPJzYP3AAAAHIOhRV1r7auttU8MXn8jye4kT0lyQZItg49tSXLhsGYAAAA40R2Xc+qq6uwkL0jy8SRntta+Olh1Z5Iz5/jOZVW1q6p27dmz53iMCQAA0J2hR11VPSbJB5O8rrX29cPXtZk7uc56N9fW2nWttYnW2sSqVauGPSYAAECXhhp1VbU8M0H3u6213xssvquqnjRY/6Qkdw9zBgAAgBPZMK9+WUk2J9ndWrv6sFU3Jrlk8PqSJNuGNQMAAMCJbtkQf/sHkrw6yW1V9anBsquS/GqSG6pqMskdSS4a4gwAAAAntKFFXWttZ5KaY/X5w9ouAADAyeS4XP0SAACA4RB1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRN1AAAAHRta1FXV/1dVd1fV7YctO6OqtlfVZwfPpw9r+wAAACeDYe6p+50kP/aQZW9KcnNr7ZlJbh68BwAA4BgNLepaa3+W5J6HLL4gyZbB6y1JLhzW9gEAAE4Gx/ucujNba18dvL4zyZnHefsAAAAnlJFdKKW11pK0udZX1WVVtauqdu3Zs+c4TgYAANCP4x11d1XVk5Jk8Hz3XB9srV3XWptorU2sWrXquA0IAADQk+MddTcmuWTw+pIk247z9gEAAE4ow7ylwdYkf5Hke6rqS1U1meRXk6ypqs8m+ZHBewAAAI7RsmH9cGvt4jlWnT+sbQIAAJxsRnahFAAAAP7hRB0AAEDHRB0AAEDHRB0AAEDHRB0AAEDHRN3/ae+OdeMoozAMnyOjXAGuAEUpUG7A4g6Q3NGmR0qVC6FNQWNFFh2pXUSipaGwS0KFkFCcBiMuACGdFHFhkNcWKfbfz36ebmaL+dpXMzsDAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAME+WnHR7j6squdVtVdVL2bmmxU7AMjR3ff6+jOz9PoA7K6tR11371XVt1X1ZVWdV9Vpd5/MzC/b3gJADlEDANdb8fjlF1X168z8NjN/V9XLqvpqwQ4AAIB4K6Luk6p6c+X4/PIcAAAA/9POviilu59291l3n11cXKyeAwAAsJNWRN3bqvrsyvGnl+f+ZWaOZuZgZg729/e3Ng4AACDJiqg7rarPu/tRdz+oqidVdbJgBwAAQLytv/1yZv7p7mdV9UO9/6TB8cy83vYOAACAu2DJd+pm5lVVvVpxbQAAgLtkZ1+UAgAAwO1EHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEE3UAAADBRB0AAEAwUQcAABBM1AEAAAQTdQAAAMFEHQAAQDBRBwAAEEzUAQAABOuZWb3hVt19UVW/r94BwL31cVX9uXoEAPfaw5nZv+6HiKgDgJW6+2xmDlbvAIDrePwSAAAgmKgDAAAIJuoA4HZHqwcAwCb+UwcAABDMnToAAIBgog4AACCYqAOADbr7uLv/6O6fV28BgE1EHQBs9l1VHa4eAQA3EXUAsMHM/FhVf63eAQA3EXUAAADBRB0AAEAwUQcAABBM1AEAAAQTdQCwQXd/X1U/VdXj7j7v7q9XbwKA/+qZWb0BAACAD+ROHQAAQDBRBwAAEEzUAQAABBN1AAAAwUQdAABAMFEHAAAQTNQBAAAEewf09CGLTa578wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0_rPngZDiHZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "db86fed8-3d08-4195-f30b-7cb05f1948a5"
      },
      "source": [
        "plt.hist(x = [df[df['Survived']==1]['Fare'], df[df['Survived']==0]['Fare']], \n",
        "         stacked=True, color = ['b','y'],label = ['Survived','Dead'])\n",
        "plt.title('Fare Histogram by Survival')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('Number of Passengers')\n",
        "plt.legend()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5942c57dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "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": "jZI2GnZKDxVe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "cc28fc60-2430-4fcb-b39a-e209ef5792c2"
      },
      "source": [
        "plt.hist(x = [df[df['Survived']==1]['Age'], df[df['Survived']==0]['Age']], \n",
        "         stacked=True, color = ['blue','black'],label = ['Survived','Dead'])\n",
        "plt.title('Age Histogram by Survival')\n",
        "plt.xlabel('Age (Years)')\n",
        "plt.ylabel('Number of Passengers')\n",
        "plt.legend()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5942cc73c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "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": "PB4FIDNFECHa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "5f284da5-c5e5-4045-8409-b22d0c457457"
      },
      "source": [
        "sns.barplot(x='Embarked', y='Survived', data=df)\n",
        "plt.show()\n",
        "sns.barplot(x = 'Pclass', y = 'Survived', order=[1,2,3], data=df)"
      ],
      "execution_count": 146,
      "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"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f59426b8b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        },
        {
          "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": "qZwXANCUEcWN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "outputId": "de1d0316-a1aa-4914-c74c-3ff9a84effe0"
      },
      "source": [
        "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(14,12))\n",
        "\n",
        "sns.boxplot(x = 'Pclass', y = 'Fare', hue = 'Survived', data = df, ax = axis1)\n",
        "axis1.set_title('Pclass vs Fare Survival Comparison')\n",
        "\n",
        "sns.violinplot(x = 'Pclass', y = 'Age', hue = 'Survived', data = df, split = True, ax = axis2)\n",
        "axis2.set_title('Pclass vs Age Survival Comparison')"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Pclass vs Age Survival Comparison')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtryMK1kE8ee",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "f06150d6-1498-4597-a44a-f17bfac1388d"
      },
      "source": [
        "a = sns.FacetGrid( df, hue = 'Survived', aspect=4 )\n",
        "a.map(sns.kdeplot, 'Age', shade= True )\n",
        "a.set(xlim=(0 , df['Age'].max()))\n",
        "a.add_legend()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f5942358c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 915.875x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ru8uGv5DFNVq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "5b9e38e4-a102-477f-95de-297fdf9de87d"
      },
      "source": [
        "h = sns.FacetGrid(df, row = 'Sex', col = 'Pclass', hue = 'Survived')\n",
        "h.map(plt.hist, 'Age', alpha = .75)\n",
        "h.add_legend()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f5947598c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 699.875x432 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AaXIjneDFUUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "d68e0345-4435-46d2-d59c-ec069d411ae2"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train,y_train)\n",
        "prediction_lr=model.predict(X_test)\n",
        "result_lr = roc_auc_score(prediction_lr, y_test)\n",
        "print('The accuracy of the Logistic Regression is',round(accuracy_score(prediction_lr,y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_lr, y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the Logistic Regression is 80.27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9x4ORRnXKOiB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "c4d9a45c-77f3-4f39-cacd-0e4de0b2df16"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(criterion='gini', n_estimators=700)\n",
        "model.fit(X_train,y_train)\n",
        "prediction_rf=model.predict(X_test)\n",
        "result_rf = roc_auc_score(prediction_rf, y_test)\n",
        "print('The accuracy of the Random Forest is',round(accuracy_score(prediction_rf,y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_rf,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the Random Forest is 82.06\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R37P9F3gcNLK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "0f467aa3-ae07-4c42-dc17-8e3eb1ae23f7"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC()\n",
        "model.fit(X_train,y_train)\n",
        "prediction_svc=model.predict(X_test)\n",
        "result_svc = roc_auc_score(prediction_svc, y_test)\n",
        "print('The accuracy of the Support Vector Classifier is',round(accuracy_score(prediction_svc,y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_svc,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the Support Vector Classifier is 65.92\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFtcEv6xdTrm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "dc8ba6ab-26a6-4ab2-8a8c-043cfc0ac148"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier(n_neighbors=4)\n",
        "model.fit(X_train,y_train)\n",
        "prediction_knn=model.predict(X_test)\n",
        "result_knn = roc_auc_score(prediction_knn, y_test)\n",
        "print('The accuracy of the kNN Classifier is',round(accuracy_score(prediction_knn,y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_knn,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the kNN Classifier is 65.47\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daGZi0t0e-Ry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "1c5e70f8-ef34-4f49-a134-b10df1c5549d"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "model = GaussianNB()\n",
        "model.fit(X_train, y_train)\n",
        "prediction_nb = model.predict(X_test)\n",
        "result_nb = roc_auc_score(prediction_nb, y_test)\n",
        "print(\"The accuracy of Naive Bayes is: \", round(accuracy_score(prediction_nb, y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_nb,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of Naive Bayes is:  75.34\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-g4PEuYfjof",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "09f370c7-b6e0-430b-928c-e9b240b59848"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model= DecisionTreeClassifier(criterion='gini', \n",
        "                             min_samples_split=10,min_samples_leaf=1,\n",
        "                             max_features='auto')\n",
        "model.fit(X_train,y_train)\n",
        "prediction_tree=model.predict(X_test)\n",
        "result_tree = roc_auc_score(prediction_tree, y_test)\n",
        "print(\"The accuracy of Decision Tree is: \", round(accuracy_score(prediction_tree, y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_tree,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of Decision Tree is:  77.58\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmNVbsJZf9Ey",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "d0055973-52c4-478e-fba6-3831aec85daf"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "model= AdaBoostClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "prediction_adb=model.predict(X_test)\n",
        "result_adb = roc_auc_score(prediction_adb, y_test)\n",
        "print(\"The accuracy of Decision Tree is: \", round(accuracy_score(prediction_adb, y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_adb,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of Decision Tree is:  81.17\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AL9eiGFmgKfn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "06e96e10-709b-49c8-c85c-e0a5507caf50"
      },
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "model= LinearDiscriminantAnalysis()\n",
        "model.fit(X_train,y_train)\n",
        "prediction_lda=model.predict(X_test)\n",
        "result_lda = roc_auc_score(prediction_lda, y_test)\n",
        "print(\"The accuracy of LDA is: \", round(accuracy_score(prediction_lda, y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_lda,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of LDA is:  79.82\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naKbN2GUgUo_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "58ab88f7-3dd8-4283-826b-92072ab96b97"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "model= GradientBoostingClassifier()\n",
        "model.fit(X_train,y_train)\n",
        "prediction_gbc=model.predict(X_test)\n",
        "result_gbc = roc_auc_score(prediction_gbc, y_test)\n",
        "print(result_gbc)\n",
        "print(\"The accuracy of GBC is: \", round(accuracy_score(prediction_gbc, y_test)*100,2))\n",
        "sns.heatmap(confusion_matrix(prediction_gbc,y_test),annot=True,fmt='3.0f',cmap=\"summer\")\n",
        "plt.title('Confusion_matrix', y=1.05, size=15)\n"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8318536342515765\n",
            "The accuracy of GBC is:  83.86\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Confusion_matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_T1R5k-fggpp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "64ba5ce0-0544-4296-b5ae-55cc70378212"
      },
      "source": [
        "# Sort by highest AUC ROC SCORE\n",
        "models = pd.DataFrame({\n",
        "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
        "              'Random Forest', 'Naive Bayes', 'AdaBoostClassifier', \n",
        "              'Gradient Boosting', 'Linear Discriminant Analysis', \n",
        "              'Decision Tree'],\n",
        "    'Score': [result_svc, result_knn, result_lr, \n",
        "              result_rf, result_nb, result_adb, \n",
        "              result_gbc, result_lda, result_tree]})\n",
        "models.sort_values(by='Score',ascending=False)"
      ],
      "execution_count": 198,
      "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>6</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.831854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.813350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AdaBoostClassifier</td>\n",
              "      <td>0.804016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.795497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.790199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.769527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.746470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Support Vector Machines</td>\n",
              "      <td>0.683982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.653359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Model     Score\n",
              "6             Gradient Boosting  0.831854\n",
              "3                 Random Forest  0.813350\n",
              "5            AdaBoostClassifier  0.804016\n",
              "2           Logistic Regression  0.795497\n",
              "7  Linear Discriminant Analysis  0.790199\n",
              "8                 Decision Tree  0.769527\n",
              "4                   Naive Bayes  0.746470\n",
              "0       Support Vector Machines  0.683982\n",
              "1                           KNN  0.653359"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHN2EORcidUJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}