HW 2 - Titanic data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 2\n",
    "\n",
    "**Author:** Javed Ali"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1: Analyze the Titanic data and find whether the individuals from the test dataset had survived or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all required libraries\n",
    "\n",
    "# data analysis and wrangling\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "\n",
    "# data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import graphviz\n",
    "\n",
    "# machine learning\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "plt.rcParams['figure.figsize']= (16,10)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the training and testing datasets\n",
    "\n",
    "train_df = pd.read_csv(\"Data/train.csv\", header=0)\n",
    "test_df = pd.read_csv('Data/test.csv', header=0)\n",
    "\n",
    "combine_df = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>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  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the training dataset\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the test dataset\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preprocess the Titanic training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning algorithms to work, it is necessary to convert the raw data into a clean data set and dataset must be converted to numeric data. We have to encode all the categorical lables to column vectors with binary values.  Missing values in the dataset is an annoying problem. We have to either drop the missing rows or fill them up with a mean or interpolated  values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_percentage(df):\n",
    "    total = df.isnull().sum().sort_values(ascending = False)\n",
    "    percent = round(df.isnull().sum().sort_values(ascending = False)/len(df)*100, 2)\n",
    "    return pd.concat([total, percent], axis=1, keys=['Total','Percent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>77.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>19.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Percent\n",
       "Cabin          687    77.10\n",
       "Age            177    19.87\n",
       "Embarked         2     0.22\n",
       "Fare             0     0.00\n",
       "Ticket           0     0.00\n",
       "Parch            0     0.00\n",
       "SibSp            0     0.00\n",
       "Sex              0     0.00\n",
       "Name             0     0.00\n",
       "Pclass           0     0.00\n",
       "Survived         0     0.00\n",
       "PassengerId      0     0.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the missing values in training dataset\n",
    "missing_percentage(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>327</td>\n",
       "      <td>78.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>86</td>\n",
       "      <td>20.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total  Percent\n",
       "Cabin          327    78.23\n",
       "Age             86    20.57\n",
       "Fare             1     0.24\n",
       "Embarked         0     0.00\n",
       "Ticket           0     0.00\n",
       "Parch            0     0.00\n",
       "SibSp            0     0.00\n",
       "Sex              0     0.00\n",
       "Name             0     0.00\n",
       "Pclass           0     0.00\n",
       "PassengerId      0     0.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check th emissing values in test dataset\n",
    "missing_percentage(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above results that the `Cabin` and `Age` columns in training and testing data have lots of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before dropping the columns:  (891, 12) (418, 11) (891, 12) (418, 11)\n",
      "After dropping the unuseful columns:  (891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "# dropping the columns which are not useful\n",
    "\n",
    "print(\"Before dropping the columns: \", train_df.shape, test_df.shape, combine_df[0].shape, combine_df[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine_df = [train_df, test_df]\n",
    "\n",
    "print(\"After dropping the unuseful columns: \", train_df.shape, test_df.shape, combine_df[0].shape, combine_df[1].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating new feature from existing\n",
    "\n",
    "We want to analyze if `Name` feature can be engineered to extract titles and test correlation between titles and survival, before dropping Name and `PassengerId` features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for data in combine_df:\n",
    "    data['Title'] = data.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can replace many titles with a more common name or classify them as `Other`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4   Other  0.347826"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for data in combine_df:\n",
    "    data['Title'] = data['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Other')\n",
    "\n",
    "    data['Title'] = data['Title'].replace('Mlle', 'Miss')\n",
    "    data['Title'] = data['Title'].replace('Ms', 'Miss')\n",
    "    data['Title'] = data['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the categorical titles to ordinal\n",
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Other\": 5}\n",
    "\n",
    "for data in combine_df:\n",
    "    data['Title'] = data['Title'].map(title_mapping)\n",
    "    data['Title'] = data['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new feature for `FamilySize` that combines `Parch` and `SibSp` features. Afterwards, we can drop `Parch` and `SibSp` from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for data in combine_df:\n",
    "    data['FamilySize'] = data['SibSp'] + data['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can drop the `Name` feature from training and testing datasets. We also do not need the `PassengerId` feature in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 10), (418, 10))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "\n",
    "combine_df = [train_df, test_df]\n",
    "\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can drop `Parch`, and `SibSp` features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare Embarked  Title  FamilySize\n",
       "0         0       3    male  22.0   7.2500        S      1           2\n",
       "1         1       1  female  38.0  71.2833        C      3           2\n",
       "2         1       3  female  26.0   7.9250        S      2           1\n",
       "3         1       1  female  35.0  53.1000        S      3           2\n",
       "4         0       3    male  35.0   8.0500        S      1           1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp'], axis=1)\n",
    "combine_df = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting categorical features to numerical and filling the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</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>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare Embarked  Title  FamilySize\n",
       "0         0       3    0  22.0   7.2500        S      1           2\n",
       "1         1       1    1  38.0  71.2833        C      3           2\n",
       "2         1       3    1  26.0   7.9250        S      2           1\n",
       "3         1       1    1  35.0  53.1000        S      3           2\n",
       "4         0       3    0  35.0   8.0500        S      1           1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the sex data from categorical to numerical\n",
    "for data in combine_df:\n",
    "    data['Sex'] = data['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling missing values in the Age and Fare columns using the median values of the data\n",
    "train_df['Fare'] = train_df['Fare'].fillna(train_df['Fare'].dropna().median())\n",
    "train_df['Age'] = train_df['Age'].fillna(train_df['Age'].dropna().median())\n",
    "\n",
    "# filling the missing values in the Embarked columnn with the most common embarked port 'S'\n",
    "port_freq = train_df['Embarked'].dropna().mode()[0]\n",
    "train_df['Embarked']= train_df['Embarked'].fillna(port_freq)\n",
    "\n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>26.0</td>\n",
       "      <td>7.9250</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>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare  Embarked  Title  FamilySize\n",
       "0         0       3    0  22.0   7.2500         0      1           2\n",
       "1         1       1    1  38.0  71.2833         1      3           2\n",
       "2         1       3    1  26.0   7.9250         0      2           1\n",
       "3         1       1    1  35.0  53.1000         0      3           2\n",
       "4         0       3    0  35.0   8.0500         0      1           1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the embarked data from categorical to numerical\n",
    "for data in combine_df:\n",
    "    data['Embarked'] = data['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27657a571f0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation heatmap for training dataset\n",
    "sns.heatmap(train_df.corr(), cmap='BrBG', annot=True, linewidths=.5, fmt= '.2f') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</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>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>26.0</td>\n",
       "      <td>7.9250</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>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare  Embarked  Title  FamilySize\n",
       "0         0       3    0  22.0   7.2500         0      1           2\n",
       "1         1       1    1  38.0  71.2833         1      3           2\n",
       "2         1       3    1  26.0   7.9250         0      2           1\n",
       "3         1       1    1  35.0  53.1000         0      3           2\n",
       "4         0       3    0  35.0   8.0500         0      1           1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the training data again\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FamilySize</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total  Percent\n",
       "FamilySize      0      0.0\n",
       "Title           0      0.0\n",
       "Embarked        0      0.0\n",
       "Fare            0      0.0\n",
       "Age             0      0.0\n",
       "Sex             0      0.0\n",
       "Pclass          0      0.0\n",
       "Survived        0      0.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the missing values\n",
    "missing_percentage(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Select a set of important features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VarianceThreshold Feature Selection using `sklearn.feature_selection`\n",
    "\n",
    "`VarianceThreshold` is a simple feature selection approach that filters features with low variance. The variance of a random variable X provides a measure of how much X's value deviates from X's mean value. A slight variation shows that the data points appear to be very close to each other and to the mean value. A high variance means that the data points are quite spread out from the mean and from each other. The `VarianceThreshold` approach removes all features whose variance doesn’t meet a threshold. For the given titanic data, we can set a threshold of 0.5, so features having variance value lower than 0.5 will be removed. \n",
    "\n",
    "The closely similar or the same data do not provide meaningful information. So, we will remove less informative features by setting the threshold at 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selcting important features using VarianceThreshold feature selection method\n",
    "\n",
    "modelSelect = VarianceThreshold(threshold=0.5)\n",
    "modelSelect.fit(train_df)\n",
    "\n",
    "ix = modelSelect.get_support() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age     Fare  Title  FamilySize\n",
       "0     3.0  22.0   7.2500    1.0         2.0\n",
       "1     1.0  38.0  71.2833    3.0         2.0\n",
       "2     3.0  26.0   7.9250    2.0         1.0\n",
       "3     1.0  35.0  53.1000    3.0         2.0\n",
       "4     3.0  35.0   8.0500    1.0         1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selected features from VarianceThreshold method\n",
    "\n",
    "data_imp = pd.DataFrame(modelSelect.transform(train_df), columns = train_df.columns.values[ix])\n",
    "data_imp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the VarianceThreshold method, the selected features are `Pclass`, `Age`, `Fare`, `Title` and `FamilySize`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SelectKBest Univariate Feature Selection\n",
    "\n",
    "`SelectKBest` univariate feature selection is another feature selection approach, whihc is used to select the best features based on univariate statistical tests. `SelectKBest` removes all features except the highest scoring features. `SelectKBest` uses the chi-square univariate statistical test to test the correlation of qualitative independent variables to qualitative dependent variables. The chi-square test assesses whether the relationship between X and Y that we observed in the data is due to a real relationship between X and Y in the population, or if it is something that could have happened just by chance due to sampling variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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      "text/plain": [
       "   Pclass  Sex   Age     Fare  Title\n",
       "0     3.0  0.0  22.0   7.2500    1.0\n",
       "1     1.0  1.0  38.0  71.2833    3.0\n",
       "2     3.0  1.0  26.0   7.9250    2.0\n",
       "3     1.0  1.0  35.0  53.1000    3.0\n",
       "4     3.0  0.0  35.0   8.0500    1.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train_df.drop(\"Survived\", axis=1)\n",
    "y = train_df[\"Survived\"]\n",
    "\n",
    "modelSelection = SelectKBest(chi2, k=5) \n",
    "modelSelection.fit(X,y)\n",
    "ix = modelSelection.get_support() \n",
    "\n",
    "# selected features from SelectKBest univariate feature selection method\n",
    "data_imp2 = pd.DataFrame(modelSelection.transform(X), columns = X.columns.values[ix]) \n",
    "data_imp2.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `SelectKBest` univariate feature selection method, the selected features are `Pclass`, `Sex`, `Age`, `Fare`, and `Title`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `SelectFromModel` for Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pclass  Sex\n",
       "0     3.0  0.0\n",
       "1     1.0  1.0\n",
       "2     3.0  1.0\n",
       "3     1.0  1.0\n",
       "4     3.0  0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train_df.drop(\"Survived\",axis=1)\n",
    "y = train_df[\"Survived\"]\n",
    "\n",
    "# Linear Model\n",
    "lin_model = LogisticRegression()\n",
    "lin_model.fit(X,y)\n",
    "\n",
    "model = SelectFromModel(lin_model, prefit=True)\n",
    "ix = model.get_support() \n",
    "\n",
    "# # selected features\n",
    "data_imp3 = pd.DataFrame(model.transform(X), columns = X.columns.values[ix]) \n",
    "data_imp3.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `SelectFromModel` method, the selected features are `Pclass` and `Sex`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recursive Feature Selection\n",
    "\n",
    "Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pclass  Sex  Embarked  Title  FamilySize\n",
       "0     3.0  0.0       0.0    1.0         2.0\n",
       "1     1.0  1.0       1.0    3.0         2.0\n",
       "2     3.0  1.0       0.0    2.0         1.0\n",
       "3     1.0  1.0       0.0    3.0         2.0\n",
       "4     3.0  0.0       0.0    1.0         1.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelSelect2 = RFE(lin_model,n_features_to_select=5)\n",
    "modelSelect2.fit(X,y)\n",
    "ix = modelSelect2.get_support() \n",
    "\n",
    "# selected features\n",
    "data_imp4 = pd.DataFrame(modelSelect2.transform(X), columns = X.columns.values[ix]) \n",
    "data_imp4.head(n=5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `RecursiveFeatureElimination (RFE)` method, the selected features are `Pclass`, `Sex`, `Embarked`, `Title` and `FamilySize`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `RecursiveFeatureElimination (RFE)` method selected `Pclass`, `Sex`, `Embarked`, `Title` and `FamilySize`. `SelectKBest` univariate feature selection method selected `Pclass`, `Sex`, `Age`, `Fare`, and `Title` and the `VarianceThreshold` feature selection method selected `Pclass`, `Age`, `Fare`, `Title` and `FamilySize`.\n",
    "\n",
    "So, we will select the following features: \n",
    "1. `Pclass` \n",
    "2. `Sex`\n",
    "3. `Fare`\n",
    "4. `Age`\n",
    "5. `Title`\n",
    "6. `Embarked`\n",
    "7. `FamilySize`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Learn a Decision Tree model with the Titanic training data using Gini Index and plot the Decision Tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree score:  0.8246268656716418\n",
      "MSE    : 0.17537313432835822\n",
      "R2     : 0.27721351925173576\n"
     ]
    }
   ],
   "source": [
    "# decision tree model with Titanic training dataset using Gini index\n",
    "\n",
    "target = train_df['Survived'].values\n",
    "selected_features = ['Pclass','Sex','Fare', 'Title','Embarked','Age', 'FamilySize']\n",
    "features = train_df[selected_features].values\n",
    "\n",
    "# build training and testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state = 42)\n",
    "\n",
    "decision_tree = tree.DecisionTreeClassifier(random_state = 1, criterion = 'entropy', min_samples_split = 100)\n",
    "\n",
    "decision_tree_1 = decision_tree.fit(X_train, y_train)\n",
    "target_predict = decision_tree_1.predict(X_test)\n",
    "\n",
    "\n",
    "print(\"Decision tree score: \", accuracy_score(y_test, target_predict))\n",
    "\n",
    "print (\"MSE    :\",mean_squared_error(y_test, target_predict))\n",
    "\n",
    "print (\"R2     :\",r2_score(y_test, target_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalized decision tree score:  0.8496071829405163\n"
     ]
    },
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       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Age &lt;= 32.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.18</text>\r\n",
       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 390</text>\r\n",
       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-304.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [351, 39]</text>\r\n",
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       "<!-- 1&#45;&gt;2 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"968.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 26.775</text>\r\n",
       "<text text-anchor=\"middle\" x=\"968.5\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.443</text>\r\n",
       "<text text-anchor=\"middle\" x=\"968.5\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 127</text>\r\n",
       "<text text-anchor=\"middle\" x=\"968.5\" y=\"-304.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [85, 42]</text>\r\n",
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       "<!-- 1&#45;&gt;15 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Age &lt;= 30.75</text>\r\n",
       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.201</text>\r\n",
       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 291</text>\r\n",
       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [258, 33]</text>\r\n",
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       "<!-- 2&#45;&gt;3 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 7.91</text>\r\n",
       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.114</text>\r\n",
       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 99</text>\r\n",
       "<text text-anchor=\"middle\" x=\"552.5\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [93, 6]</text>\r\n",
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       "<!-- 2&#45;&gt;10 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Embarked &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.178</text>\r\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 274</text>\r\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [247, 27]</text>\r\n",
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       "<!-- 3&#45;&gt;4 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 7.815</text>\r\n",
       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.457</text>\r\n",
       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17</text>\r\n",
       "<text text-anchor=\"middle\" x=\"329.5\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [11, 6]</text>\r\n",
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       "<!-- 3&#45;&gt;7 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"55.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.15</text>\r\n",
       "<text text-anchor=\"middle\" x=\"55.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 208</text>\r\n",
       "<text text-anchor=\"middle\" x=\"55.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [191, 17]</text>\r\n",
       "</g>\r\n",
       "<!-- 4&#45;&gt;5 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.257</text>\r\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 66</text>\r\n",
       "<text text-anchor=\"middle\" x=\"181.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [56, 10]</text>\r\n",
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       "<!-- 4&#45;&gt;6 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"297.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"297.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"297.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;8 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"406.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.49</text>\r\n",
       "<text text-anchor=\"middle\" x=\"406.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 14</text>\r\n",
       "<text text-anchor=\"middle\" x=\"406.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [8, 6]</text>\r\n",
       "</g>\r\n",
       "<!-- 7&#45;&gt;9 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"490.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"490.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 35</text>\r\n",
       "<text text-anchor=\"middle\" x=\"490.5\" y=\"-104.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [35, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 10&#45;&gt;11 -->\r\n",
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       "<path fill=\"none\" stroke=\"black\" d=\"M532.37,-192.884C525.619,-181.776 518.079,-169.372 511.278,-158.184\"/>\r\n",
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       "<!-- 12 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 7.988</text>\r\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.17</text>\r\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 64</text>\r\n",
       "<text text-anchor=\"middle\" x=\"606.5\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [58, 6]</text>\r\n",
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       "<!-- 10&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>10&#45;&gt;12</title>\r\n",
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       "<!-- 13 -->\r\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"515.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"515.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4</text>\r\n",
       "<text text-anchor=\"middle\" x=\"515.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [2, 2]</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>12&#45;&gt;13</title>\r\n",
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       "<!-- 14 -->\r\n",
       "<g id=\"node15\" class=\"node\"><title>14</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"677.5,-53 579.5,-53 579.5,-0 677.5,-0 677.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"628.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.124</text>\r\n",
       "<text text-anchor=\"middle\" x=\"628.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 60</text>\r\n",
       "<text text-anchor=\"middle\" x=\"628.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [56, 4]</text>\r\n",
       "</g>\r\n",
       "<!-- 12&#45;&gt;14 -->\r\n",
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       "<g id=\"node17\" class=\"node\"><title>16</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"955.5,-261 857.5,-261 857.5,-193 955.5,-193 955.5,-261\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"906.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Age &lt;= 53.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"906.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.43</text>\r\n",
       "<text text-anchor=\"middle\" x=\"906.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n",
       "<text text-anchor=\"middle\" x=\"906.5\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [5, 11]</text>\r\n",
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       "<!-- 15&#45;&gt;16 -->\r\n",
       "<g id=\"edge16\" class=\"edge\"><title>15&#45;&gt;16</title>\r\n",
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       "<!-- 21 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 387.665</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.403</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 111</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [80, 31]</text>\r\n",
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       "<!-- 15&#45;&gt;21 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"793.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Age &lt;= 31.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"793.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.153</text>\r\n",
       "<text text-anchor=\"middle\" x=\"793.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 12</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"906.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"906.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 4</text>\r\n",
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       "<!-- 16&#45;&gt;20 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"741.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.444</text>\r\n",
       "<text text-anchor=\"middle\" x=\"741.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"741.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [1, 2]</text>\r\n",
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       "<!-- 17&#45;&gt;18 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"850.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"850.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 9</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">FamilySize &lt;= 4.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.391</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1029.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 109</text>\r\n",
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       "<!-- 21&#45;&gt;22 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1152.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1152.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2</text>\r\n",
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       "<!-- 21&#45;&gt;25 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"966.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.417</text>\r\n",
       "<text text-anchor=\"middle\" x=\"966.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 98</text>\r\n",
       "<text text-anchor=\"middle\" x=\"966.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [69, 29]</text>\r\n",
       "</g>\r\n",
       "<!-- 22&#45;&gt;23 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1085.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1085.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 11</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1085.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [11, 0]</text>\r\n",
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       "<!-- 22&#45;&gt;24 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1487.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 18.375</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1487.5\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.346</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1487.5\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 323</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1487.5\" y=\"-304.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [72, 251]</text>\r\n",
       "</g>\r\n",
       "<!-- 26&#45;&gt;27 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1673.5\" y=\"-349.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Pclass &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1673.5\" y=\"-334.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.315</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1673.5\" y=\"-319.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 51</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1673.5\" y=\"-304.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [41, 10]</text>\r\n",
       "</g>\r\n",
       "<!-- 26&#45;&gt;40 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"1428.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Title &lt;= 4.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1428.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.461</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1428.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 150</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1428.5\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [54, 96]</text>\r\n",
       "</g>\r\n",
       "<!-- 27&#45;&gt;28 -->\r\n",
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       "<g id=\"node34\" class=\"node\"><title>33</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Title &lt;= 4.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.186</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 173</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-200.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [18, 155]</text>\r\n",
       "</g>\r\n",
       "<!-- 27&#45;&gt;33 -->\r\n",
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       "<polygon fill=\"none\" stroke=\"black\" points=\"1368.5,-157 1264.5,-157 1264.5,-89 1368.5,-89 1368.5,-157\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Pclass &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.441</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 143</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1316.5\" y=\"-96.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [47, 96]</text>\r\n",
       "</g>\r\n",
       "<!-- 28&#45;&gt;29 -->\r\n",
       "<g id=\"edge29\" class=\"edge\"><title>28&#45;&gt;29</title>\r\n",
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       "<polygon fill=\"none\" stroke=\"black\" points=\"1478,-149.5 1387,-149.5 1387,-96.5 1478,-96.5 1478,-149.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1432.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1432.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 7</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1432.5\" y=\"-104.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [7, 0]</text>\r\n",
       "</g>\r\n",
       "<!-- 28&#45;&gt;32 -->\r\n",
       "<g id=\"edge32\" class=\"edge\"><title>28&#45;&gt;32</title>\r\n",
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       "<g id=\"node31\" class=\"node\"><title>30</title>\r\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"1250.5,-53 1152.5,-53 1152.5,-0 1250.5,-0 1250.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1201.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.165</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1201.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 33</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1201.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [3, 30]</text>\r\n",
       "</g>\r\n",
       "<!-- 29&#45;&gt;30 -->\r\n",
       "<g id=\"edge30\" class=\"edge\"><title>29&#45;&gt;30</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1276.28,-88.9485C1264.7,-79.4346 1252.09,-69.074 1240.58,-59.6175\"/>\r\n",
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       "<polygon fill=\"none\" stroke=\"black\" points=\"1372.5,-53 1268.5,-53 1268.5,-0 1372.5,-0 1372.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1320.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1320.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 110</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1320.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [44, 66]</text>\r\n",
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       "<!-- 29&#45;&gt;31 -->\r\n",
       "<g id=\"edge31\" class=\"edge\"><title>29&#45;&gt;31</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1317.9,-88.9485C1318.25,-80.7153 1318.62,-71.848 1318.98,-63.4814\"/>\r\n",
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       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Pclass &lt;= 2.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.119</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1554.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 157</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1674.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Sex &lt;= 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1674.5\" y=\"-126.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1674.5\" y=\"-111.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 16</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1442.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.082</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1442.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 140</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1561.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.36</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1561.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 17</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1674.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.473</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1674.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 13</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1783.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1783.5\" y=\"-22.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1783.5\" y=\"-7.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">value = [0, 3]</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1673.5\" y=\"-238.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1673.5\" y=\"-223.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 6</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1786.5\" y=\"-245.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">Fare &lt;= 14.5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1786.5\" y=\"-230.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.162</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1786.5\" y=\"-215.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 45</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1785.5\" y=\"-134.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1785.5\" y=\"-119.3\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1908.5\" y=\"-141.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">FamilySize &lt;= 6.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"2018.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">gini = 0.266</text>\r\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x276581d8d30>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the decision tree\n",
    "\n",
    "dec_tree = tree.DecisionTreeClassifier(\n",
    "        random_state = 1,\n",
    "        max_depth = 5,\n",
    "        min_samples_split=2\n",
    ")\n",
    "\n",
    "dec_tree_1 = dec_tree.fit(features, target)\n",
    "\n",
    "print(\"Generalized decision tree score: \", dec_tree_1.score(features,target))\n",
    "\n",
    "dot_data = tree.export_graphviz(dec_tree_1, feature_names = selected_features, out_file = None)\n",
    "\n",
    "plot_dc = graphviz.Source(dot_data)\n",
    "\n",
    "plot_dc.format = \"png\"\n",
    "plot_dc.render(\"decision-tree\")\n",
    "\n",
    "plot_dc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest model with the Titanic training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest score:  0.8283582089552238\n",
      "MSE    : 0.17164179104477612\n",
      "R2     : 0.29259195501233715\n"
     ]
    }
   ],
   "source": [
    "target = train_df['Survived'].values\n",
    "selected_features_names = ['Pclass','Sex','Fare', 'Title','Embarked','Age', 'FamilySize']\n",
    "features = train_df[selected_features_names].values\n",
    "\n",
    "# create training and testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.3, random_state = 42)\n",
    "\n",
    "random_forest_model = RandomForestClassifier(max_depth=5, min_samples_split=10, n_estimators=500, random_state=5,criterion = 'entropy')\n",
    "\n",
    "random_forest_model_1 = random_forest_model.fit(X_train, y_train)\n",
    "target_predict = random_forest_model_1.predict(X_test)\n",
    "\n",
    "print(\"Random forest score: \", accuracy_score(y_test, target_predict))\n",
    "\n",
    "print (\"MSE    :\", mean_squared_error(y_test, target_predict))\n",
    "\n",
    "print (\"R2     :\", r2_score(y_test, target_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 & 5. Apply the five-fold cross validation of the `Decision Tree` and the `Random Forest` learning algorithms to the Titanic training data to extract their average classification accuracies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   CrossValMeans  CrossValErrors     Algorithms\n",
      "0       0.810568        0.044449  Decision Tree\n",
      "1       0.836271        0.038094  Random Forest\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing different models \n",
    "random_state = 2\n",
    "\n",
    "K_fold = StratifiedKFold(n_splits = 5)\n",
    "\n",
    "models = [] \n",
    "\n",
    "# cross validation result\n",
    "cv_results = [] \n",
    "\n",
    "# cross validation mean value\n",
    "cv_means = [] \n",
    "\n",
    "# cross validation standard deviation\n",
    "cv_std = [] \n",
    "\n",
    "\n",
    "models.append(decision_tree)\n",
    "models.append(random_forest_model)\n",
    "\n",
    "\n",
    "for model in models :\n",
    "    cv_results.append(cross_val_score(model, X_train, y_train, \n",
    "                                      scoring = \"accuracy\", \n",
    "                                      cv = K_fold, \n",
    "                                      n_jobs=4))\n",
    "\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "cv_frame = pd.DataFrame({\"CrossValMeans\":cv_means, \"CrossValErrors\": cv_std, \"Algorithms\":[\"Decision Tree\", \"Random Forest\"]})\n",
    "\n",
    "print(cv_frame)\n",
    "\n",
    "fig = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "cv_plot = sns.barplot(\"CrossValMeans\",\"Algorithms\", data = cv_frame, palette=\"Set3\", **{'xerr':cv_std})\n",
    "\n",
    "plt.subplots_adjust(left=0.15)\n",
    "cv_plot.set_xlabel(\"Mean Accuracy\", fontsize=16)\n",
    "cv_plot.set_ylabel(\"Models\", fontsize=16)\n",
    "cv_plot.set_title(\"Cross Validation Scores\", fontsize=18, y=1.03)\n",
    "\n",
    "plt.tick_params(axis='x', labelsize=12)\n",
    "plt.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "plt.savefig(\"cross-validation-scores.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  {
   "cell_type": "markdown",
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   "source": [
    "## 6. Which Algorithm is better, Decision Tree or Random Forest?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Looking at the above graph, we can conclude that the `Random Forest` algorithm is more accurate than the `Decision Tree` algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. What are your observations and conclusions from the algorithm comparison and analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree\n",
    "The `Decision Tree` algorithm is fast, can handle large datasets and can fit linearly inseparable datasets. A decision tree is simply a series of sequential decisions made to reach a specific result. Decision trees are much easier to interpret and understand. Since a random forest combines multiple decision trees, it becomes more difficult to interpret. The disadvantages to the `Decision Tree` is that it is sensitive to small data variations, resulting in different tree structures. Decision trees are prone to overfitting, particularly when the tree is deep. Decision trees can lead to errors due to bias and variance. Decision tree gives low prediction accuracy for a dataset as compared to other machine learning algorithms.\n",
    "\n",
    "### Random Forest\n",
    "The `Random Forest` is typically an ensemble of `Decesion Tree` classifer, where the results are aggregated into a single final result. It performs better than `Decision Tree`. The Random Forest algorithm can limit overfitting and can minimize errors due to bias and variance. As the Random Forest algorithm combines the output of multiple (randomly created) Decision Trees to generate the final output, it is computationally more expensive, especially with very large datasets. Random Forest has a higher training time than a single decision tree. We should take this into consideration because as we increase the number of trees in a random forest, the time taken to train each of them also increases. That can often be crucial when you’re working with a tight deadline in a machine learning project.\n",
    "\n",
    "Random forest does not rely on the feature importance given by a single decision tree. The decision tree model gives high importance to a particular set of features. But the random forest chooses features randomly during the training process. Therefore, it does not depend highly on any specific set of features. This is a special characteristic of random forest over bagging trees. Therefore, the random forest can generalize over the data in a better way. This randomized feature selection makes random forest much more accurate than a decision tree.\n",
    "\n",
    "\n",
    "Despite instability and dependency on a particular set of features, decision trees are really helpful because they are easier to interpret and faster to train. Anyone with very little knowledge of data science can also use decision trees to make quick data-driven decisions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 3, Q5: Draw the full decision tree after splitting on A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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