Titanic_ML_from_Disaster.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Titanic-ML_from_Disaster.ipynb",
      "provenance": [],
      "mount_file_id": "1gzXoXxwmIXtV4e-hsDRpUDgt4k8WRi5_",
      "authorship_tag": "ABX9TyMONc3o9PbTvebVes4rlDdN",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tatha04/Titanic-Machine-Learning-from-Disaster/blob/main/Titanic_ML_from_Disaster.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t-P4imYN6qGn"
      },
      "source": [
        "# Titanic : Machine Learning from Disaster\n",
        "This is a beginners attempt to work with the Titanic: ML from disaster dataset available from [Kaggle](https://www.kaggle.com/c/titanic/data).\n",
        "The data has been already separated into two groups - the training set and the test set.\n",
        "\n",
        "###Contents:\n",
        "* Import dataset from Kaggle/local storage.\n",
        "* Import necessary libraries.\n",
        "* Explore and visualize the dataset. Identify numerical and categorical features.\n",
        "* Fnd outif any of the features has missing data. If yes, fill up missing data.\n",
        "* Feature Engineering : Drop unneccesary columns. Adjust other columns into usable features.\n",
        "* Model fitting : Choose best fit model.\n",
        "\n",
        "## Import necessary Libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uxkFegQ6z6i"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from numpy import random"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCcEq-l4e5pY"
      },
      "source": [
        "## Import Dataset\n",
        "I have downloaded the dataset into google drive and have mounted the drive into google colab workspace. You need to change the path with the correct path of the data files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOVtsyzyfbGK"
      },
      "source": [
        "df_Train = pd.read_csv(\"/content/drive/My Drive/Data Science/Data_Sets/Titanic/train.csv\")\n",
        "df_Test = pd.read_csv(\"/content/drive/My Drive/Data Science/Data_Sets/Titanic/test.csv\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYwHva44f-RD"
      },
      "source": [
        "## Explore the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkwDTgJLfn7g",
        "outputId": "ba169d97-fe3c-4691-c545-35205a3f980c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df_Train.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uIrnomBfx6k",
        "outputId": "fef71e07-e2c9-41ea-fbbf-51aa19f3b93b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "print(\"Shape\")\n",
        "print(\"Training data : \", df_Train.shape)\n",
        "print(\"Test data     : \", df_Test.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape\n",
            "Training data :  (891, 12)\n",
            "Test data     :  (418, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNUW86BsgDT3"
      },
      "source": [
        "Thus the Training and Test datasets contains records of 891 and 418 passengers, respectively. In the Training dateset, there is column 'Survived' that specifies whether the passenger did or did not survive. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GsI0OkH-lsrc"
      },
      "source": [
        "## Data Analysis and Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dzta6CXElzZq",
        "outputId": "e8bdeda9-88ec-4872-b6c8-566261b55ec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "df_Train.describe(include='all')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>681</td>\n",
              "      <td>NaN</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Klasen, Mr. Klas Albin</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>347082</td>\n",
              "      <td>NaN</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PassengerId    Survived      Pclass  ...        Fare Cabin  Embarked\n",
              "count    891.000000  891.000000  891.000000  ...  891.000000   204       889\n",
              "unique          NaN         NaN         NaN  ...         NaN   147         3\n",
              "top             NaN         NaN         NaN  ...         NaN    G6         S\n",
              "freq            NaN         NaN         NaN  ...         NaN     4       644\n",
              "mean     446.000000    0.383838    2.308642  ...   32.204208   NaN       NaN\n",
              "std      257.353842    0.486592    0.836071  ...   49.693429   NaN       NaN\n",
              "min        1.000000    0.000000    1.000000  ...    0.000000   NaN       NaN\n",
              "25%      223.500000    0.000000    2.000000  ...    7.910400   NaN       NaN\n",
              "50%      446.000000    0.000000    3.000000  ...   14.454200   NaN       NaN\n",
              "75%      668.500000    1.000000    3.000000  ...   31.000000   NaN       NaN\n",
              "max      891.000000    1.000000    3.000000  ...  512.329200   NaN       NaN\n",
              "\n",
              "[11 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEu91XLWl9Ye",
        "outputId": "8fc4427f-dff2-48f7-b2c6-f16489df815f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "df_Test.describe(include='all')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418</td>\n",
              "      <td>418</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418</td>\n",
              "      <td>417.000000</td>\n",
              "      <td>91</td>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>418</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>363</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PC 17608</td>\n",
              "      <td>NaN</td>\n",
              "      <td>B57 B59 B63 B66</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>266</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>2.265550</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.272590</td>\n",
              "      <td>0.447368</td>\n",
              "      <td>0.392344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.627188</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>120.810458</td>\n",
              "      <td>0.841838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.181209</td>\n",
              "      <td>0.896760</td>\n",
              "      <td>0.981429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>55.907576</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>892.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>996.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.895800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.454200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1204.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PassengerId      Pclass  ...            Cabin Embarked\n",
              "count    418.000000  418.000000  ...               91      418\n",
              "unique          NaN         NaN  ...               76        3\n",
              "top             NaN         NaN  ...  B57 B59 B63 B66        S\n",
              "freq            NaN         NaN  ...                3      270\n",
              "mean    1100.500000    2.265550  ...              NaN      NaN\n",
              "std      120.810458    0.841838  ...              NaN      NaN\n",
              "min      892.000000    1.000000  ...              NaN      NaN\n",
              "25%      996.250000    1.000000  ...              NaN      NaN\n",
              "50%     1100.500000    3.000000  ...              NaN      NaN\n",
              "75%     1204.750000    3.000000  ...              NaN      NaN\n",
              "max     1309.000000    3.000000  ...              NaN      NaN\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fw23TFl6mBk7",
        "outputId": "65c01c03-07ec-4e7a-e4b8-c7b441d7eeaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        }
      },
      "source": [
        "print(df_Train.columns)\n",
        "print()\n",
        "print(df_Train.dtypes)\n",
        "print()\n",
        "\n",
        "s = (df_Train.dtypes == 'object')\n",
        "object_cols = list(s[s].index)\n",
        "print(\"Categorical variables:\")\n",
        "print(object_cols)\n",
        "\n",
        "s = (df_Train.dtypes == 'int')\n",
        "num_cols = list(s[s].index)\n",
        "print(\"Integer variables:\")\n",
        "print(num_cols)\n",
        "\n",
        "s = (df_Train.dtypes == 'float')\n",
        "num_cols = list(s[s].index)\n",
        "print(\"Real variables:\")\n",
        "print(num_cols)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
            "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
            "      dtype='object')\n",
            "\n",
            "PassengerId      int64\n",
            "Survived         int64\n",
            "Pclass           int64\n",
            "Name            object\n",
            "Sex             object\n",
            "Age            float64\n",
            "SibSp            int64\n",
            "Parch            int64\n",
            "Ticket          object\n",
            "Fare           float64\n",
            "Cabin           object\n",
            "Embarked        object\n",
            "dtype: object\n",
            "\n",
            "Categorical variables:\n",
            "['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']\n",
            "Integer variables:\n",
            "['PassengerId', 'Survived', 'Pclass', 'SibSp', 'Parch']\n",
            "Real variables:\n",
            "['Age', 'Fare']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9dF2LDynmQiU"
      },
      "source": [
        "The datasets have the following columns : \n",
        "* Categorical:\n",
        "  * Name : Passenger name\n",
        "  * Sex : female or male\n",
        "  * Ticket : Ticket number, an alphnumeric value\n",
        "  * Cabin : Cabin number, an alphanumeric value\n",
        "  * Embarked : Where the passenger embarked on the ship ( S : Southampton, C : Cherbourg or Q : Queenstown)\n",
        "* Integer:\n",
        "  * PassengerId\n",
        "  * Survived : 0 (did not survive) or 1 (survived)\n",
        "  * Pclass: Passenger class ( 1, 2 or 3)\n",
        "  * SibSp : Number of siblings or spouse\n",
        "  * Parch : Number of parents or children\n",
        "* Floating:\n",
        "  * Age : Age of the passenger\n",
        "  * Fare : Ticket fare"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Hw0VUiaqCBi",
        "outputId": "52fda452-5115-49d6-aea1-b1d411c71bda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        }
      },
      "source": [
        "plt.figure(figsize=(10,12))\n",
        "plt.title('Frequencies')\n",
        "\n",
        "plt.subplot(3,2,1)\n",
        "sns.countplot(x = df_Train['Sex'])\n",
        "\n",
        "plt.subplot(3,2,2)\n",
        "sns.countplot(x = df_Train['Pclass'])\n",
        "\n",
        "plt.subplot(3,2,3)\n",
        "sns.countplot(x = df_Train['SibSp'])\n",
        "\n",
        "plt.subplot(3,2,4)\n",
        "sns.countplot(x = df_Train['Parch'])\n",
        "\n",
        "plt.subplot(3,2,5)\n",
        "sns.countplot(x = df_Train['Embarked'])\n",
        "\n",
        "plt.subplot(3,2,6)\n",
        "sns.countplot(x = df_Train['Fare'])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4533976cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x864 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBHNzvZKr9vu",
        "outputId": "5ab67d89-e616-4ada-a71c-926a43a15b7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plt.figure(figsize=(10,12))\n",
        "plt.title('Survival Fractions')\n",
        "\n",
        "plt.subplot(3,2,1)\n",
        "sns.barplot(x = df_Train['Sex'],y = df_Train['Survived'])\n",
        "\n",
        "plt.subplot(3,2,2)\n",
        "sns.barplot(x = df_Train['Pclass'],y = df_Train['Survived'])\n",
        "\n",
        "plt.subplot(3,2,3)\n",
        "sns.barplot(x = df_Train['SibSp'],y = df_Train['Survived'])\n",
        "\n",
        "plt.subplot(3,2,4)\n",
        "sns.barplot(x = df_Train['Parch'],y = df_Train['Survived'])\n",
        "\n",
        "plt.subplot(3,2,5)\n",
        "sns.barplot(x = df_Train['Embarked'],y = df_Train['Survived'])\n",
        "\n",
        "print('Survival Fraction')\n",
        "\n",
        "for i in df_Train['Sex'].unique():\n",
        "    print('Sex = ', i, ': ', df_Train['Survived'][df_Train['Sex'] == i].value_counts(normalize=True)[1])\n",
        "print()\n",
        "\n",
        "for i in df_Train['Pclass'].sort_values().unique():\n",
        "  print('Pclass = ', i, ': ', df_Train['Survived'][df_Train['Pclass'] == i].value_counts(normalize=True)[1])\n",
        "print()\n",
        "\n",
        "for i in df_Train['SibSp'].sort_values().unique():\n",
        "  if (df_Train['Survived'][df_Train['SibSp']==i].sum() > 0):\n",
        "    print('SibSp = ', i, ': ', df_Train['Survived'][df_Train['SibSp'] == i].value_counts(normalize=True)[1])\n",
        "  else:\n",
        "    print('SibSp = ', i, ': ', 0)\n",
        "\n",
        "print()\n",
        "\n",
        "for i in df_Train['Parch'].sort_values().unique():\n",
        "  if (df_Train['Survived'][df_Train['Parch']==i].sum() > 0):\n",
        "    print('Parch = ', i, ': ', df_Train['Survived'][df_Train['Parch'] == i].value_counts(normalize=True)[1])\n",
        "  else:\n",
        "    print('Parch = ', i, ': ', 0)\n",
        "print()\n",
        "\n",
        "for i in df_Train['Embarked'].dropna().unique():\n",
        "  print('Embarked = ', i, ': ', df_Train['Survived'][df_Train['Embarked'] == i].value_counts(normalize=True)[1])\n",
        "print()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Survival Fraction\n",
            "Sex =  male :  0.18890814558058924\n",
            "Sex =  female :  0.7420382165605095\n",
            "\n",
            "Pclass =  1 :  0.6296296296296297\n",
            "Pclass =  2 :  0.47282608695652173\n",
            "Pclass =  3 :  0.24236252545824846\n",
            "\n",
            "SibSp =  0 :  0.34539473684210525\n",
            "SibSp =  1 :  0.5358851674641149\n",
            "SibSp =  2 :  0.4642857142857143\n",
            "SibSp =  3 :  0.25\n",
            "SibSp =  4 :  0.16666666666666666\n",
            "SibSp =  5 :  0\n",
            "SibSp =  8 :  0\n",
            "\n",
            "Parch =  0 :  0.34365781710914456\n",
            "Parch =  1 :  0.5508474576271186\n",
            "Parch =  2 :  0.5\n",
            "Parch =  3 :  0.6\n",
            "Parch =  4 :  0\n",
            "Parch =  5 :  0.2\n",
            "Parch =  6 :  0\n",
            "\n",
            "Embarked =  S :  0.33695652173913043\n",
            "Embarked =  C :  0.5535714285714286\n",
            "Embarked =  Q :  0.38961038961038963\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x864 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuRVQm3GsUx5",
        "outputId": "8d9e7981-975f-45b7-972c-b82955fcbc82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "#sns.distplot(X_Train['Age'],kde=False, label='Total')\n",
        "sns.distplot(df_Train['Age'][df_Train['Survived']==1],kde=False,label='Survived')\n",
        "sns.distplot(df_Train['Age'][df_Train['Survived']==0],kde=False,label='Not Survived')\n",
        "plt.legend()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4533af4908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCE88MOJs0vr",
        "outputId": "ecbf6086-4f01-490c-da5d-7df144272e80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "#sns.distplot(X_Train['Fare'],kde=False, label='Total')\n",
        "sns.distplot(df_Train['Fare'][df_Train['Survived']==1],kde=False,label='Survived')\n",
        "sns.distplot(df_Train['Fare'][df_Train['Survived']==0],kde=False,label='Not Survived')\n",
        "plt.legend()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4532ba20f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEGCAYAAACevtWaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAY3UlEQVR4nO3df3RU5b3v8fe3CYdYAyIQKQY0HAr3igsINAiKWIrrIrJYUFeBIF5kUW7jUkS0tGdhT71y1iqnP8ByxPZa48VCV0FAtIiUZVGstVYFExoQCFzgiBAaBbFQ4FTKj+/9YzZhgAn5MZNM8uTzWmvW7P3sZ+95nmH4zM4ze54xd0dERMLyhXQ3QEREUk/hLiISIIW7iEiAFO4iIgFSuIuIBCgz3Q0A6Nixo+fl5aW7GSIizUppaemn7p6TaFuTCPe8vDxKSkrS3QwRkWbFzD6qbpuGZUREAqRwFxEJkMJdRCRATWLMXUSanlOnTlFRUcHnn3+e7qa0eFlZWXTp0oVWrVrVeh+Fu4gkVFFRQZs2bcjLy8PM0t2cFsvdOXz4MBUVFXTr1q3W+2lYRkQS+vzzz+nQoYOCPc3MjA4dOtT5LyiFu4hUS8HeNNTn30HhLiISII25i0itLN2wL6XHmzjwuhrrzJkzh6VLl5KRkcEXvvAFnnnmGQYOHJjU465evZrt27cza9aspI4DkJ2dzfHjx5M+TkMIL9xLfnl+uWBK+tohIkl59913WbNmDZs2baJ169Z8+umn/OMf/6jVvqdPnyYzM3G8jR49mtGjR6eyqU2ShmVEpEmqrKykY8eOtG7dGoCOHTty7bXXkpeXx6effgpASUkJQ4cOBWD27NlMmjSJwYMHM2nSJAYNGsS2bduqjjd06FBKSkpYtGgRDz74IEePHuX666/n7NmzAJw4cYKuXbty6tQp9uzZw4gRI/jKV77CkCFD2LFjBwAffvghN998M7179+b73/9+Iz4bdadwF5Emafjw4ezfv5+ePXvywAMP8Ic//KHGfbZv387rr7/O888/T2FhIStWrABibxSVlZUUFBRU1b3qqqvIz8+vOu6aNWu44447aNWqFUVFRTz11FOUlpYyb948HnjgAQBmzJjB/fffzwcffEDnzp0boNepo3AXkSYpOzub0tJSiouLycnJobCwkEWLFl12n9GjR3PFFVcAMH78eFauXAnAihUrGDt27CX1CwsLWb58OQDLli2jsLCQ48eP88477zBu3Djy8/O57777qKysBOBPf/oTd999NwCTJk1KVVcbRHhj7iISjIyMDIYOHcrQoUPp3bs3ixcvJjMzs2oo5eJrv6+88sqq5dzcXDp06MCWLVtYvnw5v/jFLy45/ujRo/ne977HZ599RmlpKcOGDePEiRO0a9eOsrKyhG1qLpeH6sxdRJqknTt3smvXrqr1srIyrr/+evLy8igtLQXgxRdfvOwxCgsL+clPfsLRo0fp06fPJduzs7MZMGAAM2bMYNSoUWRkZNC2bVu6devGCy+8AMS+Ibp582YABg8ezLJlywBYsmRJSvrZUHTmLiK1UptLF1Pp+PHjTJ8+nSNHjpCZmcmXv/xliouLKS8vZ+rUqTz22GNVH6ZWZ+zYscyYMYPHHnus2jqFhYWMGzeON998s6psyZIl3H///fzgBz/g1KlTTJgwgb59+/Lkk08yceJEfvzjHzNmzJgU9bRhmLunuw0UFBR4yn6sQ5dCiqREeXk5N9xwQ7qbIZFE/x5mVuruBYnqa1hGRCRACncRkQAp3EVEAqRwFxEJkMJdRCRANYa7mXU1s9+b2XYz22ZmM6Ly2WZ2wMzKotvIuH0eNbPdZrbTzO5oyA6IiMilanOd+2lgprtvMrM2QKmZvRZtm+/u8+Irm1kvYAJwI3At8LqZ9XT3M6lsuIg0svjLjFOhFpcqmxnf/va3eeKJJwCYN28ex48fZ/bs2dXus2rVKnr27EmvXr0u2bZz507uu+8+jhw5wsmTJxkyZAjFxcX17kK8kSNHsnTpUtq1a5fUcWbPnk12djbf+c53kjpOjWfu7l7p7pui5WNAOZB7mV3GAMvc/aS7fwjsBm5KqpUi0iK1bt2al156qWoWyNpYtWoV27dvT7jtoYce4pFHHqGsrIzy8nKmT59ep/acOVP9OeratWuTDvZUqtOYu5nlAf2ADVHRg2a2xcyeM7Oro7JcYH/cbhUkeDMwsyIzKzGzkkOHDtW54SISvszMTIqKipg/f/4l2/bu3cuwYcPo06cPt99+O/v27eOdd95h9erVfPe73yU/P589e/ZcsE9lZSVdunSpWu/duzdA1TTA54waNarqG6vZ2dnMnDmTvn378sMf/pBx48ZV1XvzzTcZNWoUQNVUxLNmzeLnP/95VZ3Zs2czb15sgGPu3LkMGDCAPn368Pjjj1fVmTNnDj179uTWW29l586d9X26LlDrcDezbOBF4GF3/xvwNNAdyAcqgSfq8sDuXuzuBe5ekJOTU5ddRaQFmTZtGkuWLOHo0aMXlE+fPp3JkyezZcsW7rnnHh566CFuueUWRo8ezdy5cykrK6N79+4X7PPII48wbNgw7rzzTubPn8+RI0dqfPwTJ04wcOBANm/ezKxZs9iwYQMnTpwAYPny5UyYMOGC+vFTDUNsRsrCwkLWrVvHrl272LhxI2VlZZSWlvLWW29RWlrKsmXLKCsrY+3atbz//vv1faouUKtwN7NWxIJ9ibu/BODun7j7GXc/CzzL+aGXA0DXuN27RGUiInXWtm1b7r33XhYsWHBB+bvvvsvEiROB2PS7b7/9do3HmjJlCuXl5VVzyQwaNIiTJ09edp+MjAy+8Y1vALG/JEaMGMErr7zC6dOn+e1vf3vJHDP9+vXj4MGD/OUvf2Hz5s1cffXVdO3alXXr1rFu3Tr69etH//792bFjB7t27eKPf/wjd911F1/84hdp27Ztyn4lqjZXyxiwECh395/GlcfPVH8XsDVaXg1MMLPWZtYN6AFsTElrRaRFevjhh1m4cGHVGXMyrr32Wr75zW/y8ssvk5mZydatWy+YRhgunEo4KyuLjIyMqvUJEyawYsUK3njjDQoKCmjTps0ljzFu3DhWrlzJ8uXLKSwsBGKzSz766KOUlZVRVlbG7t27mTp1atL9qU5tztwHA5OAYRdd9vgTM/vAzLYAXwMeiTqwDVgBbAdeBabpShkRSUb79u0ZP348CxcurCq75ZZbLph+d8iQIQC0adOGY8eOJTzOq6++yqlTpwD4+OOPOXz4MLm5ueTl5VFWVsbZs2fZv38/GzdWfz761a9+lU2bNvHss89eMiRzTmFhIcuWLWPlypVVY/R33HEHzz33XNUPah84cICDBw9y2223sWrVKv7+979z7NgxXnnllTo+O4nVeCmku78NJJqdfu1l9pkDzEmiXSLS1KR5ltWZM2fys5/9rGr9qaeeYsqUKcydO5ecnBx++cvYpZoTJkzgW9/6FgsWLGDlypUXjLuvW7eOGTNmkJWVBcQ+4PzSl75Ep06d6NatG7169eKGG26gf//+1bYjIyODUaNGsWjRIhYvXpywzo033sixY8fIzc2t+jm+4cOHU15ezs033wzEPqj99a9/Tf/+/SksLKRv375cc801DBgwILknKqIpf0UkIU3527Royl8REVG4i4iESOEuItVqCsO2Ur9/B4W7iCSUlZXF4cOHFfBp5u4cPny46kPg2tIPZItIQl26dKGiogJND5J+WVlZF0ybUBsKdxFJqFWrVnTr1i3dzZB60rCMiEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgGqMdzNrKuZ/d7MtpvZNjObEZW3N7PXzGxXdH91VG5mtsDMdpvZFjPr39CdEBGRC9XmzP00MNPdewGDgGlm1guYBax39x7A+mgd4E6gR3QrAp5OeatFROSyagx3d690903R8jGgHMgFxgCLo2qLga9Hy2OAX3nMe0A7M+uc8paLiEi16jTmbmZ5QD9gA9DJ3SujTR8DnaLlXGB/3G4VUZmIiDSSWoe7mWUDLwIPu/vf4re5uwNelwc2syIzKzGzkkOHDtVlVxERqUGtwt3MWhEL9iXu/lJU/Mm54Zbo/mBUfgDoGrd7l6jsAu5e7O4F7l6Qk5NT3/aLiEgCtblaxoCFQLm7/zRu02pgcrQ8GXg5rvze6KqZQcDRuOEbERFpBJm1qDMYmAR8YGZlUdn3gB8BK8xsKvARMD7athYYCewG/guYktIWi4hIjWoMd3d/G7BqNt+eoL4D05Jsl4iIJEHfUBURCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCVCN4W5mz5nZQTPbGlc228wOmFlZdBsZt+1RM9ttZjvN7I6GariIiFSvNmfui4ARCcrnu3t+dFsLYGa9gAnAjdE+/8fMMlLVWBERqZ0aw93d3wI+q+XxxgDL3P2ku38I7AZuSqJ9IiJSD8mMuT9oZluiYZuro7JcYH9cnYqoTEREGlF9w/1poDuQD1QCT9T1AGZWZGYlZlZy6NChejZDREQSqVe4u/sn7n7G3c8Cz3J+6OUA0DWuapeoLNExit29wN0LcnJy6tMMERGpRr3C3cw6x63eBZy7kmY1MMHMWptZN6AHsDG5JoqISF1l1lTBzJ4HhgIdzawCeBwYamb5gAN7gfsA3H2bma0AtgOngWnufqZhmi4iItWpMdzd/e4ExQsvU38OMCeZRomISHL0DVURkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAlRjuJvZc2Z20My2xpW1N7PXzGxXdH91VG5mtsDMdpvZFjPr35CNFxGRxGpz5r4IGHFR2Sxgvbv3ANZH6wB3Aj2iWxHwdGqaKSIidVFjuLv7W8BnFxWPARZHy4uBr8eV/8pj3gPamVnnVDVWRERqp75j7p3cvTJa/hjoFC3nAvvj6lVEZZcwsyIzKzGzkkOHDtWzGSIikkjSH6i6uwNej/2K3b3A3QtycnKSbYaIiMSpb7h/cm64Jbo/GJUfALrG1esSlYmISCOqb7ivBiZHy5OBl+PK742umhkEHI0bvhERkUaSWVMFM3seGAp0NLMK4HHgR8AKM5sKfASMj6qvBUYCu4H/AqY0QJtFRKQGNYa7u99dzabbE9R1YFqyjRIRkeToG6oiIgFSuIuIBKjGYZmmbumGfResd993/vtWe87sY+LA6xq7SSIiaaczdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRADX7WSFrcvGskfE0Y6SIhEpn7iIiAVK4i4gEKOhhme77Xqha3nPduDS2RESkcenMXUQkQEGfudfkch+2gj5wFZHmS2fuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEqCkrnM3s73AMeAMcNrdC8ysPbAcyAP2AuPd/a/JNVNEROoiFWfuX3P3fHcviNZnAevdvQewPloXEZFG1BDDMmOAxdHyYuDrDfAYIiJyGcmGuwPrzKzUzIqisk7uXhktfwx0SrSjmRWZWYmZlRw6dCjJZoiISLxk55a51d0PmNk1wGtmtiN+o7u7mXmiHd29GCgGKCgoSFhHRETqJ6kzd3c/EN0fBH4D3AR8YmadAaL7g8k2UkRE6qbe4W5mV5pZm3PLwHBgK7AamBxVmwy8nGwjRUSkbpIZlukE/MbMzh1nqbu/ambvAyvMbCrwETA++WaKiEhd1Dvc3f0/gb4Jyg8DtyfTqMakX2sSkRDpG6oiIgFqkb/EFH+2LiISIp25i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gEqEV+QzUVlm7Yd9ntEwde10gtERG5VIsJ96Y05cDl3hj0piAiqaBhGRGRACncRUQCpHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQlQs7/OvSldvy4i0lTozF1EJEDN/sy9WSj55fnlginpa4eItBgK9ziXDPFktI/dK5BFpJnRsIyISIB05n4ZGz78DIA9Zy4/A2QqabZJEUkFhXstXO6KnD3XjUtYHh/S3fd9dr5+I75RiEjLpXAPSE1n/ZejvwhEwqJwT1L8WX11Z/Gp3DeZABeRlqPBwt3MRgBPAhnA/3X3HzXUY0nD0w+MiDQvDRLuZpYB/Bz4H0AF8L6ZrXb37Q3xeE1Fbc7EU/mN2uqOVde/IKD68D73GN0vc9yGCv5kPlxOpk2J9q3tX1l6o5OLpesiiYY6c78J2O3u/wlgZsuAMUDQ4R4v2RBPZrinoR6jMdpUG1XtSPQ9hOgLY/EfYsOF7U3mjUykuTB3T/1BzcYCI9z9f0Xrk4CB7v5gXJ0ioCha/W/Azno+XEfg0ySa29y0pP62pL5Cy+qv+poa17t7TqINaftA1d2LgeJkj2NmJe5ekIImNQstqb8tqa/Qsvqrvja8hvqG6gGga9x6l6hMREQaQUOF+/tADzPrZmb/BEwAVjfQY4mIyEUaZFjG3U+b2YPA74hdCvmcu29riMciBUM7zUxL6m9L6iu0rP6qrw2sQT5QFRGR9NKskCIiAVK4i4gEqFmHu5mNMLOdZrbbzGaluz2pYGbPmdlBM9saV9bezF4zs13R/dVRuZnZgqj/W8ysf/paXndm1tXMfm9m281sm5nNiMqD66+ZZZnZRjPbHPX136Lybma2IerT8ugCBMysdbS+O9qel87214eZZZjZn81sTbQecl/3mtkHZlZmZiVRWVpfx8023OOmOLgT6AXcbWa90tuqlFgEjLiobBaw3t17AOujdYj1vUd0KwKebqQ2psppYKa79wIGAdOif8MQ+3sSGObufYF8YISZDQJ+DMx39y8DfwWmRvWnAn+NyudH9ZqbGUB53HrIfQX4mrvnx13Tnt7Xsbs3yxtwM/C7uPVHgUfT3a4U9S0P2Bq3vhPoHC13BnZGy88Adyeq1xxvwMvE5iMKur/AF4FNwEBi31zMjMqrXtPErjS7OVrOjOpZuttehz52IRZow4A1gIXa16jde4GOF5Wl9XXcbM/cgVxgf9x6RVQWok7uXhktfwx0ipaDeQ6iP8X7ARsItL/RMEUZcBB4DdgDHHH301GV+P5U9TXafhTo0LgtTsp/AP8CnI3WOxBuXwEcWGdmpdHUKpDm17Hmc29m3N3NLKjrV80sG3gReNjd/2ZmVdtC6q+7nwHyzawd8Bvgv6e5SQ3CzEYBB9291MyGprs9jeRWdz9gZtcAr5nZjviN6XgdN+cz95Y0xcEnZtYZILo/GJU3++fAzFoRC/Yl7v5SVBxsfwHc/Qjwe2JDE+3M7NxJVnx/qvoabb8KONzITa2vwcBoM9sLLCM2NPMkYfYVAHc/EN0fJPbGfRNpfh0353BvSVMcrAYmR8uTiY1Nnyu/N/r0fRBwNO7PwCbPYqfoC4Fyd/9p3Kbg+mtmOdEZO2Z2BbHPFsqJhfzYqNrFfT33HIwF3vBogLapc/dH3b2Lu+cR+3/5hrvfQ4B9BTCzK82szbllYDiwlXS/jtP9QUSSH2KMBP4fsbHLf013e1LUp+eBSuAUsbG4qcTGH9cDu4DXgfZRXSN2xdAe4AOgIN3tr2NfbyU2VrkFKItuI0PsL9AH+HPU163A/47K/xnYCOwGXgBaR+VZ0fruaPs/p7sP9ez3UGBNyH2N+rU5um07l0Xpfh1r+gERkQA152EZERGphsJdRCRACncRkQAp3EVEAqRwFxEJkL6hKi2WmZ0hdinaOV93971pao5ISulSSGmxzOy4u2fXcR8j9v/mbI2VRdJIwzIiETPLNrP1ZrYpmpt7TFSeZ7HfDfgVsS8gdTWz75rZ+9F83P+W3paLXErDMtKSXRHN0gjwITAOuMtjk5d1BN4zs3NTWvQAJrv7e2Y2PFq/idi3DVeb2W3u/lZjd0CkOgp3acn+7u7551aiScz+3cxuIzZVbS7np2n9yN3fi5aHR7c/R+vZxMJe4S5NhsJd5Lx7gBzgK+5+KprVMCvadiKungE/dPdnGrl9IrWmMXeR864iNg/5KTP7GnB9NfV+B3wzmoceM8uN5vEWaTJ05i5y3hLgFTP7ACgBdiSq5O7rzOwG4N3oh0WOA/+T8/N1i6SdLoUUEQmQhmVERAKkcBcRCZDCXUQkQAp3EZEAKdxFRAKkcBcRCZDCXUQkQP8fVVPN8f/4sTAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjgP0Wnas9eH"
      },
      "source": [
        "### Observations\n",
        "* The passengers in the training set were predominantly male ( almost twice as many as the number of females). However the chances of survival were much higher for female passengers. \n",
        "* The number of passengers travelling in 3rd class was much larger compared with the other two. However the probability of survival decreases with the passenger class with PC1 > PC2 > PC3. This might be expected.\n",
        "* Most of the passengers embarked at Southampton. For some reason the chance of survival is maximum for Cherbourg, followed by Queenstown and Southampton.\n",
        "* The number of passengers travelling alone outnumbers the others (i.e. those with children, parents of spouses). It is difficult to see direclty from the probabilities any correlation between SibSp and Parch. All we can say is that people who were travelling alone or with large families were less likely to survive. People who were accompanied by 1-2 spouse/siblings or 2-3 parents/children were more likely to survive.\n",
        "* Children were more likely to survive.\n",
        "* Fares has an extremmely skewed distribution. We need to either (a) plot it on a logithmic scale and see if it has a more uniform  distribution, or (b) divide it into suitable bins.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8GZ3oOgw94Q"
      },
      "source": [
        "## Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QoXDbbF1UuA"
      },
      "source": [
        "df_combined = pd.concat([df_Train,df_Test])\n",
        "X_Train = df_Train.copy()\n",
        "X_Test = df_Test.copy()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MK_M4EpqxCoI",
        "outputId": "bc7476e2-fc7c-48f2-8bd9-83aa81289e13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "print(\"Missing Values : Training Data\")\n",
        "print(df_Train.isnull().sum())\n",
        "print()\n",
        "print(\"Missing Values : Test Data\")\n",
        "print(df_Test.isnull().sum())"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Missing Values : Training Data\n",
            "PassengerId      0\n",
            "Survived         0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age            177\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             0\n",
            "Cabin          687\n",
            "Embarked         0\n",
            "dtype: int64\n",
            "\n",
            "Missing Values : Test Data\n",
            "PassengerId      0\n",
            "Pclass           0\n",
            "Name             0\n",
            "Sex              0\n",
            "Age             86\n",
            "SibSp            0\n",
            "Parch            0\n",
            "Ticket           0\n",
            "Fare             1\n",
            "Cabin          327\n",
            "Embarked         0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uw59JuZ3xnB2"
      },
      "source": [
        "### Cabin:\n",
        "\n",
        "The majority of the entries in the feature 'Cabin' is missing. So we might as well drop it. However it possible that the values were more likely to be available for those that survived the sinking. Conversely, the passenger with a cabin information is more likely to survive."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLVGIpk1y1QX"
      },
      "source": [
        "X_Train[\"CabinBool\"] = (X_Train[\"Cabin\"].notnull().astype('int'))\n",
        "X_Test[\"CabinBool\"] = (X_Test[\"Cabin\"].notnull().astype('int'))"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AI35xBFd16uA",
        "outputId": "cda97f37-7f1f-4d07-b7f2-652e5d80c16b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x = X_Train['CabinBool'],y = X_Train['Survived'])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f452ab16208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmTF91uM2KjZ"
      },
      "source": [
        "### Embarked:\n",
        "There are two missing values in the training set. We will fill up these values with the mode of the mode of the distribution i.e. with 'S'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-0hpHpt3abU"
      },
      "source": [
        "df_Train.fillna({'Embarked' : 'S'},inplace=True)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIP_M04g3rmK"
      },
      "source": [
        "### Age:\n",
        "The training and test datasets have 177 and 86 missing values for Age, respectively. There are a large number of missing values, and they can be dealt with the following approaches:\n",
        "* Fill the missing values with a random distribution with mean and standard deviation of the training and test sets respectively. However, this can give rise to unphysical Age values < 0.\n",
        "* Fill the missing values with random integer values between [Mean - Std. Dev, Mean +  Std. Dev]. I will be using this approach as a first approximation.\n",
        "* A more elegant approach might be to infer the age of the passenger based on the title (i.e. Mr. Mrs. Master etc.) on the passenger's name."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "po-Qn_Ip6s4G",
        "outputId": "958218b5-8bdd-45f2-b47b-dfed53be062b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "Age_mean = df_combined['Age'].mean()\n",
        "Age_sigma  = df_combined['Age'].std()\n",
        "X_Train['Age'][X_Train['Age'].isnull()] = random.randint(Age_mean - Age_sigma, Age_mean + Age_sigma, int(X_Train['Age'].isnull().sum()))\n",
        "X_Test['Age'][X_Test['Age'].isnull()]   = random.randint(Age_mean - Age_sigma, Age_mean + Age_sigma, int(X_Test['Age'].isnull().sum()))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmvrpXNV-QDS",
        "outputId": "e5cf2d3e-eae0-412e-ea39-91e070c8fb0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "#sns.distplot(X_Train['Age'],kde=False, label='Total')\n",
        "sns.distplot(X_Train['Age'][X_Train['Survived']==1],kde=False,label='Survived')\n",
        "sns.distplot(X_Train['Age'][X_Train['Survived']==0],kde=False,label='Not Survived')\n",
        "plt.legend()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f452aad5978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n66FsxhGgdE5"
      },
      "source": [
        "### Fare:\n",
        "The Test set has 1 missing fare value. We can set it to be the mean of the fares of the corresponding Pclass."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvOfSIfVyo_k",
        "outputId": "3a4cc4c8-fe9f-477e-fae7-bf77a24e8b0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        }
      },
      "source": [
        "df_Test[df_Test['Fare'].isnull()]"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>1044</td>\n",
              "      <td>3</td>\n",
              "      <td>Storey, Mr. Thomas</td>\n",
              "      <td>male</td>\n",
              "      <td>60.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3701</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Pclass                Name   Sex  ...  Ticket  Fare  Cabin Embarked\n",
              "152         1044       3  Storey, Mr. Thomas  male  ...    3701   NaN    NaN        S\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ime2yjrJ2lWY"
      },
      "source": [
        "X_Test.loc[152, 'Fare'] = df_combined['Fare'][df_combined['Pclass']==3].mean()"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMpv68JHbBe2"
      },
      "source": [
        "## Data Cleaning\n",
        "In this section, we will go over the columns one by one and modify them as features if necessary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9riiSvlzeF_W",
        "outputId": "a61ce49b-baa4-4a2b-8ccc-c2b8e8a00fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "X_Train.head()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>CabinBool</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  CabinBool\n",
              "0            1         0       3  ...   NaN        S          0\n",
              "1            2         1       1  ...   C85        C          1\n",
              "2            3         1       3  ...   NaN        S          0\n",
              "3            4         1       1  ...  C123        S          1\n",
              "4            5         0       3  ...   NaN        S          0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zVYFxOmeShn"
      },
      "source": [
        "### PassengerId :\n",
        "Drop this column as survival is unlikely to depend on these values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uUKeDgxFHLU"
      },
      "source": [
        "X_Train.drop(columns='PassengerId',inplace=True)\n",
        "X_Test.drop(columns='PassengerId',inplace=True)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1o5xPVfrGShT"
      },
      "source": [
        "### Pclass :\n",
        "This column can be directly used as a feature vector.\n",
        "\n",
        "### Name :\n",
        "As a first approximation, let us drop this column. A more detailed approach might be to extract a feature depending on the title on the passenger name. However this is likely to have a correspondence with the Age of the passengers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_toGIOQHgIS"
      },
      "source": [
        "X_Train.drop(columns='Name',inplace=True)\n",
        "X_Test.drop(columns='Name',inplace=True)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xRnAqlxHnKF"
      },
      "source": [
        "### Sex :\n",
        "We can map this column to numerical values such that male : 0 and female : 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4AmNK9yH0L9"
      },
      "source": [
        "sex_mapping = {'male': 0, 'female': 1}\n",
        "X_Train['Sex'] = df_Train['Sex'].map(sex_mapping)\n",
        "X_Test['Sex'] = df_Test['Sex'].map(sex_mapping)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MPjCouXgIEnN"
      },
      "source": [
        "### Ticket :\n",
        "Drop this column as it is unlikely to yield any useful information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cT2LVIz0IIHZ"
      },
      "source": [
        "X_Train.drop(columns='Ticket',inplace=True)\n",
        "X_Test.drop(columns='Ticket',inplace=True)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gFPTMxxMtMf"
      },
      "source": [
        "### Cabin :\n",
        "Drop this column as it contains a large number of missing values. Any useful information has been saved in the column CabinBool."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CA2NYERM5ho"
      },
      "source": [
        "X_Train.drop(columns='Cabin',inplace=True)\n",
        "X_Test.drop(columns='Cabin',inplace=True)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XhH-SEqwM-SN"
      },
      "source": [
        "### Embarked :\n",
        "Map the 'Embarked' column into numerical feature vector such that S : 0, Q : 1 and C : 2.\n",
        "Alternatively one can perform a onehot coding of the three labels for better results (maybe)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiNvnyrlOc_1"
      },
      "source": [
        "embarked_mapping = {'S' : 0, 'Q' : 1, 'C' : 2}\n",
        "X_Train['Embarked'] = df_Train['Embarked'].map(embarked_mapping)\n",
        "X_Test['Embarked']  = df_Test['Embarked'].map(embarked_mapping)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PL4dJZgyOzzw"
      },
      "source": [
        "### SibSp and Parch :\n",
        "Since both these columns have information regarding the family sizes and co-passengers, we can combine both of them to create a new feature called 'Family_Size' = No. of Parents/Children + No. of Siblings/Spouses + 1 (the passenger himself/herself). Plot and see how survival probability depends on this feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEVpJxvgQLHL",
        "outputId": "767d73e4-ba89-4a7f-c76c-872a03fd0c7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "X_Train['Family_Size'] = X_Train['SibSp'] + X_Train['Parch'] + 1\n",
        "sns.barplot(x = X_Train['Family_Size'],y = X_Train['Survived'])"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f452a9a02b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXpT1DtsSbjp"
      },
      "source": [
        "Family sizes with No. of Members = 2,3, and 4 are more likely to survive (probability ~ 60-70%) wheras persons travellig alone (Family size = 1) or with a large family (Family size >= 5) have a low survival probability (~15-25%). We can create a feature vector 'Small_Family' and set it to 1 for Family Sizes 2,3, and 4. Use this feature vector and drop 'SibSp', 'Parch' and 'Family_Size'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjmKEy6gQrQd"
      },
      "source": [
        "X_Train['Small_Family'] = 0\n",
        "X_Train.loc[(X_Train['Family_Size'] > 1) & (X_Train['Family_Size'] < 5) , 'Small_Family'] = 1\n",
        "X_Train.drop(columns = ['SibSp','Parch','Family_Size'], inplace = True)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VkzZbxocCZXU"
      },
      "source": [
        "Do the same for the Test Dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PwPpA6zChCg"
      },
      "source": [
        "X_Test['Family_Size'] = X_Test['SibSp'] + X_Test['Parch'] + 1\n",
        "X_Test['Small_Family'] = 0\n",
        "X_Test.loc[(X_Test['Family_Size'] > 1) & (X_Test['Family_Size'] < 5) , 'Small_Family'] = 1\n",
        "X_Test.drop(columns = ['SibSp','Parch','Family_Size'], inplace = True)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5F7Cd-Lqy7l"
      },
      "source": [
        "### Fare :\n",
        "We will be dividing the column 'Fare' into 4 quantiles with labels : 1,2,3,4. This will be saved as a new feature 'Fare_band'. Subsequently drop the 'Fare' column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sODwYzk_3MZ",
        "outputId": "bcba2450-8a77-466c-f00e-b4432eee44bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "X_Train['Fare_band'] = pd.qcut(X_Train['Fare'],q=4,labels=[1,2,3,4])\n",
        "X_Test['Fare_band']  = pd.qcut(X_Test['Fare'],q=4,labels=[1,2,3,4])\n",
        "X_Train.drop(columns ='Fare', inplace = True)\n",
        "X_Test.drop(columns = 'Fare', inplace = True)\n",
        "sns.barplot(x = X_Train['Fare_band'] ,y = X_Train['Survived'])"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f452a931f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYVONr_eA0tb"
      },
      "source": [
        "### Age :\n",
        "We can either leave the column 'Age' as it is and use it as a feature vector. In this case it might be better to shift and rescale the values using the mean and standard deviation.\n",
        "An alternate approach would be to band different age groups into a number of bins (5 or 6) and label the passenger age depending on the age group into which it falls."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPs9oN0NBWJn",
        "outputId": "e544e751-6c51-453e-b731-26557d52340e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "X_Train['AgeBand'] = pd.cut(X_Train['Age'], 5)\n",
        "X_Train[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
      ],
      "execution_count": 79,
      "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>AgeBand</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(0.34, 16.336]</td>\n",
              "      <td>0.531532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>0.353712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>0.371901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(48.168, 64.084]</td>\n",
              "      <td>0.434783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(64.084, 80.0]</td>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AgeBand  Survived\n",
              "0    (0.34, 16.336]  0.531532\n",
              "1  (16.336, 32.252]  0.353712\n",
              "2  (32.252, 48.168]  0.371901\n",
              "3  (48.168, 64.084]  0.434783\n",
              "4    (64.084, 80.0]  0.090909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eDguIm7RPM_"
      },
      "source": [
        "Age_bins = [0,16,32,48,64,80]\n",
        "X_Train['AgeBand'] = pd.cut(X_Train['Age'], bins = Age_bins, labels = [0,1,2,3,4])\n",
        "X_Test['AgeBand']  = pd.cut(X_Test['Age'], bins = Age_bins, labels = [0,1,2,3,4])"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PK-GA9yzJrty"
      },
      "source": [
        "X_Train.drop(columns = 'Age', inplace = True)\n",
        "X_Test.drop(columns = 'Age', inplace = True)  "
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqUPi7sGT9x0",
        "outputId": "1db5b286-715a-4542-a3b5-bc3ac26e2ea4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "X_Train.head()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>CabinBool</th>\n",
              "      <th>Small_Family</th>\n",
              "      <th>Fare_band</th>\n",
              "      <th>AgeBand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Embarked  CabinBool  Small_Family Fare_band AgeBand\n",
              "0         0       3    0         0          0             1         1       1\n",
              "1         1       1    1         2          1             1         4       2\n",
              "2         1       3    1         0          0             0         2       1\n",
              "3         1       1    1         0          1             1         4       2\n",
              "4         0       3    0         0          0             0         2       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiBDzaw5ZopZ",
        "outputId": "5d639492-932a-4e46-fb27-00ca905231e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "X_Test.head()"
      ],
      "execution_count": 86,
      "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>Sex</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>CabinBool</th>\n",
              "      <th>Small_Family</th>\n",
              "      <th>Fare_band</th>\n",
              "      <th>AgeBand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex  Embarked  CabinBool  Small_Family Fare_band AgeBand\n",
              "0       3    0         1          0             0         1       2\n",
              "1       3    1         0          0             1         1       2\n",
              "2       2    0         1          0             0         2       3\n",
              "3       3    0         0          0             0         2       1\n",
              "4       3    1         0          0             1         2       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cq94tcTrZqi0"
      },
      "source": [
        "Finally prepare for model fitting. Copy the column 'Survived' into Y_Train and delete the column from the training set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgwsVg6iaBHL"
      },
      "source": [
        "Y_Train = X_Train['Survived']\n",
        "X_Train.drop(columns = 'Survived', inplace = True)"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSbLRvFOaQrZ"
      },
      "source": [
        "## Model Fitting\n",
        "The problem is a classification problem. I would be using the following classifiers and compare the results.\n",
        "* Logistic Regression\n",
        "* Decision Tree Classifier\n",
        "* Random Forest Classifier\n",
        "* Stochastic Gradient Descent\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "deBc8HzsaTEO",
        "outputId": "9e62e4e4-88ba-4247-f1e2-2b5efea6c371",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_Train, Y_Train)\n",
        "Y_Pred_logreg = logreg.predict(X_Test)\n",
        "acc_logreg = round(logreg.score(X_Train, Y_Train) * 100, 2)\n",
        "print(acc_logreg)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80.36\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKNr_AWPdINm",
        "outputId": "fceb2991-39c2-4632-be82-c2534de2003d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Decision Tree Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dectree = DecisionTreeClassifier()\n",
        "dectree.fit(X_Train, Y_Train)\n",
        "Y_Pred_dectree = dectree.predict(X_Test)\n",
        "acc_dectree = round(dectree.score(X_Train, Y_Train) * 100, 2)\n",
        "print(acc_dectree)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "87.09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1ynmw-1dL6m",
        "outputId": "e7a12aca-57f9-4097-d34c-a7a7cc5a29fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Random Forest Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier \n",
        "\n",
        "ranfor = RandomForestClassifier()\n",
        "ranfor.fit(X_Train, Y_Train)\n",
        "Y_Pred_ranfor = ranfor.predict(X_Test)\n",
        "acc_ranfor = round(ranfor.score(X_Train, Y_Train) * 100, 2)\n",
        "print(acc_ranfor)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "87.09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKc94EdSfxQm",
        "outputId": "ecf4d53e-ecb9-4c4a-9e72-cd68336685b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Stochastic Gradient Descent\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "sgd = SGDClassifier()\n",
        "sgd.fit(X_Train,Y_Train)\n",
        "Y_Pred_sgd = sgd.predict(X_Test)\n",
        "acc_sgd = round(sgd.score(X_Train,Y_Train) * 100, 2)\n",
        "print(acc_sgd)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80.02\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DY1dIL5ZgxOc"
      },
      "source": [
        "Both Random Forest Classifier and Decision Tree Classifier have better accuracies compared to the other methods."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZ061OGyh8lJ"
      },
      "source": [
        "## Preparing submission File\n",
        "I would be using the results obtained from the RandomForestClassifier to submit to Kaggle."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3UetF1FiLE9"
      },
      "source": [
        "submission = pd.DataFrame({\"PassengerId\": df_Test[\"PassengerId\"], \"Survived\": Y_Pred_ranfor })\n",
        "submission.to_csv('Titanic.csv', index=False)"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4gqlaZpjCQ3"
      },
      "source": [
        "I would like to acknowledge the help from the following Kaggle notebooks.\n",
        "* https://www.kaggle.com/nadintamer/titanic-survival-predictions-beginner\n",
        "* https://www.kaggle.com/startupsci/titanic-data-science-solutions"
      ]
    }
  ]
}