Titanic.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Titanic",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHLl0jWbptPq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRUxIYTCpWRF",
        "colab_type": "text"
      },
      "source": [
        "# **Importing the libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZkvSGK9wIZV",
        "colab_type": "text"
      },
      "source": [
        "> ## **Importing Algorithms**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "meuPKL0vxNAL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCfElIIhyr7S",
        "colab_type": "text"
      },
      "source": [
        "> ## **Importing Encoders**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3X32v5Ecyz7O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8aVGpw-uNUn",
        "colab_type": "text"
      },
      "source": [
        "## **Importing Visualising Libaries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwyCpAT4ujCt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KMkpNm3p-Wh",
        "colab_type": "text"
      },
      "source": [
        "# **Importing the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mhjkcaKtqEDu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "d0c66a94-d17f-454a-ec02-1021e9bfd772"
      },
      "source": [
        "dt_train = pd.read_csv(\"train.csv\")\n",
        "dt_test = pd.read_csv(\"test.csv\")\n",
        "dt_train.head(7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "5            6         0       3  ...   8.4583   NaN         Q\n",
              "6            7         0       1  ...  51.8625   E46         S\n",
              "\n",
              "[7 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 972
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jwsKH1TsnRx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "27709513-7019-45dd-afb5-c61ff128a00b"
      },
      "source": [
        "dt_test.head(7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>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>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>897</td>\n",
              "      <td>3</td>\n",
              "      <td>Svensson, Mr. Johan Cervin</td>\n",
              "      <td>male</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7538</td>\n",
              "      <td>9.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>898</td>\n",
              "      <td>3</td>\n",
              "      <td>Connolly, Miss. Kate</td>\n",
              "      <td>female</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330972</td>\n",
              "      <td>7.6292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0          892       3  ...   NaN        Q\n",
              "1          893       3  ...   NaN        S\n",
              "2          894       2  ...   NaN        Q\n",
              "3          895       3  ...   NaN        S\n",
              "4          896       3  ...   NaN        S\n",
              "5          897       3  ...   NaN        S\n",
              "6          898       3  ...   NaN        Q\n",
              "\n",
              "[7 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 973
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFMnFgwMrNGF",
        "colab_type": "text"
      },
      "source": [
        "#**Data Analysis**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8hV4n3prRpW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "a058af6a-717c-4230-89c1-d25cbc3d5706"
      },
      "source": [
        "dt_train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BskYs09BznN3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "ae77c85e-2cf8-4721-8d0d-40d73cfcf895"
      },
      "source": [
        "total = dt_train.isnull().sum().sort_values(ascending=False)\n",
        "percent = dt_train.isnull().sum() / dt_train.isnull().count() * 100\n",
        "missingData = pd.concat([total, round(percent, 2).sort_values(ascending=False)], axis=1, keys=[\"Total\", \"Percentage\"])\n",
        "missingData.head(12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "      <td>77.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>177</td>\n",
              "      <td>19.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Total  Percentage\n",
              "Cabin          687       77.10\n",
              "Age            177       19.87\n",
              "Embarked         2        0.22\n",
              "Fare             0        0.00\n",
              "Ticket           0        0.00\n",
              "Parch            0        0.00\n",
              "SibSp            0        0.00\n",
              "Sex              0        0.00\n",
              "Name             0        0.00\n",
              "Pclass           0        0.00\n",
              "Survived         0        0.00\n",
              "PassengerId      0        0.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 975
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuqK2DTe4VB-",
        "colab_type": "text"
      },
      "source": [
        "> ## **PassengerId**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ke_jCdPs46Ca",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "29ba78b9-6c8a-41a7-eb1f-80d2dd934523"
      },
      "source": [
        "len(dt_train.PassengerId.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 976
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kzSv45rop1w",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "> ## **Pclass**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1G50EgCpMPY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a0b59fb8-e892-4695-e531-0ec8e2f7e0ca"
      },
      "source": [
        "sns.barplot(x=\"Pclass\", y=\"Survived\" , data=dt_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe8e21cda58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 977
        },
        {
          "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": "t7Xs_18IqJMi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "5dd9aa13-2bb6-4bf2-b471-072185c639dd"
      },
      "source": [
        "grid = sns.FacetGrid(dt_train, col=\"Survived\", row=\"Pclass\", height=2.2, aspect=1.6)\n",
        "grid.map(plt.hist, \"Age\", alpha=0.4, bins= 20)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZT38JvAU4qLutWtxwNGC239Fuv/8dOGKxg1hoJvO5+xJwIECSN3ffnjck+fTEFZO8Jckt3fLLt/cIkvxe10vYkOSLXdnBSW7uehB3JDloLkFW1Veq6tFu9mvA8hnuYhmwZdz+7ujiPCbJ1ePq+Ikkp3TT9yc5J8nXgXcnuXncevsnubObvinJyiSnJfnIuHWe+qae5PfHPR9/tf3DJcm/S/KNbt9Hz7BOz5BkGfALVfW16l1S8FPA8XPdr4aW7XeE2m9Xt69V1YOD2FdLTOZzkGQp8GrgziQHA+8Hjq2qQ4HTJ9nkiqr6tW75JuDUrvyPgFd15a/tyk4DPl5VhwErGdcQxz3+2q5xTLy9eZrQTwX+xwyr+0nggiQ3Jnlfkl/uc7vvVdVLqupsYNckB3TlrwfWTlj3cuB3x82/Hrgkya9200d3z8eTwEld4v0AvQ+BlwOTDvUl+Y0pnqevTLL6Pjz9ud7SlWnE2H770lr73WktXewAGrVbktu76S8BFwBvBS6rqkcAqur7k2x3SJIPAnsAuwPXdeV/D1yU5FLgiq7sq8D7kiyn9yFy78SdVdXrZxp4kt+g92Hw8plsV1XXJXkBvSHnVwO3JTmkj03HN/hL6TXqs7v7p8VfVduS3JfkKOBe4EX0npu3AYcDt6T38/VuwFbgSOCmqtrW1W0t8K8mif1G4LD+a6sRZ/u1/Y4ck/ns/Kj7hvmU9HeM1EXA8VW1oRvKOgagqk5LciTwGuDWJIdX1cVJ1nVl1yR5a1V9YcJjrgVeOMnjfLSqPjWxMMm/Ac4HXl1V3+sn4PG6D7iLgYu7oblfBx7m6SM8z5qw2Q/HTa8FLktyRW93z/yAAy4BXgf8A/D5qqr0ntw1VXXmhPr0NfzdfQCeO8mif6qql00oe4CnD2Eu78o0Omy/o9t+d15V5W2GN+DxScoOBr4BPL+b37O7Pwt4Vzf9CPCLwC7A9cBFXfmvjNvPLfS+hb4ASFf2Z8A75hjzfsBm4GXTrHcRcMwk5ccCz+6mn0tvmPHXgH2B+4Gfp9dj+RZwSrfe/cBeE/ZzC/Bp4D3jym4CVnbTzwO+CdwIHNGVraD3Tf8Xtz+3wL+k9zvgt4Hnd8/pl4BPDOD1vRk4Cgi94czfXuz3nLfB3Wy/o91+d/Q6j/LNnvmAVNXdST4E/F2SJ4HbgFMmrPaHwDpgW3f/3K78I90BMgFuADYA7wXelOQnwEPAn84xxD+i12j+W9cLeaKqVs5g+8OBTyR5gt43+fOr6haAbnjxLnofBLdNs5+1wEeAAyZbWFWPJtkErKiqm7uyjUneD/xtkp8DfgK8raq+luQsekOajwG3T7bPWfgP9D4Ud6OXzGf6+6QaY/sdnfab5L8AbwSenWQLvbqeNYh9D7Pt3xwloPc/VXo9jpsWORRJM2T73Xl5NLskSY0zmWuiv6H3W5mk9th+d1IOs0uS1Dh75pIkNc5kLklS4xb0r2mrVq2qa6+9diEfUtIzzeoqcLZfadFN2XYXtGf+yCOPLOTDSRog2680vBxmlySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcX0n8yRLktyW5Opu/oAk65JsTrI2ya7zF6YkSZrKTHrmpwObxs2fA5xbVQcCjwKnDjIwSZLUn76SeZLlwGuA87v5AMcCn+tWWQMcPx8BSpKkHeu3Z/4x4D3AT7v55wOPVdUT3fwWYJ8BxyZJkvowbTJP8jvA1qq6dTYPkGR1kvVJ1m/btm02u5C0SGy/Uhv66ZkfDbw2yf3AJfSG1z8O7JFk+1XXlgMPTLZxVZ1XVSurauXY2NgAQpa0UGy/UhumTeZVdWZVLa+q/YE3AF+oqpOAG4ETutVOBq6ctyglSdKU5vI/8/cC/ynJZnq/oV8wmJAkSdJMLJ1+lZ+pqpuAm7rp+4AjBh+SJEmaCc8AJ0lS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDVu2mSe5FlJbk6yIcndST7QlR+QZF2SzUnWJtl1/sOVJEkT9dMz/2fg2Ko6FDgMWJXkKOAc4NyqOhB4FDh1/sKUJElTmTaZV8/j3ewu3a2AY4HPdeVrgOPnJUJJkrRDff1mnmRJktuBrcD1wDeBx6rqiW6VLcA+8xOiJEnakb6SeVU9WVWHAcuBI4AX9fsASVYnWZ9k/bZt22YZpqTFYPuV2jCjo9mr6jHgRuClwB5JlnaLlgMPTLHNeVW1sqpWjo2NzSlYSQvL9iu1oZ+j2ceS7NFN7wa8EthEL6mf0K12MnDlfAUpSZKmtnT6VVgGrEmyhF7yv7Sqrk6yEbgkyQeB24AL5jFOSZI0hWmTeVXdAbx4kvL76P1+LkmSFpFngJMkqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlx0ybzJPsmuTHJxiR3Jzm9K98zyfVJ7u3unzf/4UqSpIn66Zk/AbyzqlYARwFvS7ICOAO4oaoOAm7o5iVJ0gKbNplX1YNV9fVu+h+BTcA+wHHAmm61NcDx8xWkJEma2ox+M0+yP/BiYB2wd1U92C16CNh7oJFJkqS+9J3Mk+wOXA68o6p+MH5ZVRVQU2y3Osn6JOu3bds2p2AlLSzbr9SGvpJ5kl3oJfLPVNUVXfHDSZZ1y5cBWyfbtqrOq6qVVbVybGxsEDFLWiC2X6kN/RzNHuACYFNVfXTcoquAk7vpk4ErBx+eJEmaztI+1jkaeBNwZ5Lbu7I/AM4GLk1yKvBt4HXzE6IkSdqRaZN5VX0ZyBSLXzHYcCRJ0kx5BjhJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaN20yT3Jhkq1J7hpXtmeS65Pc290/b37DlCRJU1naxzoXAZ8APjWu7Azghqo6O8kZ3fx7Bx+eAC5e950pl73xyP0WMBJJ0jCatmdeVV8Evj+h+DhgTTe9Bjh+wHFJkqQ+zfY3872r6sFu+iFg7wHFI0mSZqifYfYdqqpKUlMtT7IaWA2w334795DwfAyXOwSv+TTb9ruj9yX43twRnzvNxmx75g8nWQbQ3W+dasWqOq+qVlbVyrGxsVk+nKTFYPuV2jDbnvlVwMnA2d39lQOLSJKGmD1nDaN+/pr2WeCrwAuTbElyKr0k/sok9wK/2c1LkqRFMG3PvKpOnGLRKwYciyRJmoU5HwCnNnngnLQ4phuml2bD07lKktQ4e+ZDYj6+rdsDkJ5pvg9gs91pMdgzlySpcSZzSZIa5zD7LDiMJkkaJvbMJUlqnMlckqTGmcwlSWqcyVySpMZ5AJyewbPDab55sZLZm8tz5/M+uuyZS5LUOJO5JEmNc5hdAzPb/9/PdmhvoR9PC2cxf+pp/TwSixm/w/iLx565JEmNs2euGfGCMBp2vp9mb7EvQmPPffbm1DNPsirJPUk2JzljUEFJkqT+zTqZJ1kCfBJ4NbACODHJikEFJkmS+jOXYfYjgM1VdR9AkkuA44CNgwhMmi+zPbhqNsO3DhtqlCz2TxgO009tLsPs+wDfHTe/pSuTJEkLaN4PgEuyGljdzT6e5J5pNtkLeGR+o1pQo1SfUaoLTFKfkwb8AIPe3w7M5LW5tqpW9bPiMLbfBXxOYbTe80Nflxm+tvPefhdQv6/NlG03VTWrR07yUuCsqnpVN38mQFV9eFY7/Nl+11fVyrnsY5iMUn1GqS4wWvUZlroMSxyDMkr1GaW6wGjVZxB1mcsw+y3AQUkOSLIr8AbgqrkEI0mSZm7Ww+xV9USStwPXAUuAC6vq7oFFJkmS+jKn38yr6hrgmgHFst15A97fYhul+oxSXWC06jMsdRmWOAZllOozSnWB0arPnOsy69/MJUnScPDc7JIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5rOU5Mkktye5K8llSZ69g3XPSvKuhYxvijiOS3JHF/f6JC+fYr2bkuw/SfkLu2W3J9mUZGBXLUpyTZI9BrCfgTzXSVYluSfJ5iRnzHV/Gh623ZFvuxcm2ZrkrrnuqyUm89n7UVUdVlWHAD8GTlvsgPpwA3BoVR0G/J/A+TPc/s+Bc7t6/yrwFzPZOMmSqZZV1W9X1WMzjGdedHF+Eng1sAI4McmKxY1KA2TbHdG227kIWLXYQSw0k/lgfAk4ECDJm7tv0BuSfHriiknekuSWbvnl23sFSX6v6ylsSPLFruzgJDd336bvSHLQXIKsqsfrZ9e8fQ4w0+vfLgO2jNvfnV2cpyT5xLg6Xp3kmG768ST/NckG4Mwkl41b75gkV3fT9yfZK8nZSd42bp2nvq0neXf33N2R5APj1nlfkm8k+TLwwhnWaTJHAJur6r6q+jFwCXDcAPar4WPbHa22S1V9Efj+IPbVkqWLHUDrkiyl14O7NsnBwPuBl1XVI0n2nGSTK6rq/+62/SBwKr1vyX8EvKqqHhg3ZHUa8PGq+kySXYFnfDtOspbJG8FHq+pTk6z/u8CHgV8EXjPD6p4LfCHJV4C/Bf66j2/kzwHWVdU7u+fqviTPqaofAq+nlyjHWwt8jF7PGOB1wKuS/BZwEL1EG+CqJL8O/BB4A3AYvffz14FbJwaR5CTg3ZPEt7mqTphQtg/w3XHzW4Ajp6mnGmPbHcm2u9Mymc/ebklu76a/BFwAvBW4rKoeAaiqyb4dHtJ9EOwB7A5c15X/PXBRkkuBK7qyrwLvS7Kc3gfJvRN3VlWvn0nQVfV54PNdY/oT4DdnsO1fJ7mO3hDWccBbkxw6zWZPApd32z+R5Frgf0/yOXofSO+Z8Bi3JfnFJL8MjAGPVtV3k5wO/BZwW7fq7vQ+IJ4LfL6q/gkgyVVTxP4Z4DP91lUjzbZr2x05JvPZ+1H3+9VTkvSz3UXA8VW1IckpwDEAVXVakiPpNZJbkxxeVRcnWdeVXZPkrVX1hQmPOaNv99tV1ReTvCDJXts/wPpRVf8fcCFwYXoHmBwCPMHTf7J51rjp/1VVT46bvwR4O71hsPVV9Y+TPMxlwAnAL9H7tg+9b/Qfrqq/Gr9iknf0E/cMv90/AOw7bn55V6bRYNsd3ba786oqb7O4AY9PUnYw8A3g+d38nt39WcC7uulH6A2T7QJcD1zUlf/KuP3cQm/o6QVAurI/A94xx5gPHLe/l9BLUJlkvZuA/ScpXwXs0k3/EvBgd/9y4Cv0PhT2BX4AHDPZ80RvuPF+eo3+dePK7wf2Gvc8fqV7Lpd1Zb8FrAN27+b36Z7HlwB3ALvR+6Z/7/bneg7P01LgPuAAYFdgA3DwYr/nvA3mZtsd3bY7Lqb9gbsW+722kDd75gNUVXcn+RDwd0mepDesdMqE1f6Q3ht7W3f/3K78I91BMqF35OoG4L3Am5L8BHgI+NM5hvhvgTd3+/sR8Prq3vl9+i3g40n+Vzf/7qp6KMnDwLeAjcAmer99TaqqnuwOnDkFOHmKde5O8lzggap6sCv72yS/Cny160U9Dvx+VX296+FsALbS+zCdk+oNKb6d3jDqEuDCqrp7rvvV8LLtjkbbBUjyWXqjJnsl2QL8cVVdMIh9D7PM7P2gnUGSm4BTqur+RQ5F0gzYdnde/jVNkqTGmcw1mYuAYToJhKT+XIRtd6fkMLskSY2zZy5JUuMW9Gj2VatW1bXXXruQDynpmfr6U/VEtl9p0U3Zdhe0Z/7II32f30DSkLH9SsPLYXZJkhpnMpckqXEmc0mSGmcylySpcZ6bfZ5cvO47067zxiP3W4BIJEmjzp65JEmNM5lLktS4vpN5kiVJbusugUeSA5KsS7I5ydoku85fmJIkaSoz6ZmfTu96t9udA5xbVQcCjwKnDjIwSZLUn76SeZLlwGuA87v5AMcCn/Jw3dAAAAvhSURBVOtWWQMcPx8BSpKkHeu3Z/4x4D3AT7v55wOPVdUT3fwWYJ8BxyZJkvowbTJP8jvA1qq6dTYPkGR1kvVJ1m/btm02u5C0SGy/Uhv66ZkfDbw2yf3AJfSG1z8O7JFk+//UlwMPTLZxVZ1XVSurauXY2NgAQpa0UGy/UhumTeZVdWZVLa+q/YE3AF+oqpOAG4ETutVOBq6ctyglSdKU5nIGuPcClyT5IHAbcMFgQhpu/ZzZTZKkhTSjZF5VNwE3ddP3AUcMPiRJkjQTngFOkqTGmcwlSWqcyVySpMaZzCVJapzJXJKkxpnMJUlq3Fz+Z6456uc/6288cr8FiESS1DJ75pIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zjPADbl+zhIHnilOknZm0/bMkzwryc1JNiS5O8kHuvIDkqxLsjnJ2iS7zn+4kiRpon6G2f8ZOLaqDgUOA1YlOQo4Bzi3qg4EHgVOnb8wJUnSVKZN5tXzeDe7S3cr4Fjgc135GuD4eYlQkiTtUF8HwCVZkuR2YCtwPfBN4LGqeqJbZQuwz/yEKEmSdqSvZF5VT1bVYcBy4AjgRf0+QJLVSdYnWb9t27ZZhilpMdh+pTbM6K9pVfUYcCPwUmCPJNuPhl8OPDDFNudV1cqqWjk2NjanYCUtLNuv1IZ+jmYfS7JHN70b8EpgE72kfkK32snAlfMVpCRJmlo//zNfBqxJsoRe8r+0qq5OshG4JMkHgduAC+YxTkmSNIVpk3lV3QG8eJLy++j9fi5JkhaRp3OVJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcdMm8yT7JrkxycYkdyc5vSvfM8n1Se7t7p83/+FKkqSJ+umZPwG8s6pWAEcBb0uyAjgDuKGqDgJu6OYlSdICmzaZV9WDVfX1bvofgU3APsBxwJputTXA8fMVpCRJmtqMfjNPsj/wYmAdsHdVPdgtegjYe6CRSZKkviztd8UkuwOXA++oqh8keWpZVVWSmmK71cBqgP32229u0WpKF6/7zrTrvPFIn3/NjO1XakNfPfMku9BL5J+pqiu64oeTLOuWLwO2TrZtVZ1XVSurauXY2NggYpa0QGy/Uhum7Zmn1wW/ANhUVR8dt+gq4GTg7O7+ynmJUAvOXr4ktaWfYfajgTcBdya5vSv7A3pJ/NIkpwLfBl43PyFKkqQdmTaZV9WXgUyx+BWDDUeSJM2UZ4CTJKlxJnNJkhrX91/T1L5+DmyTJLXHnrkkSY0zmUuS1DiTuSRJjTOZS5LUOJO5JEmNM5lLktQ4k7kkSY0zmUuS1DiTuSRJjTOZS5LUOJO5JEmNM5lLktS4ob7QSj8XBnnjkfstQCSSJA2vaXvmSS5MsjXJXePK9kxyfZJ7u/vnzW+YkiRpKv0Ms18ErJpQdgZwQ1UdBNzQzUuSpEUw7TB7VX0xyf4Tio8Djumm1wA3Ae8dYFwD1e91vB2ylyS1aLYHwO1dVQ920w8Bew8oHkmSNENzPgCuqipJTbU8yWpgNcB++9nzHRX9jnb0wxGR4bUztt9BHHg73T58z2vQZtszfzjJMoDufutUK1bVeVW1sqpWjo2NzfLhJC0G26/Uhtkm86uAk7vpk4ErBxOOJEmaqWmH2ZN8lt7Bbnsl2QL8MXA2cGmSU4FvA6+bzyAltcMh5un5HGnQ+jma/cQpFr1iwLFIkqRZGOozwEkaPfPdKx3kwZlSKzw3uyRJjTOZS5LUOIfZJWmGHMrXsLFnLklS4+yZS9KQ8a9rmil75pIkNc5kLklS45ofZh/kgSge1LI4BnFhC0namdkzlySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcc3/z1wadv2ev8D/0vd4KtPpzfWcGD6Ho2dOPfMkq5Lck2RzkjMGFZQkSerfrHvmSZYAnwReCWwBbklyVVVtHFRw0nbDenY+ezgLb1jfCy1pvWfv6M0zzaVnfgSwuaruq6ofA5cAxw0mLEmS1K+5JPN9gO+Om9/SlUmSpAU07wfAJVkNrO5mH09yzzSb7AU8Mr9RLahRqs8o1QUGUJ+TBhTIAPY1k7pcW1Wr+lnR9jtS9RlYXQb5vp+DKeszJPHNRL+vzZRtN1U1q0dO8lLgrKp6VTd/JkBVfXhWO/zZftdX1cq57GOYjFJ9RqkuMFr1GZa6DEscgzJK9RmlusBo1WcQdZnLMPstwEFJDkiyK/AG4Kq5BCNJkmZu1sPsVfVEkrcD1wFLgAur6u6BRSZJkvoyp9/Mq+oa4JoBxbLdeQPe32IbpfqMUl1gtOozLHUZljgGZZTqM0p1gdGqz5zrMuvfzCVJ0nDw3OySJDVuqJJ5y6eHTbJvkhuTbExyd5LTu/I9k1yf5N7u/nmLHWu/kixJcluSq7v5A5Ks616ftd2Bj01IskeSzyX5hySbkry08dfm/+reZ3cl+WySZy3m69Ny2wXb77Cz/U5vaJL5uNPDvhpYAZyYZMXiRjUjTwDvrKoVwFHA27r4zwBuqKqDgBu6+VacDmwaN38OcG5VHQg8Cpy6KFHNzsfp/UfzRcCh9OrV5GuTZB/gPwIrq+oQegegvoFFen1GoO2C7XfY2X6nU1VDcQNeClw3bv5M4MzFjmsO9bmS3nnr7wGWdWXLgHsWO7Y+419Or4EcC1wNhN5JDZZO9noN8w34F8C36I4RGVfe6muz/eyLe9I7iPVq4FWL9fqMWtvt6mD7HZKb7be/29D0zBmh08Mm2R94MbAO2LuqHuwWPQTsvUhhzdTHgPcAP+3mnw88VlVPdPMtvT4HANuAv+6GHc9P8hwafW2q6gHgz4DvAA8C/xO4lcV7fUam7YLtdwjZfvswTMl8JCTZHbgceEdV/WD8sup95Rr6vw8k+R1ga1XdutixDMhS4CXAX1bVi4EfMmFIrpXXBqD7bfA4eh9yvww8B+jr9KzaMdvvULL99mGYkvkDwL7j5pd3Zc1Isgu9D4LPVNUVXfHDSZZ1y5cBWxcrvhk4GnhtkvvpXQ3vWHq/We2RZPu5CVp6fbYAW6pqXTf/OXofDi2+NgC/CXyrqrZV1U+AK+i9Zov1+jTfdsH2O8Rsv30YpmTe9OlhkwS4ANhUVR8dt+gq4ORu+mR6v8UNtao6s6qWV9X+9F6HL1TVScCNwAndak3UBaCqHgK+m+SFXdErgI00+Np0vgMcleTZ3ftue30W6/Vpuu2C7XeY2X77tNgHA0w4MOC3gW8A3wTet9jxzDD2l9Mb5rkDuL27/Ta936puAO4F/l9gz8WOdYb1Oga4upt+AXAzsBm4DPj5xY5vBvU4DFjfvT5/Azyv5dcG+ADwD8BdwKeBn1/M16flttvFb/sd4pvtd/qbZ4CTJKlxwzTMLkmSZsFkLklS40zmkiQ1zmQuSVLjTOaSJDXOZK6nSXJ8kkryosWORdLM2H53XiZzTXQi8OXuXlJbbL87KZO5ntKdl/rl9C6994au7OeS/LfuOsLXJ7kmyQndssOT/F2SW5Nct/3UipIWnu1352Yy13jH0btm8DeA7yU5HPg/gP3pXaf6TfQuzbf9PNZ/AZxQVYcDFwIfWoygJQG2353a0ulX0U7kRHoXZIDeBRpOpPceuayqfgo8lOTGbvkLgUOA63unF2YJvcv5SVoctt+dmMlcACTZk97Vlf51kqLXuAv4/FSbAHdX1UsXKERJU7D9ymF2bXcC8Omq+pdVtX9V7Qt8C/g+8G+73972pnfhBoB7gLEkTw3bJTl4MQKXZPvd2ZnMtd2JPPNb/OXAL9G7nvBG4P8Bvg78z6r6Mb0PkHOSbKB3lamXLVy4ksax/e7kvGqappVk96p6PMnz6V2i7+jqXWNY0pCz/e4c/M1c/bg6yR7ArsCf+EEgNcX2uxOwZy5JUuP8zVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGmcwlSWrc/w9SBKbHJL99HgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 506.88x475.2 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1pPVp4i2gCv",
        "colab_type": "text"
      },
      "source": [
        "> ## **Age and Sex**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnLgyyDF1ckN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "005a0dfb-cb28-4b45-8f60-592f13fd5d5b"
      },
      "source": [
        "women = dt_train[dt_train['Sex']=='female']\n",
        "plt.hist(women[women['Survived']==1].Age.dropna(), bins=18, label = \"Survived\", color=\"grey\", alpha=0.4)\n",
        "plt.hist(women[women['Survived']==0].Age.dropna(), bins=36, label = \"Not Survived\", color=\"orange\", alpha=0.4)\n",
        "plt.legend()\n",
        "plt.title('Female')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7SjSoilrxMJA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "f2f216a0-5c9f-4376-90e1-6987c0471614"
      },
      "source": [
        "men = dt_train[dt_train['Sex']=='male']\n",
        "plt.hist(men[men['Survived']==1].Age.dropna(), bins=18, label = \"Survived\", color=\"grey\", alpha=0.4)\n",
        "plt.hist(men[men['Survived']==0].Age.dropna(), bins=36, label = \"Not Survived\", color=\"orange\", alpha=0.4)\n",
        "plt.legend()\n",
        "plt.title('Male')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-sTUxJQBImp",
        "colab_type": "text"
      },
      "source": [
        "> ## **SibSp and Parch**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsHMNVA9BUDZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "outputId": "ce877c9b-2e70-4cee-9ee6-df1e3fffa968"
      },
      "source": [
        "compare = pd.concat([dt_train.SibSp, dt_train.Parch, dt_train.Survived], axis = 1, keys=[\"SibSp\", \"Parch\", \"Survived\"])\n",
        "compare[\"Relatives\"] = compare[\"SibSp\"] + compare[\"Parch\"]\n",
        "compare.head(17)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Relatives</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    SibSp  Parch  Survived  Relatives\n",
              "0       1      0         0          1\n",
              "1       1      0         1          1\n",
              "2       0      0         1          0\n",
              "3       1      0         1          1\n",
              "4       0      0         0          0\n",
              "5       0      0         0          0\n",
              "6       0      0         0          0\n",
              "7       3      1         0          4\n",
              "8       0      2         1          2\n",
              "9       1      0         1          1\n",
              "10      1      1         1          2\n",
              "11      0      0         1          0\n",
              "12      0      0         0          0\n",
              "13      1      5         0          6\n",
              "14      0      0         0          0\n",
              "15      0      0         1          0\n",
              "16      4      1         0          5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 981
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWc_uxKXC2gY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "9bd0e8dc-caa3-43ab-e911-4ac9cd7dcb9c"
      },
      "source": [
        "axes = sns.catplot('Relatives','Survived', data=compare, aspect = 2.5, kind=\"point\" )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qe2ZZjQILbUG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "5bd8a048-ab92-4740-9b98-3d5ad8083078"
      },
      "source": [
        "axes = sns.catplot('SibSp','Survived', data=compare, aspect = 2.5, kind=\"point\" )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUxZkxmsNghf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "f7c94faf-3090-4841-cb06-6756305a59bc"
      },
      "source": [
        "axes = sns.catplot('Parch','Survived', data=compare, aspect = 2.5, kind=\"point\" )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwoQtCwOXK2u",
        "colab_type": "text"
      },
      "source": [
        "> ## **Ticket and Cabin**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kxlfIEuz91A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "63be0710-92b3-478f-d053-9be2a092ff8f"
      },
      "source": [
        "len(dt_train.Ticket.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "681"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 985
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjMHTw3X0TLh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "301d68d8-70f5-431a-ea14-306375bfa75c"
      },
      "source": [
        "dt_train.Ticket.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 986
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIVGnutTXSxm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "243a0302-c0ab-4192-c51f-6b8fe16244c6"
      },
      "source": [
        "compare = pd.concat([dt_train.Ticket, dt_train.Cabin], axis = 1, keys=[\"Ticket\", \"Cabin\"])\n",
        "compare.head(7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PC 17599</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>113803</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>373450</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>330877</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>17463</td>\n",
              "      <td>E46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Ticket Cabin\n",
              "0         A/5 21171   NaN\n",
              "1          PC 17599   C85\n",
              "2  STON/O2. 3101282   NaN\n",
              "3            113803  C123\n",
              "4            373450   NaN\n",
              "5            330877   NaN\n",
              "6             17463   E46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 987
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHdWns-Cf5Fm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f2fa2fa5-8bc4-40a8-91d0-0f22270bcc93"
      },
      "source": [
        "(891-681)/891 * 100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.56902356902357"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 988
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tfdit0Loi69G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "7cc82612-aa52-4faa-bd52-d92186a641cd"
      },
      "source": [
        "compare['Cabin_Filled'] = compare['Ticket'].map(compare.groupby('Ticket')['Cabin'].first())\n",
        "compare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Cabin_Filled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PC 17599</td>\n",
              "      <td>C85</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>113803</td>\n",
              "      <td>C123</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>373450</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>211536</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>112053</td>\n",
              "      <td>B42</td>\n",
              "      <td>B42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>111369</td>\n",
              "      <td>C148</td>\n",
              "      <td>C148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>370376</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Ticket Cabin Cabin_Filled\n",
              "0           A/5 21171   NaN          NaN\n",
              "1            PC 17599   C85          C85\n",
              "2    STON/O2. 3101282   NaN          NaN\n",
              "3              113803  C123         C123\n",
              "4              373450   NaN          NaN\n",
              "..                ...   ...          ...\n",
              "886            211536   NaN          NaN\n",
              "887            112053   B42          B42\n",
              "888        W./C. 6607   NaN          NaN\n",
              "889            111369  C148         C148\n",
              "890            370376   NaN          NaN\n",
              "\n",
              "[891 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 989
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNkQPTgQ-BkK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "74577557-2e4a-4df6-9f9e-8aa9d194f431"
      },
      "source": [
        "compare.Cabin.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "687"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 990
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8t7IQKh-ea4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9fd6af59-7d3e-4993-9ad9-75d18105db68"
      },
      "source": [
        "compare.Cabin_Filled.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "676"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 991
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2GnXpRq-2uI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d52db0ab-99ef-448a-a64a-c04f26265fba"
      },
      "source": [
        "compare.Cabin_Filled.isnull().count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 992
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsluUrbF-_6Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e39b2fb2-6cd8-4cf7-a3cb-d75c433fac9e"
      },
      "source": [
        "(891-676)/891 * 100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.130190796857466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 993
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ifan_OG_E4Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "26481b4c-d4c5-4f12-cf8f-5f0ce2c79cb2"
      },
      "source": [
        "(687/891 * 100)-(676/891 * 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.2345679012345698"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 994
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G1BfbiEDBz6I",
        "colab_type": "text"
      },
      "source": [
        "> ## **Fare**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbg02KD5CH5S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "af4d9e2a-06e5-4b0a-abbd-d6a35070ed2a"
      },
      "source": [
        "class1 = dt_train[dt_train['Pclass']==1]\n",
        "plt.hist(class1[class1['Survived']==1].Fare.dropna(), bins=18, label = \"Survived\", color=\"blue\", alpha=0.4)\n",
        "plt.hist(class1[class1['Survived']==0].Fare.dropna(), bins=18, label = \"Not Survived\", color=\"lightblue\", alpha=0.8)\n",
        "plt.legend()\n",
        "plt.title('Class 1')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtO-FTsWEMDh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "7059afdf-38c6-4d97-c279-3cbba1cf2682"
      },
      "source": [
        "class2 = dt_train[dt_train['Pclass']==2]\n",
        "plt.hist(class2[class2['Survived']==1].Fare.dropna(), bins=18, label = \"Survived\", color=\"blue\", alpha=0.4)\n",
        "plt.hist(class2[class2['Survived']==0].Fare.dropna(), bins=18, label = \"Not Survived\", color=\"lightblue\", alpha=0.8)\n",
        "plt.legend()\n",
        "plt.title('Class 2')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zgLSueDEW9v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "3e4260f5-67f7-4a04-f77c-1fb3bb21e47b"
      },
      "source": [
        "class3 = dt_train[dt_train['Pclass']==3]\n",
        "plt.hist(class3[class3['Survived']==1].Fare.dropna(), bins=18, label = \"Survived\", color=\"blue\", alpha=0.4)\n",
        "plt.hist(class3[class3['Survived']==0].Fare.dropna(), bins=18, label = \"Not Survived\", color=\"lightblue\", alpha=0.8)\n",
        "plt.legend()\n",
        "plt.title('Class 3')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_E1eHcQLsPN",
        "colab_type": "text"
      },
      "source": [
        "> ## **Embarked, Pclass and Sex**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxhGojJXL21A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "outputId": "1795768e-5b7f-445f-96be-e6745d35052b"
      },
      "source": [
        "FacetGrid = sns.FacetGrid(dt_train, row='Embarked', height=4.5, aspect=1.6)\n",
        "FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None,  order=None, hue_order=None )\n",
        "FacetGrid.add_legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fe8e15019e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 998
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 588.9x972 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJGke5Q7_AUt",
        "colab_type": "text"
      },
      "source": [
        "> ## **Name and Cabin**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vf8dJwgGBRKz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "328e7cc2-79b2-48be-aff6-fb67e7ac827d"
      },
      "source": [
        "len(dt_train.Name.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 999
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vbU3ZKMMBtgs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "76c689c5-24f3-4451-cb34-7fb03046521c"
      },
      "source": [
        "dt_train.Name.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1000
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afBax-uUAq_6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "c8e16a43-d83f-47a2-865f-78b654f37883"
      },
      "source": [
        "compare = pd.concat([dt_train.Name, dt_train.Cabin], axis = 1, keys=[\"Name\", \"Cabin\"])\n",
        "compare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Cabin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>B42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>C148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Name Cabin\n",
              "0                              Braund, Mr. Owen Harris   NaN\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...   C85\n",
              "2                               Heikkinen, Miss. Laina   NaN\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  C123\n",
              "4                             Allen, Mr. William Henry   NaN\n",
              "..                                                 ...   ...\n",
              "886                              Montvila, Rev. Juozas   NaN\n",
              "887                       Graham, Miss. Margaret Edith   B42\n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"   NaN\n",
              "889                              Behr, Mr. Karl Howell  C148\n",
              "890                                Dooley, Mr. Patrick   NaN\n",
              "\n",
              "[891 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1001
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OnVYjG-CCtd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lastname= []\n",
        "for i in range(0, dt_train.Name.count()):\n",
        "  last = compare[\"Name\"][i]\n",
        "  last = last.split()[0]\n",
        "  lastname.append(last)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md6KDaRrHx5U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "fc08b09d-42a1-4554-8f5e-507110d842d7"
      },
      "source": [
        "compare[\"Lastname\"] = np.array(lastname)\n",
        "compare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Lastname</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Braund,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>C85</td>\n",
              "      <td>Cumings,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Heikkinen,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>C123</td>\n",
              "      <td>Futrelle,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Allen,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montvila,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>B42</td>\n",
              "      <td>Graham,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Johnston,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>C148</td>\n",
              "      <td>Behr,</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dooley,</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Name Cabin    Lastname\n",
              "0                              Braund, Mr. Owen Harris   NaN     Braund,\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...   C85    Cumings,\n",
              "2                               Heikkinen, Miss. Laina   NaN  Heikkinen,\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  C123   Futrelle,\n",
              "4                             Allen, Mr. William Henry   NaN      Allen,\n",
              "..                                                 ...   ...         ...\n",
              "886                              Montvila, Rev. Juozas   NaN   Montvila,\n",
              "887                       Graham, Miss. Margaret Edith   B42     Graham,\n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"   NaN   Johnston,\n",
              "889                              Behr, Mr. Karl Howell  C148       Behr,\n",
              "890                                Dooley, Mr. Patrick   NaN     Dooley,\n",
              "\n",
              "[891 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1003
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8h6FJbvIVmy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4038bb63-c178-4a03-9d0b-dc7e0a6e7cd5"
      },
      "source": [
        "len(compare.Lastname.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "661"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1004
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42QFB0w5Idka",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c41131a6-344c-4275-da55-889acaabadba"
      },
      "source": [
        "compare.Lastname.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1005
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eJK4QbJzFvg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "6793fa88-0cfd-40c6-fb27-bdf466f5746b"
      },
      "source": [
        "compare['Cabin_Filled'] = compare['Lastname'].map(compare.groupby('Lastname')['Cabin'].first())\n",
        "compare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Lastname</th>\n",
              "      <th>Cabin_Filled</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Braund,</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>C85</td>\n",
              "      <td>Cumings,</td>\n",
              "      <td>C85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Heikkinen,</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>C123</td>\n",
              "      <td>Futrelle,</td>\n",
              "      <td>C123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Allen,</td>\n",
              "      <td>B5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montvila,</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>B42</td>\n",
              "      <td>Graham,</td>\n",
              "      <td>C125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Johnston,</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>C148</td>\n",
              "      <td>Behr,</td>\n",
              "      <td>C148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dooley,</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  Name  ... Cabin_Filled\n",
              "0                              Braund, Mr. Owen Harris  ...          NaN\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  ...          C85\n",
              "2                               Heikkinen, Miss. Laina  ...          NaN\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  ...         C123\n",
              "4                             Allen, Mr. William Henry  ...           B5\n",
              "..                                                 ...  ...          ...\n",
              "886                              Montvila, Rev. Juozas  ...          NaN\n",
              "887                       Graham, Miss. Margaret Edith  ...         C125\n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  ...          NaN\n",
              "889                              Behr, Mr. Karl Howell  ...         C148\n",
              "890                                Dooley, Mr. Patrick  ...          NaN\n",
              "\n",
              "[891 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1006
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vatgt9rP1h4U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4db2350c-8929-4032-f4d0-98780fa685fa"
      },
      "source": [
        "compare.Cabin.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "687"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1007
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKEIJdlj1jeM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "632a9e6d-ab2d-45c4-ddd7-a523f012ac60"
      },
      "source": [
        "compare.Cabin_Filled.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "664"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1008
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kJR4eVC1nAW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c256c150-fc8c-4b13-d4a0-9c20c5438f2d"
      },
      "source": [
        "compare.Cabin.isnull().count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1009
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FHvtCyLG2B7r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c4f3be8c-4d5c-409f-fa85-1e7cec99db22"
      },
      "source": [
        "(891-664)/891 * 100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.476992143658812"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1010
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X3fEzTNX2HX0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5d08cc7b-dbfd-4cb9-ad19-fb1ce73d20c9"
      },
      "source": [
        "664/891 *100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74.5230078563412"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1011
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7tkLj042MaO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "10e0e77b-7d69-430f-8541-59ab28e1e9f9"
      },
      "source": [
        "687/891 * 100\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.10437710437711"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1012
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtMTn1D52R5M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4d8fbf74-778a-42e4-f087-8eec56bb02c7"
      },
      "source": [
        "(687-664)/891 * 100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.581369248035915"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1013
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMCapmqE9WDg",
        "colab_type": "text"
      },
      "source": [
        "# **Data Preprocessing: Missing Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ly9JglLr9lPK",
        "colab_type": "text"
      },
      "source": [
        "> ## **Cabin**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rIIs60yA-HIa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "deck = {\"A\": 1,\"B\": 2, \"C\":3,\"D\": 4, \"E\": 5,\"F\": 6, \"G\": 7,\"U\": 8}\n",
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "  i['Cabin_Filled'] = i['Ticket'].map(i.groupby('Ticket')['Cabin'].first())\n",
        "  lastname= []\n",
        "  for j in range(0, i.Name.count()):\n",
        "    last = i[\"Name\"][j]\n",
        "    last = last.split()[0]\n",
        "    lastname.append(last)\n",
        "  i[\"Lastname\"] = np.array(lastname)\n",
        "  i['Cabin_Filled'] = i['Lastname'].map(i.groupby('Lastname')['Cabin'].first())\n",
        "  i['Cabin_Filled'] = i['Cabin_Filled'].fillna(\"U0\")\n",
        "  i['Deck'] = i['Cabin_Filled'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
        "  i['Deck'] = i['Deck'].map(deck)\n",
        "  i['Deck'] = i['Deck'].fillna(0)\n",
        "  i['Deck'] = i['Deck'].astype(int)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DL2riOzcC7Iy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "6a05e5c3-38d6-4476-862d-27e1a7d29c2f"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Cabin_Filled</th>\n",
              "      <th>Lastname</th>\n",
              "      <th>Deck</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>U0</td>\n",
              "      <td>Braund,</td>\n",
              "      <td>8</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>C85</td>\n",
              "      <td>Cumings,</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>U0</td>\n",
              "      <td>Heikkinen,</td>\n",
              "      <td>8</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>C123</td>\n",
              "      <td>Futrelle,</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>B5</td>\n",
              "      <td>Allen,</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin_Filled    Lastname  Deck\n",
              "0            1         0       3  ...           U0     Braund,     8\n",
              "1            2         1       1  ...          C85    Cumings,     3\n",
              "2            3         1       3  ...           U0  Heikkinen,     8\n",
              "3            4         1       1  ...         C123   Futrelle,     3\n",
              "4            5         0       3  ...           B5      Allen,     2\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1015
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OedMRnu5DRDv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "33e2ea5f-2e70-450d-c8fa-ae6643be4717"
      },
      "source": [
        "dt_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
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              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Wilkes,</td>\n",
              "      <td>8</td>\n",
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              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
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              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>U0</td>\n",
              "      <td>Myles,</td>\n",
              "      <td>8</td>\n",
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              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>U0</td>\n",
              "      <td>Wirz,</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>U0</td>\n",
              "      <td>Hirvonen,</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  ...   Lastname Deck\n",
              "0          892       3  ...     Kelly,    8\n",
              "1          893       3  ...    Wilkes,    8\n",
              "2          894       2  ...     Myles,    8\n",
              "3          895       3  ...      Wirz,    8\n",
              "4          896       3  ...  Hirvonen,    8\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1016
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGOAevfeDP8S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dt_train = dt_train.drop([\"Cabin\",\"Cabin_Filled\",\"Lastname\",\"Ticket\"], axis=1)\n",
        "dt_test = dt_test.drop([\"Cabin\",\"Cabin_Filled\",\"Lastname\",\"Ticket\"], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MC9rxPnuEWzb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "cb2b973f-75fa-4b99-f9c1-e0502de4b860"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <th>Name</th>\n",
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              "      <td>C</td>\n",
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              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Embarked  Deck\n",
              "0            1         0       3  ...   7.2500        S     8\n",
              "1            2         1       1  ...  71.2833        C     3\n",
              "2            3         1       3  ...   7.9250        S     8\n",
              "3            4         1       1  ...  53.1000        S     3\n",
              "4            5         0       3  ...   8.0500        S     2\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1018
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kEP0TkLEe5q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "37e419a0-797f-4b0c-da58-9827ea852978"
      },
      "source": [
        "dt_test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "      <th>Sex</th>\n",
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              "      <th>0</th>\n",
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              "      <td>3</td>\n",
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              "      <td>female</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
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              "      <td>male</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>Q</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Embarked Deck\n",
              "0          892       3  ...        Q    8\n",
              "1          893       3  ...        S    8\n",
              "2          894       2  ...        Q    8\n",
              "3          895       3  ...        S    8\n",
              "4          896       3  ...        S    8\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1019
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvYl0hwFFiIZ",
        "colab_type": "text"
      },
      "source": [
        "> ## **Age**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GB8rR9bKRjht",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b70bc332-33a6-432a-a8e6-1682dac27359"
      },
      "source": [
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "    mean = dt_train[\"Age\"].mean()\n",
        "    std = dt_test[\"Age\"].std()\n",
        "    is_null = i[\"Age\"].isnull().sum()\n",
        "    randomAge = np.random.randint(mean - std, mean + std, size = is_null)\n",
        "    ageGroup = i[\"Age\"].copy()\n",
        "    ageGroup[np.isnan(ageGroup)] = randomAge\n",
        "    i[\"Age\"] = ageGroup\n",
        "    i[\"Age\"] = dt_train[\"Age\"].astype(int)\n",
        "dt_train[\"Age\"].isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1020
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuR5ntJ2Dl4g",
        "colab_type": "text"
      },
      "source": [
        "> ## **Embarked**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBqzmRHuDvPH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "01135bd5-af27-463a-9022-3a447f2579e5"
      },
      "source": [
        "dt_train[\"Embarked\"].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     889\n",
              "unique      3\n",
              "top         S\n",
              "freq      644\n",
              "Name: Embarked, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1021
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pFTwbvUD4xQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "    i['Embarked'] = i['Embarked'].fillna(\"S\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34mijEV_Ey-_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "3b373e3b-2a77-46d6-999f-e40729718dbf"
      },
      "source": [
        "dt_train[\"Embarked\"].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     891\n",
              "unique      3\n",
              "top         S\n",
              "freq      646\n",
              "Name: Embarked, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1023
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8S8h5JtE6JY",
        "colab_type": "text"
      },
      "source": [
        "# **Data Preprocessing: Converting Features**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVTCvHtCsJsO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "54a07470-a4c6-4d5b-bbfb-68d283d2dd23"
      },
      "source": [
        "dt_train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 11 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    int64  \n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Fare         891 non-null    float64\n",
            " 9   Embarked     891 non-null    object \n",
            " 10  Deck         891 non-null    int64  \n",
            "dtypes: float64(1), int64(7), object(3)\n",
            "memory usage: 76.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyGvqExDtAu0",
        "colab_type": "text"
      },
      "source": [
        "> ## **PassengerId**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jH71CAvYtE7E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dt_train = dt_train.drop([\"PassengerId\"], axis=1)\n",
        "dt_test = dt_test.drop([\"PassengerId\"], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PEP38GpiuLhU",
        "colab_type": "text"
      },
      "source": [
        "> ## **Name**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3058S98uVJk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "56d3caf6-1561-4c3d-fcac-fa9cd266b586"
      },
      "source": [
        "data=[dt_train, dt_test]\n",
        "titles = set()\n",
        "for i in data:\n",
        "  for name in i['Name']:\n",
        "    titles.add(name.split(',')[1].split('.')[0].strip())\n",
        "\n",
        "print(titles)\n",
        "# set(['Sir', 'Major', 'the Countess', 'Don', 'Mlle', 'Capt', 'Dr', 'Lady', 'Rev', 'Mrs', 'Jonkheer', 'Master', 'Ms', 'Mr', 'Mme', 'Miss', 'Col'])\n",
        "\n",
        "Title_Dictionary = {\n",
        "    \"Capt\": \"Officer\",\n",
        "    \"Col\": \"Officer\",\n",
        "    \"Major\": \"Officer\",\n",
        "    \"Jonkheer\": \"Royalty\",\n",
        "    \"Don\": \"Royalty\",\n",
        "    \"Dona\": \"Royalty\",\n",
        "    \"Sir\" : \"Royalty\",\n",
        "    \"Dr\": \"Officer\",\n",
        "    \"Rev\": \"Officer\",\n",
        "    \"the Countess\":\"Royalty\",\n",
        "    \"Mme\": \"Mrs\",\n",
        "    \"Mlle\": \"Miss\",\n",
        "    \"Ms\": \"Mrs\",\n",
        "    \"Mr\" : \"Mr\",\n",
        "    \"Mrs\" : \"Mrs\",\n",
        "    \"Miss\" : \"Miss\",\n",
        "    \"Master\" : \"Master\",\n",
        "    \"Lady\" : \"Royalty\"\n",
        "}\n",
        "\n",
        "for i in data:\n",
        "  # we extract the title from each name\n",
        "  i['Title'] = i['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
        "  # a map of more aggregated title\n",
        "  # we map each title\n",
        "  i['Title'] = i.Title.map(Title_Dictionary)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Don', 'Mme', 'Sir', 'the Countess', 'Major', 'Ms', 'Mrs', 'Master', 'Lady', 'Capt', 'Mr', 'Rev', 'Dr', 'Dona', 'Mlle', 'Miss', 'Jonkheer', 'Col'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6KsAhzYxEwb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dt_train =dt_train.drop([\"Name\"],axis =1)\n",
        "dt_test = dt_test.drop([\"Name\"], axis =1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sH2Ikn7xpI8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titleEnc = LabelEncoder()\n",
        "data = [dt_train,dt_test]\n",
        "for i in data:\n",
        "  i[\"Title\"]= titleEnc.fit_transform(i[\"Title\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAggle3ZvZA9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "c02a9ece-b376-4a77-f8c5-208d96eebf08"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Deck</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex  Age  SibSp  Parch     Fare Embarked  Deck  Title\n",
              "0         0       3    male   22      1      0   7.2500        S     8      2\n",
              "1         1       1  female   38      1      0  71.2833        C     3      3\n",
              "2         1       3  female   26      0      0   7.9250        S     8      1\n",
              "3         1       1  female   35      1      0  53.1000        S     3      3\n",
              "4         0       3    male   35      0      0   8.0500        S     2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1029
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL6H5tP_0i80",
        "colab_type": "text"
      },
      "source": [
        "> ## **Sex**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEnzoku10uzD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sexEnc = LabelEncoder()\n",
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "  i[\"Sex\"] = sexEnc.fit_transform(i[\"Sex\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dftUNWgw1C_W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "705fec1b-b2eb-4884-dbcd-0a216bf8a6b4"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Deck</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Deck  Title\n",
              "0         0       3    1   22      1      0   7.2500        S     8      2\n",
              "1         1       1    0   38      1      0  71.2833        C     3      3\n",
              "2         1       3    0   26      0      0   7.9250        S     8      1\n",
              "3         1       1    0   35      1      0  53.1000        S     3      3\n",
              "4         0       3    1   35      0      0   8.0500        S     2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1031
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LV64z0ReAw_z",
        "colab_type": "text"
      },
      "source": [
        "> ## **SibSp and Parch**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxEt6GiEA2VG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dt_train = dt_train.drop([\"SibSp\",\"Parch\"], axis =1)\n",
        "dt_test = dt_test.drop([\"SibSp\",\"Parch\"], axis =1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hdlKikCBKrw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "9581ca6e-5841-48fd-cd52-3841100e3da3"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Deck</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age     Fare Embarked  Deck  Title\n",
              "0         0       3    1   22   7.2500        S     8      2\n",
              "1         1       1    0   38  71.2833        C     3      3\n",
              "2         1       3    0   26   7.9250        S     8      1\n",
              "3         1       1    0   35  53.1000        S     3      3\n",
              "4         0       3    1   35   8.0500        S     2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1033
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEmN3WmC1PvD",
        "colab_type": "text"
      },
      "source": [
        "> ## **Fare**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmXbTZeQ1dIb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "    i['Fare'] = i['Fare'].fillna(0)\n",
        "    i['Fare'] = i['Fare'].astype(int)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7aEqdwIN16jq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "c47fd4e4-9bed-4b44-ca14-9e21e0e295fb"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Deck</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>7</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>71</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>S</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>53</td>\n",
              "      <td>S</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>8</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  Fare Embarked  Deck  Title\n",
              "0         0       3    1   22     7        S     8      2\n",
              "1         1       1    0   38    71        C     3      3\n",
              "2         1       3    0   26     7        S     8      1\n",
              "3         1       1    0   35    53        S     3      3\n",
              "4         0       3    1   35     8        S     2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1035
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clZgQIoY2Eeq",
        "colab_type": "text"
      },
      "source": [
        "> ## **Embarked**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-pGf1Aw2JOq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embarkedEnc = LabelEncoder()\n",
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "  i[\"Embarked\"] = embarkedEnc.fit_transform(i[\"Embarked\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSuO8WhQ37SD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "cffa1d7b-c5e0-4511-a297-eda02fa4ad76"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Deck</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>71</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  Fare  Embarked  Deck  Title\n",
              "0         0       3    1   22     7         2     8      2\n",
              "1         1       1    0   38    71         0     3      3\n",
              "2         1       3    0   26     7         2     8      1\n",
              "3         1       1    0   35    53         2     3      3\n",
              "4         0       3    1   35     8         2     2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1037
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJWkXiD24-79",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "2f87b915-0899-4ca3-a88a-59a3727633b6"
      },
      "source": [
        "dt_train.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            " #   Column    Non-Null Count  Dtype\n",
            "---  ------    --------------  -----\n",
            " 0   Survived  891 non-null    int64\n",
            " 1   Pclass    891 non-null    int64\n",
            " 2   Sex       891 non-null    int64\n",
            " 3   Age       891 non-null    int64\n",
            " 4   Fare      891 non-null    int64\n",
            " 5   Embarked  891 non-null    int64\n",
            " 6   Deck      891 non-null    int64\n",
            " 7   Title     891 non-null    int64\n",
            "dtypes: int64(8)\n",
            "memory usage: 55.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxmIZ0Pc5w0r",
        "colab_type": "text"
      },
      "source": [
        "# **Creating Categories**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4eHISNz5-my",
        "colab_type": "text"
      },
      "source": [
        "> ## **Age**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbUs55916BzC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=[dt_train, dt_test]\n",
        "for i in data:\n",
        "  i.loc[ i['Age'] <= 11, 'Age'] = 0\n",
        "  i.loc[(i['Age'] > 11) & (i['Age'] <= 18), 'Age'] = 1\n",
        "  i.loc[(i['Age'] > 18) & (i['Age'] <= 22), 'Age'] = 2\n",
        "  i.loc[(i['Age'] > 22) & (i['Age'] <= 27), 'Age'] = 3\n",
        "  i.loc[(i['Age'] > 27) & (i['Age'] <= 33), 'Age'] = 4\n",
        "  i.loc[(i['Age'] > 33) & (i['Age'] <= 40), 'Age'] = 5\n",
        "  i.loc[(i['Age'] > 40) & (i['Age'] <= 66), 'Age'] = 6\n",
        "  i.loc[ i['Age'] > 66, 'Age'] = 6\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3t_i85V8aD8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "a8f5e14c-0465-486c-9a24-5ed096436ecf"
      },
      "source": [
        "dt_train.Age.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    169\n",
              "6    158\n",
              "5    152\n",
              "3    132\n",
              "2    110\n",
              "1    102\n",
              "0     68\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1040
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7T2kYxi85iK",
        "colab_type": "text"
      },
      "source": [
        "> ## **Fare**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1COBXyJ8_4y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f3176730-0a93-4426-a451-6a400bbf608b"
      },
      "source": [
        "pd.qcut(dt_train[\"Fare\"], q=7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      (-0.001, 7.0]\n",
              "1      (56.0, 512.0]\n",
              "2      (-0.001, 7.0]\n",
              "3       (27.0, 56.0]\n",
              "4         (7.0, 8.0]\n",
              "           ...      \n",
              "886     (12.0, 19.0]\n",
              "887     (27.0, 56.0]\n",
              "888     (19.0, 27.0]\n",
              "889     (27.0, 56.0]\n",
              "890    (-0.001, 7.0]\n",
              "Name: Fare, Length: 891, dtype: category\n",
              "Categories (7, interval[float64]): [(-0.001, 7.0] < (7.0, 8.0] < (8.0, 12.0] < (12.0, 19.0] <\n",
              "                                    (19.0, 27.0] < (27.0, 56.0] < (56.0, 512.0]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1041
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nquTo4ut9n5q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = [dt_train, dt_test]\n",
        "for i in data:\n",
        "  i.loc[i[\"Fare\"]<=7.0, \"Fare\"] = 0\n",
        "  i.loc[(i[\"Fare\"]> 7.0) & (i[\"Fare\"]<=8.0), \"Fare\"] = 1\n",
        "  i.loc[(i[\"Fare\"]> 8.0) & (i[\"Fare\"]<=12.0), \"Fare\"] = 2\n",
        "  i.loc[(i[\"Fare\"]> 12.0) & (i[\"Fare\"]<=19.0), \"Fare\"] = 3\n",
        "  i.loc[(i[\"Fare\"]> 19.0) & (i[\"Fare\"]<=27.0), \"Fare\"] = 4\n",
        "  i.loc[(i[\"Fare\"]> 27.0) & (i[\"Fare\"]<=56.0), \"Fare\"] = 5\n",
        "  i.loc[(i[\"Fare\"]> 56.0) & (i[\"Fare\"]<=512.0), \"Fare\"] = 7\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eGYKLF-_AyE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "68d856aa-40ac-46eb-cce2-ce78f8724b4e"
      },
      "source": [
        "dt_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Deck</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  Fare  Embarked  Deck  Title\n",
              "0         0       3    1    2     0         2     8      2\n",
              "1         1       1    0    5     7         0     3      3\n",
              "2         1       3    0    3     0         2     8      1\n",
              "3         1       1    0    5     5         2     3      3\n",
              "4         0       3    1    5     1         2     2      2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1043
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaGH4pkSFlhO",
        "colab_type": "text"
      },
      "source": [
        "# **Building the Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoU2iEN3CSV4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = dt_train.drop(\"Survived\", axis=1)\n",
        "Y_train = dt_train[\"Survived\"]\n",
        "X_test  = dt_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmZL5H71Frzf",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "> ## **Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sx7-kUjnFzmZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b64a291d-3538-468d-94ae-199581f860ce"
      },
      "source": [
        "LogReg = LogisticRegression(random_state = 0)\n",
        "LogReg.fit(x_train, y_train)\n",
        "y_pred = LogReg.predict(x_test)\n",
        "accLogReg = round(LogReg.score(x_train,y_train) * 100, 2)\n",
        "print(accLogReg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.68\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zf8B1pfcIWWA",
        "colab_type": "text"
      },
      "source": [
        "> ## **K- Nearest Neighbors Classification**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eP5uxhgBIs4O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "47cc7022-72f9-4319-9865-3dd096e37918"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "knn.fit(x_train, y_train)\n",
        "y_pred = knn.predict(x_test)\n",
        "accKnn = round(knn.score(x_train,y_train)*100, 2)\n",
        "print(accKnn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "83.84\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwN71AiMJKf0",
        "colab_type": "text"
      },
      "source": [
        "> ## **Support Vector Classification(Linear)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2S5yf9lJh4V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "790f35f3-83d1-4340-c055-20e41e243830"
      },
      "source": [
        "svc = SVC(kernel = 'linear', random_state = 0)\n",
        "svc.fit(x_train, y_train)\n",
        "y_pred = svc.predict(x_test)\n",
        "accSvc = round(svc.score(x_train,y_train)*100, 2)\n",
        "print(accSvc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.68\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcfGArtiJvKM",
        "colab_type": "text"
      },
      "source": [
        "> ## **Support Vector Classification(rbf)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pj-10hA1KFTe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1aee3028-4a9a-429f-da44-1f771fdb1f03"
      },
      "source": [
        "rbf = SVC(kernel = 'rbf', random_state = 0)\n",
        "rbf.fit(x_train, y_train)\n",
        "y_pred = rbf.predict(x_test)\n",
        "accRbf = round(rbf.score(x_train,y_train)*100, 2)\n",
        "print(accRbf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80.47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uG1b8l75KUjW",
        "colab_type": "text"
      },
      "source": [
        "> ## **Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2ncUmeYKdaP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d5dcf204-040e-4826-d83f-fb3e01cd93c4"
      },
      "source": [
        "gnb = GaussianNB()\n",
        "gnb.fit(x_train, y_train)\n",
        "y_pred = gnb.predict(x_test)\n",
        "accGnb = round(gnb.score(x_train,y_train)*100, 2)\n",
        "print(accGnb)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAme_Du6K2Ul",
        "colab_type": "text"
      },
      "source": [
        "> ## **Decision Tree Classification**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iadt96O9LJTj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c18fcb39-35a0-4aec-94a4-0c63241e4f57"
      },
      "source": [
        "dtc = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "dtc.fit(x_train, y_train)\n",
        "y_pred = dtc.predict(x_test)\n",
        "accDtc = round(dtc.score(x_train,y_train)*100, 2)\n",
        "print(accDtc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sSO9tY2GLWuu",
        "colab_type": "text"
      },
      "source": [
        "> ## **Random Forest Classification**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nANI_zrTLoZ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f13c29ec-5bae-453b-aecb-917490d5dccd"
      },
      "source": [
        "rfc = RandomForestClassifier(n_estimators = 100, criterion = 'entropy', random_state = 0)\n",
        "rfc.fit(x_train, y_train)\n",
        "y_pred = rfc.predict(x_test)\n",
        "accRfc = round(rfc.score(x_train,y_train)*100, 2)\n",
        "print(accRfc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HJTFtm5MdxX",
        "colab_type": "text"
      },
      "source": [
        "# **Comparing the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVirn5SVMipT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "dbb94131-18d3-43da-cf30-b2624d5553cd"
      },
      "source": [
        "results = pd.DataFrame({\n",
        "    'Model': [\"Logistic Regression\",\"K-NN Classification\",\"SVC: Linear\",\"SVC: rbf\",\"Gaussian Naive Bayes\",\"Decision Tree Classification\",\"Random Forest Classification\"],\n",
        "    'Score': [accLogReg, accKnn, accSvc, accRbf, accGnb, accDtc, accRfc]})\n",
        "result_df = results.sort_values(by='Score')\n",
        "result_df.head(9)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gaussian Naive Bayes</td>\n",
              "      <td>75.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>78.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SVC: Linear</td>\n",
              "      <td>78.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SVC: rbf</td>\n",
              "      <td>80.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K-NN Classification</td>\n",
              "      <td>83.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Decision Tree Classification</td>\n",
              "      <td>90.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Random Forest Classification</td>\n",
              "      <td>90.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          Model  Score\n",
              "4          Gaussian Naive Bayes  75.20\n",
              "0           Logistic Regression  78.68\n",
              "2                   SVC: Linear  78.68\n",
              "3                      SVC: rbf  80.47\n",
              "1           K-NN Classification  83.84\n",
              "5  Decision Tree Classification  90.91\n",
              "6  Random Forest Classification  90.91"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1052
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvoJrcGpTRmb",
        "colab_type": "text"
      },
      "source": [
        "> # **Feature Importance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQl7yRb1TYRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "99f5cd24-aa47-4483-f694-15e712d32d57"
      },
      "source": [
        "importances = pd.DataFrame({'feature':x_train.columns,'importance':np.round(rfc.feature_importances_,3)})\n",
        "importances = importances.sort_values('importance',ascending=False)\n",
        "importances.head(7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sex</td>\n",
              "      <td>0.212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Age</td>\n",
              "      <td>0.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fare</td>\n",
              "      <td>0.179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Title</td>\n",
              "      <td>0.132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pclass</td>\n",
              "      <td>0.122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Deck</td>\n",
              "      <td>0.097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Embarked</td>\n",
              "      <td>0.057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    feature  importance\n",
              "1       Sex       0.212\n",
              "2       Age       0.200\n",
              "3      Fare       0.179\n",
              "6     Title       0.132\n",
              "0    Pclass       0.122\n",
              "5      Deck       0.097\n",
              "4  Embarked       0.057"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1053
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9NnfmxhWlpE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "964d5fef-e19e-4ca9-cd1b-9950e893f610"
      },
      "source": [
        "plt.bar(importances[\"feature\"],height=importances[\"importance\"],label =\"Importance\", width=0.4)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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"
          }
        }
      ]
    }
  ]
}