TASK0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TASK0.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwtzIBpNtyZX"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKQ3nJ9nuNjl",
        "outputId": "e7a26752-40a2-436a-e175-abb5a0db1e6f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90lEwDk-tfWb",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "890f45e1-94f7-426b-ae4f-9cd7bb28ca4d"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-588d7e78-98f3-4fdf-aa61-198f45551992\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-588d7e78-98f3-4fdf-aa61-198f45551992\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving train (1).csv to train (1).csv\n",
            "Saving test.csv to test.csv\n",
            "Saving gender_submission.csv to gender_submission.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwRvpTckQhfi"
      },
      "source": [
        "train = pd.read_csv('/content/train (1).csv')\n",
        "test = pd.read_csv('/content/test.csv')\n",
        "gender=pd.read_csv('/content/gender_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgagR2z1tr77"
      },
      "source": [
        "import io\n",
        "train = pd.read_csv(io.BytesIO(uploaded['train (1).csv']))\n",
        "test = pd.read_csv(io.BytesIO(uploaded['test.csv']))\n",
        "gender = pd.read_csv(io.BytesIO(uploaded['gender_submission.csv']))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AFiv5kP6717t",
        "outputId": "daf01f3e-82af-45d3-9a5d-8ebd854e664f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "U76Gr3G7Q6KT",
        "outputId": "17bb09f6-8d64-4227-8733-671b067fb229"
      },
      "source": [
        "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zds_VqxpRcsH",
        "outputId": "50df67d0-e6ff-4b70-a210-28c09401033f"
      },
      "source": [
        "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",
              "        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>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",
              "  </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",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_adSFingSACd",
        "outputId": "a82a379d-78de-4858-a24d-1470cb9bd1cf"
      },
      "source": [
        "gender.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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         1\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         1"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J8FnBIjPTSos",
        "outputId": "a5869874-2140-4a8b-d8fc-1d2d0d6b063d"
      },
      "source": [
        "train=train.dropna(how='any',axis=0)\n",
        "print(train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  ...     Fare        Cabin  Embarked\n",
            "1              2         1       1  ...  71.2833          C85         C\n",
            "3              4         1       1  ...  53.1000         C123         S\n",
            "6              7         0       1  ...  51.8625          E46         S\n",
            "10            11         1       3  ...  16.7000           G6         S\n",
            "11            12         1       1  ...  26.5500         C103         S\n",
            "..           ...       ...     ...  ...      ...          ...       ...\n",
            "871          872         1       1  ...  52.5542          D35         S\n",
            "872          873         0       1  ...   5.0000  B51 B53 B55         S\n",
            "879          880         1       1  ...  83.1583          C50         C\n",
            "887          888         1       1  ...  30.0000          B42         S\n",
            "889          890         1       1  ...  30.0000         C148         C\n",
            "\n",
            "[183 rows x 12 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hCxwPLfTwmJ",
        "outputId": "d428515d-4c24-4b67-b8fd-857f686c916f"
      },
      "source": [
        "test=test.dropna(how='any',axis=0)\n",
        "print(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Pclass  ...            Cabin Embarked\n",
            "12           904       1  ...              B45        S\n",
            "14           906       1  ...              E31        S\n",
            "24           916       1  ...  B57 B59 B63 B66        C\n",
            "26           918       1  ...              B36        C\n",
            "28           920       1  ...              A21        S\n",
            "..           ...     ...  ...              ...      ...\n",
            "404         1296       1  ...              D40        C\n",
            "405         1297       2  ...              D38        C\n",
            "407         1299       1  ...              C80        C\n",
            "411         1303       1  ...              C78        Q\n",
            "414         1306       1  ...             C105        C\n",
            "\n",
            "[87 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6_miGgXUesF",
        "outputId": "a8ff1e2a-15f3-44b3-a3ea-5ac87e007987"
      },
      "source": [
        "gender=gender.dropna(how='any', axis=0)\n",
        "print(gender)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived\n",
            "0            892         0\n",
            "1            893         1\n",
            "2            894         0\n",
            "3            895         0\n",
            "4            896         1\n",
            "..           ...       ...\n",
            "413         1305         0\n",
            "414         1306         1\n",
            "415         1307         0\n",
            "416         1308         0\n",
            "417         1309         0\n",
            "\n",
            "[418 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryLBxm17-ypE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd657da9-2cb8-49ff-b755-bdf501a4d45f"
      },
      "source": [
        "train.drop('Name', axis=1, inplace=True)\n",
        "\n",
        "print(train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Survived  Pclass  ...     Fare        Cabin  Embarked\n",
            "1              2         1       1  ...  71.2833          C85         C\n",
            "3              4         1       1  ...  53.1000         C123         S\n",
            "6              7         0       1  ...  51.8625          E46         S\n",
            "10            11         1       3  ...  16.7000           G6         S\n",
            "11            12         1       1  ...  26.5500         C103         S\n",
            "..           ...       ...     ...  ...      ...          ...       ...\n",
            "871          872         1       1  ...  52.5542          D35         S\n",
            "872          873         0       1  ...   5.0000  B51 B53 B55         S\n",
            "879          880         1       1  ...  83.1583          C50         C\n",
            "887          888         1       1  ...  30.0000          B42         S\n",
            "889          890         1       1  ...  30.0000         C148         C\n",
            "\n",
            "[183 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bAaSaXFuQyG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5c939c8-33dc-4a19-87ff-be2703f19358"
      },
      "source": [
        "train.drop('PassengerId', axis=1, inplace=True)\n",
        "print(train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Survived  Pclass     Sex   Age  ...    Ticket     Fare        Cabin  Embarked\n",
            "1           1       1  female  38.0  ...  PC 17599  71.2833          C85         C\n",
            "3           1       1  female  35.0  ...    113803  53.1000         C123         S\n",
            "6           0       1    male  54.0  ...     17463  51.8625          E46         S\n",
            "10          1       3  female   4.0  ...   PP 9549  16.7000           G6         S\n",
            "11          1       1  female  58.0  ...    113783  26.5500         C103         S\n",
            "..        ...     ...     ...   ...  ...       ...      ...          ...       ...\n",
            "871         1       1  female  47.0  ...     11751  52.5542          D35         S\n",
            "872         0       1    male  33.0  ...       695   5.0000  B51 B53 B55         S\n",
            "879         1       1  female  56.0  ...     11767  83.1583          C50         C\n",
            "887         1       1  female  19.0  ...    112053  30.0000          B42         S\n",
            "889         1       1    male  26.0  ...    111369  30.0000         C148         C\n",
            "\n",
            "[183 rows x 10 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBpqU4TPucYZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "55a8e990-7ca1-4c3b-a7d9-ae247dc46db6"
      },
      "source": [
        "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>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived  Pclass     Sex   Age  ...    Ticket     Fare Cabin  Embarked\n",
              "1          1       1  female  38.0  ...  PC 17599  71.2833   C85         C\n",
              "3          1       1  female  35.0  ...    113803  53.1000  C123         S\n",
              "6          0       1    male  54.0  ...     17463  51.8625   E46         S\n",
              "10         1       3  female   4.0  ...   PP 9549  16.7000    G6         S\n",
              "11         1       1  female  58.0  ...    113783  26.5500  C103         S\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCSVaxlM44Rb",
        "outputId": "1ce70b18-e197-490f-a977-0472c0b5532a"
      },
      "source": [
        "test.drop('Name', axis=1, inplace=True)\n",
        "print(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Pclass     Sex  ...      Fare            Cabin  Embarked\n",
            "12           904       1  female  ...   82.2667              B45         S\n",
            "14           906       1  female  ...   61.1750              E31         S\n",
            "24           916       1  female  ...  262.3750  B57 B59 B63 B66         C\n",
            "26           918       1  female  ...   61.9792              B36         C\n",
            "28           920       1    male  ...   30.5000              A21         S\n",
            "..           ...     ...     ...  ...       ...              ...       ...\n",
            "404         1296       1    male  ...   27.7208              D40         C\n",
            "405         1297       2    male  ...   13.8625              D38         C\n",
            "407         1299       1    male  ...  211.5000              C80         C\n",
            "411         1303       1  female  ...   90.0000              C78         Q\n",
            "414         1306       1  female  ...  108.9000             C105         C\n",
            "\n",
            "[87 rows x 10 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IEi9NaE5COr",
        "outputId": "b3a59aac-8aae-4b57-c593-6fb98b2f2c87"
      },
      "source": [
        "test.head"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of      PassengerId  Pclass     Sex  ...      Fare            Cabin  Embarked\n",
              "12           904       1  female  ...   82.2667              B45         S\n",
              "14           906       1  female  ...   61.1750              E31         S\n",
              "24           916       1  female  ...  262.3750  B57 B59 B63 B66         C\n",
              "26           918       1  female  ...   61.9792              B36         C\n",
              "28           920       1    male  ...   30.5000              A21         S\n",
              "..           ...     ...     ...  ...       ...              ...       ...\n",
              "404         1296       1    male  ...   27.7208              D40         C\n",
              "405         1297       2    male  ...   13.8625              D38         C\n",
              "407         1299       1    male  ...  211.5000              C80         C\n",
              "411         1303       1  female  ...   90.0000              C78         Q\n",
              "414         1306       1  female  ...  108.9000             C105         C\n",
              "\n",
              "[87 rows x 10 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUCv5Go_5Ldo",
        "outputId": "6ef5dffb-ed2c-43c1-c8ed-1f6d3b06e347"
      },
      "source": [
        "test.drop('PassengerId', axis=1, inplace=True)\n",
        "print(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pclass     Sex   Age  ...      Fare            Cabin Embarked\n",
            "12        1  female  23.0  ...   82.2667              B45        S\n",
            "14        1  female  47.0  ...   61.1750              E31        S\n",
            "24        1  female  48.0  ...  262.3750  B57 B59 B63 B66        C\n",
            "26        1  female  22.0  ...   61.9792              B36        C\n",
            "28        1    male  41.0  ...   30.5000              A21        S\n",
            "..      ...     ...   ...  ...       ...              ...      ...\n",
            "404       1    male  43.0  ...   27.7208              D40        C\n",
            "405       2    male  20.0  ...   13.8625              D38        C\n",
            "407       1    male  50.0  ...  211.5000              C80        C\n",
            "411       1  female  37.0  ...   90.0000              C78        Q\n",
            "414       1  female  39.0  ...  108.9000             C105        C\n",
            "\n",
            "[87 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kz1PsC8k55gI",
        "outputId": "08257d11-3117-4384-853e-a3b26d101a45"
      },
      "source": [
        "test.head"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of      Pclass     Sex   Age  ...      Fare            Cabin Embarked\n",
              "12        1  female  23.0  ...   82.2667              B45        S\n",
              "14        1  female  47.0  ...   61.1750              E31        S\n",
              "24        1  female  48.0  ...  262.3750  B57 B59 B63 B66        C\n",
              "26        1  female  22.0  ...   61.9792              B36        C\n",
              "28        1    male  41.0  ...   30.5000              A21        S\n",
              "..      ...     ...   ...  ...       ...              ...      ...\n",
              "404       1    male  43.0  ...   27.7208              D40        C\n",
              "405       2    male  20.0  ...   13.8625              D38        C\n",
              "407       1    male  50.0  ...  211.5000              C80        C\n",
              "411       1  female  37.0  ...   90.0000              C78        Q\n",
              "414       1  female  39.0  ...  108.9000             C105        C\n",
              "\n",
              "[87 rows x 9 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYYbXpF_9UcP",
        "outputId": "8f48eda9-52be-4e7c-ce1c-05efbd306995"
      },
      "source": [
        "x=train.shape[0]\n",
        "Y=(80/100)*x \n",
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "146.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHfJUn_XCMwh",
        "outputId": "5c8ac893-139f-4ab0-910e-d55e0c05d966"
      },
      "source": [
        "Y=int(Y)\n",
        "print(train.iloc[0:Y])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Survived  Pclass     Sex   Age  ...    Ticket      Fare    Cabin  Embarked\n",
            "1           1       1  female  38.0  ...  PC 17599   71.2833      C85         C\n",
            "3           1       1  female  35.0  ...    113803   53.1000     C123         S\n",
            "6           0       1    male  54.0  ...     17463   51.8625      E46         S\n",
            "10          1       3  female   4.0  ...   PP 9549   16.7000       G6         S\n",
            "11          1       1  female  58.0  ...    113783   26.5500     C103         S\n",
            "..        ...     ...     ...   ...  ...       ...       ...      ...       ...\n",
            "698         0       1    male  49.0  ...     17421  110.8833      C68         C\n",
            "699         0       3    male  42.0  ...    348121    7.6500    F G63         S\n",
            "700         1       1  female  18.0  ...  PC 17757  227.5250  C62 C64         C\n",
            "701         1       1    male  35.0  ...  PC 17475   26.2875      E24         S\n",
            "707         1       1    male  42.0  ...  PC 17476   26.2875      E24         S\n",
            "\n",
            "[146 rows x 10 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEMFwp_ADNiC",
        "outputId": "2dfbcaa8-d09b-4c66-d219-83d92f054532"
      },
      "source": [
        "print(train.iloc[Y:x])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Survived  Pclass     Sex  ...      Fare            Cabin  Embarked\n",
            "710         1       1  female  ...   49.5042              C90         C\n",
            "712         1       1    male  ...   52.0000             C126         S\n",
            "715         0       3    male  ...    7.6500            F G73         S\n",
            "716         1       1  female  ...  227.5250              C45         C\n",
            "717         1       2  female  ...   10.5000             E101         S\n",
            "724         1       1    male  ...   53.1000               E8         S\n",
            "730         1       1  female  ...  211.3375               B5         S\n",
            "737         1       1    male  ...  512.3292             B101         C\n",
            "741         0       1    male  ...   78.8500              C46         S\n",
            "742         1       1  female  ...  262.3750  B57 B59 B63 B66         C\n",
            "745         0       1    male  ...   71.0000              B22         S\n",
            "748         0       1    male  ...   53.1000              D30         S\n",
            "751         1       3    male  ...   12.4750             E121         S\n",
            "759         1       1  female  ...   86.5000              B77         S\n",
            "763         1       1  female  ...  120.0000          B96 B98         S\n",
            "765         1       1  female  ...   77.9583              D11         S\n",
            "772         0       2  female  ...   10.5000              E77         S\n",
            "779         1       1  female  ...  211.3375               B3         S\n",
            "781         1       1  female  ...   57.0000              B20         S\n",
            "782         0       1    male  ...   30.0000               D6         S\n",
            "789         0       1    male  ...   79.2000          B82 B84         C\n",
            "796         1       1  female  ...   25.9292              D17         S\n",
            "802         1       1    male  ...  120.0000          B96 B98         S\n",
            "806         0       1    male  ...    0.0000              A36         S\n",
            "809         1       1  female  ...   53.1000               E8         S\n",
            "820         1       1  female  ...   93.5000              B69         S\n",
            "823         1       3  female  ...   12.4750             E121         S\n",
            "835         1       1  female  ...   83.1583              E49         C\n",
            "853         1       1  female  ...   39.4000              D28         S\n",
            "857         1       1    male  ...   26.5500              E17         S\n",
            "862         1       1  female  ...   25.9292              D17         S\n",
            "867         0       1    male  ...   50.4958              A24         S\n",
            "871         1       1  female  ...   52.5542              D35         S\n",
            "872         0       1    male  ...    5.0000      B51 B53 B55         S\n",
            "879         1       1  female  ...   83.1583              C50         C\n",
            "887         1       1  female  ...   30.0000              B42         S\n",
            "889         1       1    male  ...   30.0000             C148         C\n",
            "\n",
            "[37 rows x 10 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzY8-3-cDl6I",
        "outputId": "dce34b0d-c343-49c3-91f2-cc950941ae31"
      },
      "source": [
        "z=test.shape[0]\n",
        "y=(80/100)*z\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "69.60000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaZbs0b8Ew3B",
        "outputId": "8813d5e8-aa89-47b2-ee5e-e4facddf605c"
      },
      "source": [
        "y=int(y)\n",
        "print(test.iloc[0:y])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pclass     Sex   Age  ...      Fare            Cabin Embarked\n",
            "12        1  female  23.0  ...   82.2667              B45        S\n",
            "14        1  female  47.0  ...   61.1750              E31        S\n",
            "24        1  female  48.0  ...  262.3750  B57 B59 B63 B66        C\n",
            "26        1  female  22.0  ...   61.9792              B36        C\n",
            "28        1    male  41.0  ...   30.5000              A21        S\n",
            "..      ...     ...   ...  ...       ...              ...      ...\n",
            "322       2    male  26.0  ...   13.0000               F2        S\n",
            "326       2  female  12.0  ...   39.0000               F4        S\n",
            "331       1    male  39.0  ...   29.7000              A18        C\n",
            "335       1    male  30.0  ...   26.0000             C106        S\n",
            "343       1  female  58.0  ...  512.3292      B51 B53 B55        C\n",
            "\n",
            "[69 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTYWHeTyE702",
        "outputId": "e9b53ad1-5257-4dcd-f75a-91167993e31b"
      },
      "source": [
        "print(test.iloc[y:z])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Pclass     Sex   Age  SibSp  ...         Ticket      Fare        Cabin Embarked\n",
            "350       1  female  45.0      0  ...       PC 17759   63.3583      D10 D12        C\n",
            "355       1    male  50.0      0  ...         113044   26.0000          E60        S\n",
            "356       1  female  59.0      2  ...          11769   51.4792         C101        S\n",
            "364       1  female  25.0      1  ...          11765   55.4417          E50        C\n",
            "371       1  female  31.0      0  ...          16966  134.5000      E39 E41        C\n",
            "372       1    male  49.0      0  ...         112058    0.0000  B52 B54 B56        S\n",
            "374       1  female  54.0      1  ...          33638   81.8583          A34        S\n",
            "378       1    male  55.0      0  ...            680   50.0000          C39        S\n",
            "390       1    male  23.0      0  ...          12749   93.5000          B24        S\n",
            "391       1  female  51.0      0  ...       PC 17592   39.4000          D28        S\n",
            "395       1  female  18.0      1  ...          13695   60.0000          C31        S\n",
            "397       1  female  48.0      1  ...          13567   79.2000          B41        C\n",
            "400       1  female  30.0      0  ...          36928  164.8667           C7        S\n",
            "404       1    male  43.0      1  ...          17765   27.7208          D40        C\n",
            "405       2    male  20.0      0  ...  SC/PARIS 2166   13.8625          D38        C\n",
            "407       1    male  50.0      1  ...         113503  211.5000          C80        C\n",
            "411       1  female  37.0      1  ...          19928   90.0000          C78        Q\n",
            "414       1  female  39.0      0  ...       PC 17758  108.9000         C105        C\n",
            "\n",
            "[18 rows x 9 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "8AjId9PYFS94",
        "outputId": "0af48b95-caa8-4e24-ab3b-9228c97a56c4"
      },
      "source": [
        "train.hist(column=\"Fare\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATkElEQVR4nO3dYYxlZ33f8e8vXgwbj7trYxht1y4LwjV12OLEU2MEamfsGDk4jf3CsUAWXSJX+yaJjLpVu7Rqq0iNal5sCHXailVMWEUbBmpw1zIKyWbxNGpVTLzYsDbGtXHXxQ7MBLy7MLBKuuTfF3PWnY7Hnjuzc+/1M/f7ka7ueZ57ztz/H65/c/aZc+9NVSFJas9PDbsASdLaGOCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4Nqwkx5OcTjK/6PY3h12XtF42DbsAqc/+YVX9yWoPShIgVfXXfahJWheegWtkJLkoyQNJ/iLJiW770kWPzyT5zST/Hfgx8JYkb0tyOMkLSZ5MctvwOpD+fwa4RslPAb8HvAn4W8Bp4HeW7PNBYDdwIfAXwGHgD4A3Au8H/mOSKwdVsPRKDHBtdP8lyckkJ4F7qupzVfXjqvoh8JvAP1iy/6eq6vGqOgPcCByvqt+rqjNV9QjwOeCXB9uCtDzXwLXR3XJ2DTzJTyf5BAvBfFH3+IVJzquqn3Tjby869k3AO7vwP2sT8Pv9LlrqhQGuUbIHuAJ4Z1V9N8lVwCNAFu2z+OM5vw3816q6YYA1Sj1zCUWj5EIW1r1PJrkY+Dcr7P8A8LeTfDDJa7rb30vyd/peqdQDA1yj5LeBzcD3gC8DX3ylnbt18vey8MfLPwe+C3wUeG1/y5R6E7/QQZLa5Bm4JDXKAJekRhngktQoA1ySGjXQ68AvueSS2rFjx5qO/dGPfsQFF1ywvgW9So1SrzBa/Y5SrzBa/faz16NHj36vqt6wdH6gAb5jxw4efvjhNR07MzPD5OTk+hb0KjVKvcJo9TtKvcJo9dvPXpM8u9y8SyiS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktSoZr5S7djzp/jQ3i8M/HmP33XTwJ9TknrhGbgkNWrFAE9yRZJHF91+kOTDSS5OcjjJU939RSv9LEnS+lkxwKvqyaq6qqquAq4GfgzcB+wFjlTV5cCRbixJGpDVLqFcD3yrqp4FbgYOdPMHgFvWszBJ0itb1ZcaJ/kk8NWq+p0kJ6tqazcf4MTZ8ZJjdgO7AcbHx6+enp5eU6FzL5xi9vSaDm3O+GaYPQ07t28ZdikDMT8/z9jY2LDLGIhR6hVGq99+9jo1NXW0qiaWzvcc4EnOB/4c+Jmqml0c4N3jJ6rqFdfBJyYmaq2fB373wUPsO9bMRTPnZM/OM+w7tmlkroDxM6M3rlHqt8+fB75sgK9mCeUXWDj7nu3Gs0m2dT98GzB37mVKknq1mgD/APDpReP7gV3d9i7g0HoVJUlaWU8BnuQC4Abg84um7wJuSPIU8PPdWJI0ID0tKlfVj4DXL5n7PgtXpUiShsB3YkpSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVG9fiv91iT3JvlmkieSvCvJxUkOJ3mqu7+o38VKkv6fXs/APw58sareBrwDeALYCxypqsuBI91YkjQgKwZ4ki3A3wfuAaiqv6qqk8DNwIFutwPALf0qUpL0UqmqV94huQrYD3yDhbPvo8CdwPNVtbXbJ8CJs+Mlx+8GdgOMj49fPT09vaZC5144xezpNR3anPHNMHsadm7fMuxSBmJ+fp6xsbFhlzEQo9QrjFa//ex1amrqaFVNLJ3vJcAngC8D766qh5J8HPgB8OuLAzvJiap6xXXwiYmJevjhh9fUwN0HD7Hv2KY1HduaPTvPsO/YJo7fddOwSxmImZkZJicnh13GQIxSrzBa/faz1yTLBngva+DPAc9V1UPd+F7g54DZJNu6H74NmFuvYiVJK1sxwKvqu8C3k1zRTV3PwnLK/cCubm4XcKgvFUqSltXrmsSvAweTnA88A/wKC+H/2SR3AM8Ct/WnREnScnoK8Kp6FHjJ+gsLZ+OSpCHwnZiS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWpUT19qnOQ48EPgJ8CZqppIcjHwGWAHcBy4rapO9KdMSdJSqzkDn6qqq6rq7LfT7wWOVNXlwJFuLEkakHNZQrkZONBtHwBuOfdyJEm9SlWtvFPyv4ATQAGfqKr9SU5W1dbu8QAnzo6XHLsb2A0wPj5+9fT09JoKnXvhFLOn13Roc8Y3w+xp2Ll9y7BLGYj5+XnGxsaGXcZAjFKvMFr99rPXqampo4tWP17U0xo48J6qej7JG4HDSb65+MGqqiTL/iaoqv3AfoCJiYmanJxcXeWduw8eYt+xXstt256dZ9h3bBPHb58cdikDMTMzw1pfF60ZpV5htPodRq89LaFU1fPd/RxwH3ANMJtkG0B3P9evIiVJL7VigCe5IMmFZ7eB9wKPAfcDu7rddgGH+lWkJOmlelmTGAfuW1jmZhPwB1X1xSR/Bnw2yR3As8Bt/StTkrTUigFeVc8A71hm/vvA9f0oSpK0Mt+JKUmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRvUc4EnOS/JIkge68ZuTPJTk6SSfSXJ+/8qUJC21mjPwO4EnFo0/Cnysqt4KnADuWM/CJEmvrKcAT3IpcBPwu904wHXAvd0uB4Bb+lGgJGl5qaqVd0ruBf4dcCHwT4EPAV/uzr5Jchnwh1X19mWO3Q3sBhgfH796enp6TYXOvXCK2dNrOrQ545th9jTs3L5l2KUMxPz8PGNjY8MuYyBGqVcYrX772evU1NTRqppYOr9ppQOT/CIwV1VHk0yu9omraj+wH2BiYqImJ1f9IwC4++Ah9h1bsdwNYc/OM+w7tonjt08Ou5SBmJmZYa2vi9aMUq8wWv0Oo9deEvHdwC8leR/wOuBvAB8HtibZVFVngEuB5/tXpiRpqRXXwKvqI1V1aVXtAN4PfKmqbgceBG7tdtsFHOpblZKklziX68D/OfBPkjwNvB64Z31KkiT1YlWLylU1A8x0288A16x/SZKkXvhOTElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRKwZ4ktcl+UqSryV5PMlvdPNvTvJQkqeTfCbJ+f0vV5J0Vi9n4H8JXFdV7wCuAm5Mci3wUeBjVfVW4ARwR//KlCQttWKA14L5bvia7lbAdcC93fwB4Ja+VChJWlZPa+BJzkvyKDAHHAa+BZysqjPdLs8B2/tToiRpOamq3ndOtgL3Af8K+FS3fEKSy4A/rKq3L3PMbmA3wPj4+NXT09NrKnTuhVPMnl7Toc0Z3wyzp2Hn9i3DLmUg5ufnGRsbG3YZAzFKvcJo9dvPXqempo5W1cTS+U2r+SFVdTLJg8C7gK1JNnVn4ZcCz7/MMfuB/QATExM1OTm52toBuPvgIfYdW1W5zdqz8wz7jm3i+O2Twy5lIGZmZljr66I1o9QrjFa/w+i1l6tQ3tCdeZNkM3AD8ATwIHBrt9su4FC/ipQkvVQvp7TbgANJzmMh8D9bVQ8k+QYwneTfAo8A9/SxTknSEisGeFV9HfjZZeafAa7pR1GSpJX5TkxJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNWo0PmC7UTv2fmEoz3v8rpuG8rySVsczcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGrVigCe5LMmDSb6R5PEkd3bzFyc5nOSp7v6i/pcrSTqrlzPwM8CeqroSuBb41SRXAnuBI1V1OXCkG0uSBmTFAK+q71TVV7vtHwJPANuBm4ED3W4HgFv6VaQk6aVSVb3vnOwA/hR4O/C/q2prNx/gxNnxkmN2A7sBxsfHr56enl5ToXMvnGL29JoObc74Zoba687tWwb6fPPz84yNjQ30OYdllHqF0eq3n71OTU0draqJpfM9fxZKkjHgc8CHq+oHC5m9oKoqybK/CapqP7AfYGJioiYnJ1dZ+oK7Dx5i37HR+OiWPTvPDLXX47dPDvT5ZmZmWOvrojWj1CuMVr/D6LWnq1CSvIaF8D5YVZ/vpmeTbOse3wbM9adESdJyerkKJcA9wBNV9VuLHrof2NVt7wIOrX95kqSX08u/098NfBA4luTRbu5fAHcBn01yB/AscFt/SpQkLWfFAK+q/wbkZR6+fn3LkST1yndiSlKjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUSsGeJJPJplL8tiiuYuTHE7yVHd/UX/LlCQt1csZ+KeAG5fM7QWOVNXlwJFuLEkaoBUDvKr+FHhhyfTNwIFu+wBwyzrXJUlaQapq5Z2SHcADVfX2bnyyqrZ22wFOnB0vc+xuYDfA+Pj41dPT02sqdO6FU8yeXtOhzRnfzFB73bl9y0Cfb35+nrGxsYE+57CMUq8wWv32s9epqamjVTWxdH7Tuf7gqqokL/tboKr2A/sBJiYmanJyck3Pc/fBQ+w7ds7lNmHPzjND7fX47ZMDfb6ZmRnW+rpozSj1CqPV7zB6XetVKLNJtgF093PrV5IkqRdrDfD7gV3d9i7g0PqUI0nqVS+XEX4a+B/AFUmeS3IHcBdwQ5KngJ/vxpKkAVpxobWqPvAyD12/zrVIklZhNP4qqGbs2PuFoTzv8btuGsrzSufCt9JLUqMMcElqlAEuSY0ywCWpUQa4JDXKq1AkBnf1y56dZ/jQkufyChitlWfgktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhrlW+kljYx+fmTCch+TcFa/Pi7BM3BJapQBLkmNOqcllCQ3Ah8HzgN+t6r8dvoNYNDfS/lK//SU9PLWfAae5DzgPwC/AFwJfCDJletVmCTplZ3LEso1wNNV9UxV/RUwDdy8PmVJklaSqlrbgcmtwI1V9Y+78QeBd1bVry3ZbzewuxteATy5xlovAb63xmNbM0q9wmj1O0q9wmj1289e31RVb1g62ffLCKtqP7D/XH9OkoeramIdSnrVG6VeYbT6HaVeYbT6HUav57KE8jxw2aLxpd2cJGkAziXA/wy4PMmbk5wPvB+4f33KkiStZM1LKFV1JsmvAX/EwmWEn6yqx9etspc652WYhoxSrzBa/Y5SrzBa/Q681zX/EVOSNFy+E1OSGmWAS1KjmgjwJDcmeTLJ00n2Druec5Xkk0nmkjy2aO7iJIeTPNXdX9TNJ8m/73r/epKfG17lq5fksiQPJvlGkseT3NnNb9R+X5fkK0m+1vX7G938m5M81PX1me4P/yR5bTd+unt8xzDrX4sk5yV5JMkD3XhD9prkeJJjSR5N8nA3N9TX8as+wDfoW/Y/Bdy4ZG4vcKSqLgeOdGNY6Pvy7rYb+E8DqnG9nAH2VNWVwLXAr3b//23Ufv8SuK6q3gFcBdyY5Frgo8DHquqtwAngjm7/O4AT3fzHuv1acyfwxKLxRu51qqquWnS993Bfx1X1qr4B7wL+aNH4I8BHhl3XOvS1A3hs0fhJYFu3vQ14stv+BPCB5fZr8QYcAm4YhX6Bnwa+CryThXfobermX3xNs3AV17u67U3dfhl27avo8VIWgus64AEgG7jX48AlS+aG+jp+1Z+BA9uBby8aP9fNbTTjVfWdbvu7wHi3vWH67/7J/LPAQ2zgfrslhUeBOeAw8C3gZFWd6XZZ3NOL/XaPnwJeP9iKz8lvA/8M+Otu/Ho2bq8F/HGSo91HhMCQX8d+I8+rUFVVkg11fWeSMeBzwIer6gdJXnxso/VbVT8BrkqyFbgPeNuQS+qLJL8IzFXV0SSTw65nAN5TVc8neSNwOMk3Fz84jNdxC2fgo/KW/dkk2wC6+7luvvn+k7yGhfA+WFWf76Y3bL9nVdVJ4EEWlhG2Jjl7wrS4pxf77R7fAnx/wKWu1buBX0pynIVPI72Ohe8H2Ii9UlXPd/dzLPxivoYhv45bCPBRecv+/cCubnsXC2vFZ+f/UfdX7WuBU4v+yfaql4VT7XuAJ6rqtxY9tFH7fUN35k2SzSys9z/BQpDf2u22tN+z/zvcCnypukXTV7uq+khVXVpVO1j47/JLVXU7G7DXJBckufDsNvBe4DGG/Toe9h8GevzjwfuA/8nCWuK/HHY969DPp4HvAP+HhbWxO1hYCzwCPAX8CXBxt29YuArnW8AxYGLY9a+y1/ewsHb4deDR7va+Ddzv3wUe6fp9DPjX3fxbgK8ATwP/GXhtN/+6bvx09/hbht3DGvueBB7YqL12PX2tuz1+NoeG/Tr2rfSS1KgWllAkScswwCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1Kj/i+qKhKXJppbXwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "bkrXKZSEGCpy",
        "outputId": "a5ff51f2-aac0-4e01-d934-3b28f46b4d6d"
      },
      "source": [
        "train.hist(column=\"Age\");"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "G5fyvqzIGGjW",
        "outputId": "d30fb61d-3746-4ab8-9288-8e222e353f39"
      },
      "source": [
        "test.hist(column=\"Fare\");"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Gw8y3XbtGKWF",
        "outputId": "1e793dbd-d988-4fea-8fff-74bddabe78ae"
      },
      "source": [
        "test.hist(column=\"Age\");"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "5bIx1bEvHpsS",
        "outputId": "d47f7546-8a89-4db5-914f-ec19e654b67a"
      },
      "source": [
        "train.plot(x=\"Age\", y=[\"Survived\", \"Pclass\"], kind=\"bar\");"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}