ML_titanic.ipynb

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  "metadata": {
    "colab": {
      "name": "Titanic.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNVNrMUkGIZJXCx2ZrtZxXJ"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "ME6HrEalWrew"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yR_CwEbRW9k4"
      },
      "source": [
        "# Reading data\n",
        "train = pd.read_csv('https://github.com/gromdimon/Files/raw/main/train_for_titanic.csv')\n",
        "test = pd.read_csv('https://github.com/gromdimon/Files/raw/main/test_for_titanic.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "tQFbKE3GXPFX",
        "outputId": "80fcac63-6ce1-4901-88fb-8cfedd069c27"
      },
      "source": [
        "# Show dataframes\n",
        "train.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <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",
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              "    </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",
              "  </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",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "egW1r5CYBhOl",
        "outputId": "e8d7312e-2eb0-4649-8290-3a640f5f30da"
      },
      "source": [
        "test.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
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              "    <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",
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            ],
            "text/plain": [
              "   PassengerId  Pclass                              Name  ...    Fare  Cabin  Embarked\n",
              "0          892       3                  Kelly, Mr. James  ...  7.8292    NaN         Q\n",
              "1          893       3  Wilkes, Mrs. James (Ellen Needs)  ...  7.0000    NaN         S\n",
              "2          894       2         Myles, Mr. Thomas Francis  ...  9.6875    NaN         Q\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "u6oGuvGtBmvJ",
        "outputId": "96fde6c3-b7e2-4cf7-b7a3-ae4655d80ecb"
      },
      "source": [
        "# Unification of test and train datasets for EDA\n",
        "mix = pd.concat([train, test], axis=0)\n",
        "mix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>SibSp</th>\n",
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              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</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>1.0</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",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>male</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1309 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...      Fare Cabin  Embarked\n",
              "0              1       0.0       3  ...    7.2500   NaN         S\n",
              "1              2       1.0       1  ...   71.2833   C85         C\n",
              "2              3       1.0       3  ...    7.9250   NaN         S\n",
              "3              4       1.0       1  ...   53.1000  C123         S\n",
              "4              5       0.0       3  ...    8.0500   NaN         S\n",
              "..           ...       ...     ...  ...       ...   ...       ...\n",
              "413         1305       NaN       3  ...    8.0500   NaN         S\n",
              "414         1306       NaN       1  ...  108.9000  C105         C\n",
              "415         1307       NaN       3  ...    7.2500   NaN         S\n",
              "416         1308       NaN       3  ...    8.0500   NaN         S\n",
              "417         1309       NaN       3  ...   22.3583   NaN         C\n",
              "\n",
              "[1309 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "C_6cL6HJXtC8",
        "outputId": "e375fd68-6336-4467-9249-4eaaa6515488"
      },
      "source": [
        "# Transforming to numerical values Sex colomn\n",
        "sex = mix.Sex.map({'male':1, 'female':0})\n",
        "sex = sex.rename('sex')\n",
        "train_new = pd.concat([mix, sex], axis=1)\n",
        "train_new.drop(columns='Sex')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>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>sex</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17758</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>C105</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Saether, Mr. Simon Sivertsen</td>\n",
              "      <td>38.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1309 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... Cabin  Embarked  sex\n",
              "0              1       0.0       3  ...   NaN         S    1\n",
              "1              2       1.0       1  ...   C85         C    0\n",
              "2              3       1.0       3  ...   NaN         S    0\n",
              "3              4       1.0       1  ...  C123         S    0\n",
              "4              5       0.0       3  ...   NaN         S    1\n",
              "..           ...       ...     ...  ...   ...       ...  ...\n",
              "413         1305       NaN       3  ...   NaN         S    1\n",
              "414         1306       NaN       1  ...  C105         C    0\n",
              "415         1307       NaN       3  ...   NaN         S    1\n",
              "416         1308       NaN       3  ...   NaN         S    1\n",
              "417         1309       NaN       3  ...   NaN         C    1\n",
              "\n",
              "[1309 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYjY2vqUZE12"
      },
      "source": [
        "# Deletion of unusefull columns\n",
        "train_new = train_new.drop(columns=['Name', 'Ticket'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuIU4lX5ZQhB",
        "outputId": "636a0e14-f09c-4fdd-f942-02d602f74cee"
      },
      "source": [
        "# Investigation of the data type in the column Embarked for subsequent conversion\n",
        "train_new.Embarked.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    914\n",
              "C    270\n",
              "Q    123\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVCz9x--aEsR"
      },
      "source": [
        "# Transformating Embarked column\n",
        "train_f = pd.get_dummies(train_new.Embarked, drop_first=False)\n",
        "train_f\n",
        "train_final = pd.concat([train_new, train_f], axis=1)\n",
        "final = train_final.drop(columns=['Sex', 'Cabin', 'Embarked'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fStVpsYhFD4j"
      },
      "source": [
        "# Saving intermediate data\n",
        "final.to_csv('fin.csv')\n",
        "fin = pd.read_csv('fin.csv', index_col='PassengerId')\n",
        "fin = fin.drop(columns='Unnamed: 0')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWdKcxPgbSRS",
        "outputId": "35c9afab-70e2-4be4-cf93-8bb192caf4fd"
      },
      "source": [
        "# Checking Data for Nans\n",
        "fin.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    418\n",
              "Pclass        0\n",
              "Age         263\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          1\n",
              "sex           0\n",
              "C             0\n",
              "Q             0\n",
              "S             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "W_Zj3ODBbe4h",
        "outputId": "cdccb10b-282c-462c-f03a-bc52455da253"
      },
      "source": [
        "# Droping Nan row\n",
        "fin.loc[fin.Fare.isnull()]"
      ],
      "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>sex</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1044</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>60.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Survived  Pclass   Age  SibSp  Parch  Fare  sex  C  Q  S\n",
              "PassengerId                                                          \n",
              "1044              NaN       3  60.5      0      0   NaN    1  0  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Li3eImoiFlBa",
        "outputId": "8a9940c1-0ad2-47d9-d493-67630be677a3"
      },
      "source": [
        "finla = fin.drop(1044)\n",
        "finla.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1308 entries, 1 to 1309\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Survived  891 non-null    float64\n",
            " 1   Pclass    1308 non-null   int64  \n",
            " 2   Age       1045 non-null   float64\n",
            " 3   SibSp     1308 non-null   int64  \n",
            " 4   Parch     1308 non-null   int64  \n",
            " 5   Fare      1308 non-null   float64\n",
            " 6   sex       1308 non-null   int64  \n",
            " 7   C         1308 non-null   int64  \n",
            " 8   Q         1308 non-null   int64  \n",
            " 9   S         1308 non-null   int64  \n",
            "dtypes: float64(3), int64(7)\n",
            "memory usage: 112.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-rkt2sHaHDQD",
        "outputId": "1855b48b-4511-4819-acc0-7b0fb5fc26a6"
      },
      "source": [
        "# working with Age column\n",
        "finla_age = finla.Age.fillna(finla.Age.mean())\n",
        "finla_age.rename('agge', inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId\n",
              "1       22.000000\n",
              "2       38.000000\n",
              "3       26.000000\n",
              "4       35.000000\n",
              "5       35.000000\n",
              "          ...    \n",
              "1305    29.851837\n",
              "1306    39.000000\n",
              "1307    38.500000\n",
              "1308    29.851837\n",
              "1309    29.851837\n",
              "Name: agge, Length: 1308, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "4Txn7HvxIRV2",
        "outputId": "7a79914f-0388-4e69-a590-f458a616fcb1"
      },
      "source": [
        "dat = pd.concat([finla, finla_age], axis=1)\n",
        "dat.drop(columns='Age', inplace=True)\n",
        "dat"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.000000</td>\n",
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              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>38.500000</td>\n",
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              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1309</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1308 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Survived  Pclass  SibSp  Parch      Fare  sex  C  Q  S       agge\n",
              "PassengerId                                                                   \n",
              "1                 0.0       3      1      0    7.2500    1  0  0  1  22.000000\n",
              "2                 1.0       1      1      0   71.2833    0  1  0  0  38.000000\n",
              "3                 1.0       3      0      0    7.9250    0  0  0  1  26.000000\n",
              "4                 1.0       1      1      0   53.1000    0  0  0  1  35.000000\n",
              "5                 0.0       3      0      0    8.0500    1  0  0  1  35.000000\n",
              "...               ...     ...    ...    ...       ...  ... .. .. ..        ...\n",
              "1305              NaN       3      0      0    8.0500    1  0  0  1  29.851837\n",
              "1306              NaN       1      0      0  108.9000    0  1  0  0  39.000000\n",
              "1307              NaN       3      0      0    7.2500    1  0  0  1  38.500000\n",
              "1308              NaN       3      0      0    8.0500    1  0  0  1  29.851837\n",
              "1309              NaN       3      1      1   22.3583    1  1  0  0  29.851837\n",
              "\n",
              "[1308 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "PFeK5ynwJGrT",
        "outputId": "a0b23a35-ab39-49e7-daa4-d418f7d9ef12"
      },
      "source": [
        "# Setting again Test Data\n",
        "test_ = dat[891:]\n",
        "test_"
      ],
      "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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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>sex</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>agge</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>34.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>38.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Survived  Pclass  SibSp  Parch      Fare  sex  C  Q  S       agge\n",
              "PassengerId                                                                   \n",
              "892               NaN       3      0      0    7.8292    1  0  1  0  34.500000\n",
              "893               NaN       3      1      0    7.0000    0  0  0  1  47.000000\n",
              "894               NaN       2      0      0    9.6875    1  0  1  0  62.000000\n",
              "895               NaN       3      0      0    8.6625    1  0  0  1  27.000000\n",
              "896               NaN       3      1      1   12.2875    0  0  0  1  22.000000\n",
              "...               ...     ...    ...    ...       ...  ... .. .. ..        ...\n",
              "1305              NaN       3      0      0    8.0500    1  0  0  1  29.851837\n",
              "1306              NaN       1      0      0  108.9000    0  1  0  0  39.000000\n",
              "1307              NaN       3      0      0    7.2500    1  0  0  1  38.500000\n",
              "1308              NaN       3      0      0    8.0500    1  0  0  1  29.851837\n",
              "1309              NaN       3      1      1   22.3583    1  1  0  0  29.851837\n",
              "\n",
              "[417 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "ittCBKdnJgeM",
        "outputId": "fac50934-2ba7-4499-9609-aebbb220aa32"
      },
      "source": [
        "# And Train Data\n",
        "train_ = dat[:891]\n",
        "train_"
      ],
      "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>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>sex</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>agge</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>891</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>32.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Survived  Pclass  SibSp  Parch     Fare  sex  C  Q  S       agge\n",
              "PassengerId                                                                  \n",
              "1                 0.0       3      1      0   7.2500    1  0  0  1  22.000000\n",
              "2                 1.0       1      1      0  71.2833    0  1  0  0  38.000000\n",
              "3                 1.0       3      0      0   7.9250    0  0  0  1  26.000000\n",
              "4                 1.0       1      1      0  53.1000    0  0  0  1  35.000000\n",
              "5                 0.0       3      0      0   8.0500    1  0  0  1  35.000000\n",
              "...               ...     ...    ...    ...      ...  ... .. .. ..        ...\n",
              "887               0.0       2      0      0  13.0000    1  0  0  1  27.000000\n",
              "888               1.0       1      0      0  30.0000    0  0  0  1  19.000000\n",
              "889               0.0       3      1      2  23.4500    0  0  0  1  29.851837\n",
              "890               1.0       1      0      0  30.0000    1  1  0  0  26.000000\n",
              "891               0.0       3      0      0   7.7500    1  0  1  0  32.000000\n",
              "\n",
              "[891 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eq6KF8VLJutq",
        "outputId": "1818dfda-50f0-4b06-e6b8-a234f11aaaf1"
      },
      "source": [
        "# Final check of Nans \n",
        "train_.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0\n",
              "Pclass      0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "sex         0\n",
              "C           0\n",
              "Q           0\n",
              "S           0\n",
              "agge        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wID4IZZbcP3H"
      },
      "source": [
        "# Importing Regression ML tool\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8gA6MI6bnzs"
      },
      "source": [
        "# Preparing data for ML\n",
        "X = train_.drop(columns='Survived')\n",
        "Y = train_['Survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jbdqvji1b82w",
        "outputId": "3bea1070-da41-41c0-aa64-06499b23f22d"
      },
      "source": [
        "print(X.shape, Y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 9) (891,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnkkWs58cRqQ",
        "outputId": "979377fd-e082-4518-fe64-1b699d79d416"
      },
      "source": [
        "# Model training\n",
        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "model.fit(X, Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=42, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9UBpDx8KE8L"
      },
      "source": [
        "# Preparing data for test\n",
        "test_f = test_.drop(columns='Survived')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZdmkioTck0O",
        "outputId": "7c855f8d-3296-4b9f-e79b-1ef50a5470f5"
      },
      "source": [
        "# Using model for prediction\n",
        "pred = model.predict(test_f)\n",
        "pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       1.        , 0.1       , 0.06666667, 0.        , 0.97      ,\n",
              "       0.42      , 0.        , 0.63      , 0.16      , 0.10166667,\n",
              "       0.62333333, 0.13844048, 0.15      , 0.16462302, 0.24083333,\n",
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              "       0.01166667, 0.03      , 0.13      , 0.72      , 0.27      ,\n",
              "       0.98      , 0.        , 0.11736106, 0.9       , 0.08      ,\n",
              "       1.        , 0.95      , 0.72      , 0.97      , 0.32      ,\n",
              "       0.06      , 0.18      , 1.        , 0.65      , 0.01      ,\n",
              "       1.        , 0.17      , 0.36980952, 1.        , 1.        ,\n",
              "       0.12      , 0.01      , 0.07      , 0.13      , 0.11736106,\n",
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              "       0.96      , 0.39      , 0.06      , 0.07      , 1.        ,\n",
              "       0.15      , 1.        , 0.025     , 0.01      , 0.99      ,\n",
              "       0.        , 1.        , 0.42      , 0.17166667, 0.63      ,\n",
              "       0.03166667, 0.27      , 0.98963781, 0.8       , 0.87850123,\n",
              "       1.        , 0.28428571, 0.        , 1.        , 0.        ,\n",
              "       0.        , 0.61      ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nKLvFXTiFjM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "560070af-f097-4cd5-8268-4b342050ea68"
      },
      "source": [
        "answer = pred.round()\n",
        "answer.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(417,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkTcv7U1KoDT"
      },
      "source": [
        "# Importing answer dataset for evaluating prediction model\n",
        "ans = pd.read_csv('https://github.com/gromdimon/Files/raw/main/gender_submission_titanic.csv', index_col='PassengerId')\n",
        "ans = ans.drop(1044)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47X73tIWOC_C",
        "outputId": "c401bd91-4626-4ded-a8b8-e9a8b741f53d"
      },
      "source": [
        "an = ans['Survived']\n",
        "an"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId\n",
              "892     0\n",
              "893     1\n",
              "894     0\n",
              "895     0\n",
              "896     1\n",
              "       ..\n",
              "1305    0\n",
              "1306    1\n",
              "1307    0\n",
              "1308    0\n",
              "1309    0\n",
              "Name: Survived, Length: 417, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6htsieGVMdB8",
        "outputId": "5c8523a9-592e-4ae9-d834-dca3b80f1400"
      },
      "source": [
        "# Counting absolut error\n",
        "errors = abs(answer - an)\n",
        "print(round(np.mean(errors), 2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPXxlHK6NHBp",
        "outputId": "6d8e0787-3c5e-4a73-9224-fd410fc31a69"
      },
      "source": [
        "# Counting mean error\n",
        "mape = 100 * (errors / an)\n",
        "accuracy = 100 - np.mean(mape)\n",
        "print('Accuracy:', round(accuracy, 2), '%.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: -inf %.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "rrF569YNRCsH",
        "outputId": "4b28217c-a791-4a3d-bc0d-9a2353c6f4db"
      },
      "source": [
        "# DataFrame with answers\n",
        "fd = pd.DataFrame(data=an)\n",
        "fd"
      ],
      "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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Survived\n",
              "PassengerId          \n",
              "892                 0\n",
              "893                 1\n",
              "894                 0\n",
              "895                 0\n",
              "896                 1\n",
              "...               ...\n",
              "1305                0\n",
              "1306                1\n",
              "1307                0\n",
              "1308                0\n",
              "1309                0\n",
              "\n",
              "[417 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "2DVorRj8RGii",
        "outputId": "0a2d91ff-b177-499d-896f-065393287f9a"
      },
      "source": [
        "# DataFrame with predictions\n",
        "df = pd.DataFrame(data=answer)\n",
        "df"
      ],
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       0\n",
              "0    0.0\n",
              "1    0.0\n",
              "2    1.0\n",
              "3    1.0\n",
              "4    1.0\n",
              "..   ...\n",
              "412  0.0\n",
              "413  1.0\n",
              "414  0.0\n",
              "415  0.0\n",
              "416  1.0\n",
              "\n",
              "[417 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAntG9dCPsYr",
        "outputId": "1ae28817-f2a5-4e10-9956-683bc96f0bd2"
      },
      "source": [
        "# Counting right predictions\n",
        "tr = 0\n",
        "fl = 0\n",
        "m=0\n",
        "col=[]\n",
        "for i in fd.Survived:\n",
        "  if i == answer[m]:\n",
        "    tr += 1\n",
        "    col.append(1)\n",
        "  else:\n",
        "    fl += 1\n",
        "    col.append(0)\n",
        "  m +=1\n",
        "print(tr, fl)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "349 68\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "l9F-GX4gUtbW",
        "outputId": "6916f840-dd65-40e2-81db-c4afb7936dc2"
      },
      "source": [
        "test_"
      ],
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>sex</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>agge</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>34.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>62.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>108.9000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>38.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29.851837</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Survived  Pclass  SibSp  Parch      Fare  sex  C  Q  S       agge\n",
              "PassengerId                                                                   \n",
              "892               NaN       3      0      0    7.8292    1  0  1  0  34.500000\n",
              "893               NaN       3      1      0    7.0000    0  0  0  1  47.000000\n",
              "894               NaN       2      0      0    9.6875    1  0  1  0  62.000000\n",
              "895               NaN       3      0      0    8.6625    1  0  0  1  27.000000\n",
              "896               NaN       3      1      1   12.2875    0  0  0  1  22.000000\n",
              "...               ...     ...    ...    ...       ...  ... .. .. ..        ...\n",
              "1305              NaN       3      0      0    8.0500    1  0  0  1  29.851837\n",
              "1306              NaN       1      0      0  108.9000    0  1  0  0  39.000000\n",
              "1307              NaN       3      0      0    7.2500    1  0  0  1  38.500000\n",
              "1308              NaN       3      0      0    8.0500    1  0  0  1  29.851837\n",
              "1309              NaN       3      1      1   22.3583    1  1  0  0  29.851837\n",
              "\n",
              "[417 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "r3Lecqw2VLDX",
        "outputId": "393d20e1-ff26-4304-d52c-cd93d5b1942b"
      },
      "source": [
        "# Making column with right|false predictions\n",
        "co = np.array(col)\n",
        "c = pd.DataFrame(data=col, columns=['w'])\n",
        "c"
      ],
      "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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              "\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>w</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     w\n",
              "0    1\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    1\n",
              "..  ..\n",
              "412  1\n",
              "413  1\n",
              "414  1\n",
              "415  1\n",
              "416  0\n",
              "\n",
              "[417 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "xVW7mAkZWRqw",
        "outputId": "85c2ef96-e1f2-4723-f232-7d38ab1803df"
      },
      "source": [
        "# Making DataFrame of this column\n",
        "a=np.arange(892,1309)\n",
        "aa = pd.DataFrame(a, columns=['t'])\n",
        "aa"
      ],
      "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>t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>1304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>1307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        t\n",
              "0     892\n",
              "1     893\n",
              "2     894\n",
              "3     895\n",
              "4     896\n",
              "..    ...\n",
              "412  1304\n",
              "413  1305\n",
              "414  1306\n",
              "415  1307\n",
              "416  1308\n",
              "\n",
              "[417 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvFgMkjEXKHT"
      },
      "source": [
        "s = pd.DataFrame(data=c)\n",
        "ss = pd.concat([s, aa], axis=1)\n",
        "ss.set_index('t', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "gMbOxJGxc1rl",
        "outputId": "c1e4ed4b-fa5e-45e5-beb8-89b21ae58ea1"
      },
      "source": [
        "ss"
      ],
      "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>w</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>t</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>892</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>893</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>894</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1304</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1305</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1306</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1307</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>417 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      w\n",
              "t      \n",
              "892   1\n",
              "893   0\n",
              "894   0\n",
              "895   0\n",
              "896   1\n",
              "...  ..\n",
              "1304  1\n",
              "1305  1\n",
              "1306  1\n",
              "1307  1\n",
              "1308  0\n",
              "\n",
              "[417 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8_UDuPvUdv0"
      },
      "source": [
        "# Concatinating dataframes\n",
        "show = pd.concat([test_, ss], axis=1)\n",
        "show.drop(1309, inplace=True)\n",
        "show.drop(columns='Survived', inplace=True)\n",
        "show = show.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "vEBB5LSMb1od",
        "outputId": "2d52fc9d-ea99-4bbe-e983-e8a3cc0f5755"
      },
      "source": [
        "show.describe()"
      ],
      "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>Pclass</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>sex</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>agge</th>\n",
              "      <th>w</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>416.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.262019</td>\n",
              "      <td>0.447115</td>\n",
              "      <td>0.391827</td>\n",
              "      <td>35.659085</td>\n",
              "      <td>0.634615</td>\n",
              "      <td>0.242788</td>\n",
              "      <td>0.110577</td>\n",
              "      <td>0.646635</td>\n",
              "      <td>30.113957</td>\n",
              "      <td>0.836538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.842315</td>\n",
              "      <td>0.898240</td>\n",
              "      <td>0.983150</td>\n",
              "      <td>55.971095</td>\n",
              "      <td>0.482118</td>\n",
              "      <td>0.429285</td>\n",
              "      <td>0.313985</td>\n",
              "      <td>0.478591</td>\n",
              "      <td>12.578160</td>\n",
              "      <td>0.370231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29.851837</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>35.250000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Pclass       SibSp       Parch  ...           S        agge           w\n",
              "count  416.000000  416.000000  416.000000  ...  416.000000  416.000000  416.000000\n",
              "mean     2.262019    0.447115    0.391827  ...    0.646635   30.113957    0.836538\n",
              "std      0.842315    0.898240    0.983150  ...    0.478591   12.578160    0.370231\n",
              "min      1.000000    0.000000    0.000000  ...    0.000000    0.170000    0.000000\n",
              "25%      1.000000    0.000000    0.000000  ...    0.000000   23.000000    1.000000\n",
              "50%      3.000000    0.000000    0.000000  ...    1.000000   29.851837    1.000000\n",
              "75%      3.000000    1.000000    0.000000  ...    1.000000   35.250000    1.000000\n",
              "max      3.000000    8.000000    9.000000  ...    1.000000   76.000000    1.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "id": "delAn96za1GT",
        "outputId": "7d78b13c-c069-4ecb-98eb-3870dcaebec7"
      },
      "source": [
        "# Visualizing correlation of death with age\n",
        "sns.regplot(x=show['agge'], y=show['w'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7817c9f810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}