Titanic Dataset_V2.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b62f5ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c9e405e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "514540ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4a0e144",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b624d0",
   "metadata": {},
   "source": [
    "# 1.Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f8ff27eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>360</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mockler, Miss. Helen Mary \"Ellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330980</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hedman, Mr. Oskar Arvid</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347089</td>\n",
       "      <td>6.9750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Holverson, Mr. Alexander Oskar</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>585</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Paulner, Mr. Uscher</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3411</td>\n",
       "      <td>8.7125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>835</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allum, Mr. Owen George</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2223</td>\n",
       "      <td>8.3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>517</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Lemore, Mrs. (Amelia Milley)</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 34260</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>F33</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Pengelly, Mr. Frederick William</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28665</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "359          360         1       3  Mockler, Miss. Helen Mary \"Ellie\"  female   \n",
       "804          805         1       3            Hedman, Mr. Oskar Arvid    male   \n",
       "35            36         0       1     Holverson, Mr. Alexander Oskar    male   \n",
       "584          585         0       3                Paulner, Mr. Uscher    male   \n",
       "834          835         0       3             Allum, Mr. Owen George    male   \n",
       "516          517         1       2       Lemore, Mrs. (Amelia Milley)  female   \n",
       "238          239         0       2    Pengelly, Mr. Frederick William    male   \n",
       "\n",
       "      Age  SibSp  Parch      Ticket     Fare Cabin Embarked  \n",
       "359   NaN      0      0      330980   7.8792   NaN        Q  \n",
       "804  27.0      0      0      347089   6.9750   NaN        S  \n",
       "35   42.0      1      0      113789  52.0000   NaN        S  \n",
       "584   NaN      0      0        3411   8.7125   NaN        C  \n",
       "834  18.0      0      0        2223   8.3000   NaN        S  \n",
       "516  34.0      0      0  C.A. 34260  10.5000   F33        S  \n",
       "238  19.0      0      0       28665  10.5000   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "152a273c",
   "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>55</th>\n",
       "      <td>947</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1159</td>\n",
       "      <td>3</td>\n",
       "      <td>Warren, Mr. Charles William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 49867</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1033</td>\n",
       "      <td>1</td>\n",
       "      <td>Daniels, Miss. Sarah</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1140</td>\n",
       "      <td>2</td>\n",
       "      <td>Hold, Mrs. Stephen (Annie Margaret Hill)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26707</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>929</td>\n",
       "      <td>3</td>\n",
       "      <td>Cacic, Miss. Manda</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315087</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>1219</td>\n",
       "      <td>1</td>\n",
       "      <td>Rosenshine, Mr. George (Mr George Thorne\")\"</td>\n",
       "      <td>male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17585</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>1222</td>\n",
       "      <td>2</td>\n",
       "      <td>Davies, Mrs. John Morgan (Elizabeth Agnes Mary...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 33112</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     PassengerId  Pclass                                               Name  \\\n",
       "55           947       3                               Rice, Master. Albert   \n",
       "267         1159       3                        Warren, Mr. Charles William   \n",
       "141         1033       1                               Daniels, Miss. Sarah   \n",
       "248         1140       2           Hold, Mrs. Stephen (Annie Margaret Hill)   \n",
       "37           929       3                                 Cacic, Miss. Manda   \n",
       "327         1219       1        Rosenshine, Mr. George (Mr George Thorne\")\"   \n",
       "330         1222       2  Davies, Mrs. John Morgan (Elizabeth Agnes Mary...   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket      Fare Cabin Embarked  \n",
       "55     male  10.0      4      1      382652   29.1250   NaN        Q  \n",
       "267    male   NaN      0      0  C.A. 49867    7.5500   NaN        S  \n",
       "141  female  33.0      0      0      113781  151.5500   NaN        S  \n",
       "248  female  29.0      1      0       26707   26.0000   NaN        S  \n",
       "37   female  21.0      0      0      315087    8.6625   NaN        S  \n",
       "327    male  46.0      0      0    PC 17585   79.2000   NaN        C  \n",
       "330  female  48.0      0      2  C.A. 33112   36.7500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9aff64c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75ae1d42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24ef5cc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ba9eff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5b454aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "147efa99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Almost 80% of the data in cabin is null; can be removed but will perform more analysis\n",
    "#Age also has missing data\n",
    "#Embarked is null for 2 rows in the test data\n",
    "#PassengerID, Ticket are just ref numbers; can be removed\n",
    "#SibSp, Parch - Maybe we can combine both as one single column as 'family_members' ; will decide later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "325466d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
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       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>891.0</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>1.00</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>446.0000</td>\n",
       "      <td>668.5</td>\n",
       "      <td>891.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        mean         std   min       25%       50%    75%  \\\n",
       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
       "\n",
       "                  max  \n",
       "PassengerId  891.0000  \n",
       "Survived       1.0000  \n",
       "Pclass         3.0000  \n",
       "Age           80.0000  \n",
       "SibSp          8.0000  \n",
       "Parch          6.0000  \n",
       "Fare         512.3292  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6293f80e",
   "metadata": {},
   "source": [
    "## 1.2 Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3cc631f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of the target\n",
    "\n",
    "sns.countplot(x='Survived', data=train)\n",
    "plt.title('Distribution of the Target Atribute - \"Survived\"')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1439e55b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of occurances: \n",
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n",
      "\n",
      "Percentage of occurances: \n",
      "0    61.616162\n",
      "1    38.383838\n",
      "Name: Survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Analyzing the target variable\n",
    "\n",
    "print('Number of occurances: ')\n",
    "print(train['Survived'].value_counts())\n",
    "\n",
    "print('\\nPercentage of occurances: ')\n",
    "print(train['Survived'].value_counts(normalize=True) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be49b916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "4     18\n",
       "3     16\n",
       "8      7\n",
       "5      5\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['SibSp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7322bd8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "3      5\n",
       "5      5\n",
       "4      4\n",
       "6      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Parch'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88951965",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Both 'SibSp' and 'Parch' can be considered as categories\n",
    "\n",
    "numeric_cols = ['Age', 'Fare']\n",
    "category_cols = ['Pclass', 'Sex', 'Cabin', 'SibSp', 'Parch', 'Embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7633d84e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist_plot(df, columns, type='boxplot', label=None):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    for idx, var in enumerate(columns):\n",
    "        plt.subplot(3, 3, idx + 1)\n",
    "        if (type == 'boxplot'):\n",
    "            if not label:\n",
    "                g = sns.boxplot(x=var, data=df, showfliers=True)\n",
    "            else:\n",
    "                g = sns.boxplot(y=var, data=df, showfliers=True, x=label)\n",
    "        elif (type=='histplot'):\n",
    "            if not label:\n",
    "                g = sns.histplot(x=var, data=df)\n",
    "            else:\n",
    "                g = sns.histplot(x=var, data=df, hue=label)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "def count_plot(df, columns, label=None):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    for idx, var in enumerate(columns):\n",
    "        plt.subplot(3, 3, idx + 1)\n",
    "        if not label:\n",
    "            g = sns.countplot(x=var, data=df)\n",
    "        else:\n",
    "            g = sns.countplot(x=var, data=df, hue = label)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b1620b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_plot(train, numeric_cols)\n",
    "dist_plot(train, numeric_cols, label='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0a888b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_plot(train, numeric_cols)\n",
    "dist_plot(train, numeric_cols, label='Survived', type='histplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "489df3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_plot(train, category_cols)\n",
    "count_plot(train, category_cols, label='Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74077d23",
   "metadata": {},
   "source": [
    "***Initial Observations***\n",
    "\n",
    "- *Among survived individuals, male to female ratio is almost 1:2 i.e., double female survived compared to male.*\n",
    "- *Majority of the deceased passengers are from Pclass 3; who probably don't have a cabin? Will further analyze.*\n",
    "- *Looks like if the pasenger embarked his/her journey from Cherbourg, there is a lot of chance of survival. Need to check more here.*\n",
    "- *Passenger deceased are majorly within the age range of 20-40, however the survived count is also better there as more passengers are from that age range. Lets try to analyze it further.*\n",
    "- *The data for fare is highly skewed.*\n",
    "- *Will spare a few more mins to understand if Cabin can be utilized.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40f4ab93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage_calc(df, by, agg):\n",
    "    temp = df.groupby([by])[agg].transform('sum')\n",
    "    pct = round((df[agg]/temp) * 100, 2).astype('str') + '%'\n",
    "    return pct\n",
    "\n",
    "def create_group(df, grp_cols, agg_func='count', temp = 'PassengerId'):\n",
    "    df_grp = df.groupby(grp_cols).aggregate({temp: agg_func}).reset_index()\n",
    "    grp_cols.append('Agg')\n",
    "    df_grp.columns = grp_cols\n",
    "    return df_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9809408",
   "metadata": {},
   "source": [
    "***Male vs Female Survival Stats***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04e7bdf2",
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Agg</th>\n",
       "      <th>PCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>25.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>233</td>\n",
       "      <td>74.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>81.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>18.89%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived  Agg     PCT\n",
       "0  female         0   81   25.8%\n",
       "1  female         1  233   74.2%\n",
       "2    male         0  468  81.11%\n",
       "3    male         1  109  18.89%"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sex_grp = create_group(train, ['Sex', 'Survived'])\n",
    "train_sex_grp['PCT'] = percentage_calc(train_sex_grp, 'Sex', 'Agg')\n",
    "\n",
    "train_sex_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a160c18",
   "metadata": {},
   "source": [
    "*Hence, it is proved here - almost 75% of female survived the mishap whereas more than 80% men were died.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8087c6a",
   "metadata": {},
   "source": [
    "***Survival stats on the basis of Pclass***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "537b940e",
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Agg</th>\n",
       "      <th>PCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>1.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>77</td>\n",
       "      <td>35.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>91</td>\n",
       "      <td>42.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>45</td>\n",
       "      <td>20.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>6</td>\n",
       "      <td>3.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>91</td>\n",
       "      <td>49.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>70</td>\n",
       "      <td>38.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>17</td>\n",
       "      <td>9.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>72</td>\n",
       "      <td>14.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>300</td>\n",
       "      <td>61.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>72</td>\n",
       "      <td>14.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>47</td>\n",
       "      <td>9.57%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass  Survived     Sex  Agg     PCT\n",
       "0        1         0  female    3   1.39%\n",
       "1        1         0    male   77  35.65%\n",
       "2        1         1  female   91  42.13%\n",
       "3        1         1    male   45  20.83%\n",
       "4        2         0  female    6   3.26%\n",
       "5        2         0    male   91  49.46%\n",
       "6        2         1  female   70  38.04%\n",
       "7        2         1    male   17   9.24%\n",
       "8        3         0  female   72  14.66%\n",
       "9        3         0    male  300   61.1%\n",
       "10       3         1  female   72  14.66%\n",
       "11       3         1    male   47   9.57%"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_class_grp = create_group(train, ['Pclass', 'Survived', 'Sex'])\n",
    "train_class_grp['PCT'] = percentage_calc(train_class_grp, 'Pclass', 'Agg')\n",
    "\n",
    "train_class_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec57a4b4",
   "metadata": {},
   "source": [
    "- *Most of the deceased individuals are from lower class - 25% of all deaths.*\n",
    "- *More than 60% of the survived passengers boarded are from upper class.*\n",
    "- *More than 40% of all survived individuals from upper class are female.*\n",
    "- *Similar trend is observed for middle class as well; almost 40% of survived passengers are female.*\n",
    "- *Even in lower class, almost 15% of survived passengers are female.*\n",
    "\n",
    "*Hence being female and if boarded one of Pclass 1 & 2 then survival percentage increases significantly.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a4af3b",
   "metadata": {},
   "source": [
    "***Studying Embarkation point***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "395b24e4",
   "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>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Agg</th>\n",
       "      <th>PCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>14.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>19.64%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>25.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>8.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>10.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>5.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>11.69%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>46.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>31.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.93%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>8.54%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>7.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>12.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>231</td>\n",
       "      <td>35.87%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>7.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>61</td>\n",
       "      <td>9.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>5.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>2.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>5.28%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked  Survived     Sex  Pclass  Agg     PCT\n",
       "0         C         0  female       1    1    0.6%\n",
       "1         C         0  female       3    8   4.76%\n",
       "2         C         0    male       1   25  14.88%\n",
       "3         C         0    male       2    8   4.76%\n",
       "4         C         0    male       3   33  19.64%\n",
       "5         C         1  female       1   42   25.0%\n",
       "6         C         1  female       2    7   4.17%\n",
       "7         C         1  female       3   15   8.93%\n",
       "8         C         1    male       1   17  10.12%\n",
       "9         C         1    male       2    2   1.19%\n",
       "10        C         1    male       3   10   5.95%\n",
       "11        Q         0  female       3    9  11.69%\n",
       "12        Q         0    male       1    1    1.3%\n",
       "13        Q         0    male       2    1    1.3%\n",
       "14        Q         0    male       3   36  46.75%\n",
       "15        Q         1  female       1    1    1.3%\n",
       "16        Q         1  female       2    2    2.6%\n",
       "17        Q         1  female       3   24  31.17%\n",
       "18        Q         1    male       3    3    3.9%\n",
       "19        S         0  female       1    2   0.31%\n",
       "20        S         0  female       2    6   0.93%\n",
       "21        S         0  female       3   55   8.54%\n",
       "22        S         0    male       1   51   7.92%\n",
       "23        S         0    male       2   82  12.73%\n",
       "24        S         0    male       3  231  35.87%\n",
       "25        S         1  female       1   46   7.14%\n",
       "26        S         1  female       2   61   9.47%\n",
       "27        S         1  female       3   33   5.12%\n",
       "28        S         1    male       1   28   4.35%\n",
       "29        S         1    male       2   15   2.33%\n",
       "30        S         1    male       3   34   5.28%"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_emb_grp = create_group(train, ['Embarked', 'Survived', 'Sex', 'Pclass'])\n",
    "train_emb_grp['PCT'] = percentage_calc(train_emb_grp, 'Embarked', 'Agg')\n",
    "\n",
    "train_emb_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4713f2a2",
   "metadata": {},
   "source": [
    "<i><u>Cherbourg</u></i>\n",
    "- *About 5% female passengers died whereas almost 40% of men were casualties of the mishap while boarding from Cherbourg.*\n",
    "- *Survival chances gets increased when a female is from upper class passengers. However, the same is not true for males - almost 15% of deceased men were from upper class.*\n",
    "- *All female travelers from middle class section survived the mishap.*\n",
    "\n",
    "<i><u>Queensland</u></i>\n",
    "- *Not a single female died from upper or middle class section who started their journey from Queensland.*\n",
    "- *Lower class males died in numbers - more than 30% whereas over 45% lower class female survived.*\n",
    "\n",
    "<i><u>Southampton</u></i>\n",
    "- *Male passengers died in numbers, around 36% are from lower class whereas very minimal from upper class individuals.*\n",
    "- *347 males from lower class started their journey from Southampton i.e., almost 40% of total travellers.* \n",
    "\n",
    "\n",
    "*For males from lower class section significantly rises the probability of death from the accident.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58071c4c",
   "metadata": {},
   "source": [
    "***Lets analyze the column for cabin***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae0d5e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cabin_null = train.loc[(train['Cabin'].isnull()) & (train['Sex'] == 'male')]\n",
    "\n",
    "count_plot(cabin_null, ['Pclass'], label='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7e2f93a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cabin_null = train.loc[(train['Cabin'].isnull()) & (train['Sex'] == 'female')]\n",
    "\n",
    "count_plot(cabin_null, ['Pclass'], label='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cf0df74d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n    0.602339\n",
      "C    0.102339\n",
      "B    0.102339\n",
      "D    0.073099\n",
      "E    0.070175\n",
      "F    0.023392\n",
      "A    0.020468\n",
      "G    0.005848\n",
      "Name: Cabin_Series, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_cabin = train.copy()\n",
    "\n",
    "cabins = df_cabin['Cabin']\n",
    "df_cabin['Cabin_Series'] = [str(cabin)[0] for cabin in cabins]\n",
    "\n",
    "df_cabin_surv = df_cabin.loc[df_cabin['Survived'] == 1][['Cabin_Series', 'Pclass']]\n",
    "\n",
    "print(df_cabin_surv['Cabin_Series'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4fc460aa",
   "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>Cabin_Series</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>D</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>G</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>n</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>n</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>n</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>n</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>n</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cabin_Series  Pclass     Sex  Agg\n",
       "0             A       1  female    1\n",
       "1             A       1    male   14\n",
       "2             B       1  female   27\n",
       "3             B       1    male   20\n",
       "4             C       1  female   27\n",
       "5             C       1    male   32\n",
       "6             D       1  female   16\n",
       "7             D       1    male   13\n",
       "8             D       2  female    2\n",
       "9             D       2    male    2\n",
       "10            E       1  female   10\n",
       "11            E       1    male   15\n",
       "12            E       2  female    4\n",
       "13            E       3  female    1\n",
       "14            E       3    male    2\n",
       "15            F       2  female    4\n",
       "16            F       2    male    4\n",
       "17            F       3  female    1\n",
       "18            F       3    male    4\n",
       "19            G       3  female    4\n",
       "20            T       1    male    1\n",
       "21            n       1  female   13\n",
       "22            n       1    male   27\n",
       "23            n       2  female   66\n",
       "24            n       2    male  102\n",
       "25            n       3  female  138\n",
       "26            n       3    male  341"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cabin_class = create_group(df_cabin, ['Cabin_Series', 'Pclass', 'Sex'])\n",
    "df_cabin_class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4b5bc0",
   "metadata": {},
   "source": [
    "- *Wherever we have cabin as null, majority of them are males and from lower class.*\n",
    "- *Does that mean, the individuals with cabin as null has no dedicated cabin?*\n",
    "- *Cabins with null values are less for female. If we assume the previous point as true then is this an indicative reason for a higher survival rates among females compared to males?*\n",
    "- *Without cabin reduces survival chances.*\n",
    "- *A series has low survival percentage - less women count*\n",
    "- *Maybe we can set a flag (y/n) for having cabin or not.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0607bc3",
   "metadata": {},
   "source": [
    "***Analyzing the column for Fare***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d1fcb0bd",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>413</th>\n",
       "      <td>414</td>\n",
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       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
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       "      <td>0</td>\n",
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       "      <th>466</th>\n",
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       "      <td>Johnson, Mr. Alfred</td>\n",
       "      <td>male</td>\n",
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       "      <td>Andrews, Mr. Thomas Jr</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>S</td>\n",
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       "      <th>815</th>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
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       "      <th>822</th>\n",
       "      <td>823</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19972</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                              Name   Sex  \\\n",
       "179          180         0       3               Leonard, Mr. Lionel  male   \n",
       "263          264         0       1             Harrison, Mr. William  male   \n",
       "271          272         1       3      Tornquist, Mr. William Henry  male   \n",
       "277          278         0       2       Parkes, Mr. Francis \"Frank\"  male   \n",
       "302          303         0       3   Johnson, Mr. William Cahoone Jr  male   \n",
       "413          414         0       2    Cunningham, Mr. Alfred Fleming  male   \n",
       "466          467         0       2             Campbell, Mr. William  male   \n",
       "481          482         0       2  Frost, Mr. Anthony Wood \"Archie\"  male   \n",
       "597          598         0       3               Johnson, Mr. Alfred  male   \n",
       "633          634         0       1     Parr, Mr. William Henry Marsh  male   \n",
       "674          675         0       2        Watson, Mr. Ennis Hastings  male   \n",
       "732          733         0       2              Knight, Mr. Robert J  male   \n",
       "806          807         0       1            Andrews, Mr. Thomas Jr  male   \n",
       "815          816         0       1                  Fry, Mr. Richard  male   \n",
       "822          823         0       1   Reuchlin, Jonkheer. John George  male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "179  36.0      0      0    LINE   0.0   NaN        S  \n",
       "263  40.0      0      0  112059   0.0   B94        S  \n",
       "271  25.0      0      0    LINE   0.0   NaN        S  \n",
       "277   NaN      0      0  239853   0.0   NaN        S  \n",
       "302  19.0      0      0    LINE   0.0   NaN        S  \n",
       "413   NaN      0      0  239853   0.0   NaN        S  \n",
       "466   NaN      0      0  239853   0.0   NaN        S  \n",
       "481   NaN      0      0  239854   0.0   NaN        S  \n",
       "597  49.0      0      0    LINE   0.0   NaN        S  \n",
       "633   NaN      0      0  112052   0.0   NaN        S  \n",
       "674   NaN      0      0  239856   0.0   NaN        S  \n",
       "732   NaN      0      0  239855   0.0   NaN        S  \n",
       "806  39.0      0      0  112050   0.0   A36        S  \n",
       "815   NaN      0      0  112058   0.0  B102        S  \n",
       "822  38.0      0      0   19972   0.0   NaN        S  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['Fare'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "036b540c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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>Pclass</th>\n",
       "      <th>Mean_Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>84.154687</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>20.662183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13.675550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Mean_Fare\n",
       "0       1  84.154687\n",
       "1       2  20.662183\n",
       "2       3  13.675550"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_fare_grp = create_group(train, ['Pclass'], agg_func = 'mean', temp='Fare')\n",
    "\n",
    "train_fare_grp.columns = ['Pclass', 'Mean_Fare']\n",
    "train_fare_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "131bea31",
   "metadata": {},
   "source": [
    "*There are few rows where Fare=0 ; lets put mean value as per their class.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e6825d",
   "metadata": {},
   "source": [
    "***Analyzing age of passengers***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1122f9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Mean_Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>38.233441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>29.877630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>25.140620</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Pclass   Mean_Age\n",
       "0       1  38.233441\n",
       "1       2  29.877630\n",
       "2       3  25.140620"
      ]
     },
     "execution_count": 33,
     "metadata": {},
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    }
   ],
   "source": [
    "train_age_class = create_group(train, ['Pclass'], agg_func='mean', temp='Age')\n",
    "\n",
    "train_age_class.columns = ['Pclass', 'Mean_Age']\n",
    "train_age_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e9eb3d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>25.666667</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>34.939024</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>28.080882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>33.369048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>16.022000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>23.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>19.329787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>27.255814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>22.274211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass     Sex  Survived   Mean_Age\n",
       "0        1  female         0  25.666667\n",
       "1        1  female         1  34.939024\n",
       "2        1    male         0  44.581967\n",
       "3        1    male         1  36.248000\n",
       "4        2  female         0  36.000000\n",
       "5        2  female         1  28.080882\n",
       "6        2    male         0  33.369048\n",
       "7        2    male         1  16.022000\n",
       "8        3  female         0  23.818182\n",
       "9        3  female         1  19.329787\n",
       "10       3    male         0  27.255814\n",
       "11       3    male         1  22.274211"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_age_grp = create_group(train, ['Pclass', 'Sex', 'Survived'], agg_func='mean', temp='Age')\n",
    "\n",
    "train_age_grp.columns = ['Pclass', 'Sex', 'Survived', 'Mean_Age']\n",
    "train_age_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bcdd63",
   "metadata": {},
   "source": [
    "*Among upper class passengers, mostly older female survived where as for other classes majority of the younger generation made the cut regardless of their gender.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0a96d1",
   "metadata": {},
   "source": [
    "***Checking Sibsp & Parch***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9553b1cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>210</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
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       "      <td>15</td>\n",
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       "      <th>5</th>\n",
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       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SibSp  Survived  Agg\n",
       "0       0         0  398\n",
       "1       0         1  210\n",
       "2       1         0   97\n",
       "3       1         1  112\n",
       "4       2         0   15\n",
       "5       2         1   13\n",
       "6       3         0   12\n",
       "7       3         1    4\n",
       "8       4         0   15\n",
       "9       4         1    3\n",
       "10      5         0    5\n",
       "11      8         0    7"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Sib = create_group(train, ['SibSp', 'Survived'])\n",
    "train_Sib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85766700",
   "metadata": {},
   "source": [
    "*Less sibling increases the survival rate.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fad18111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parch  Survived  Agg\n",
       "0       0         0  445\n",
       "1       0         1  233\n",
       "2       1         0   53\n",
       "3       1         1   65\n",
       "4       2         0   40\n",
       "5       2         1   40\n",
       "6       3         0    2\n",
       "7       3         1    3\n",
       "8       4         0    4\n",
       "9       5         0    4\n",
       "10      5         1    1\n",
       "11      6         0    1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Par = create_group(train, ['Parch', 'Survived'])\n",
    "train_Par"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c5fe84",
   "metadata": {},
   "source": [
    "*Almost similar trend is visible for Parch.*\n",
    "*Can be created a single feature combining both these columns.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d167a4",
   "metadata": {},
   "source": [
    "**Discovered Information**\n",
    "\n",
    "- Upper & middle class females has a better survival rates than others. \n",
    "- Passengers with less family members has a better stats for survival.\n",
    "- Considering missing cabin value as without a cabin and it decreases chance of survival.\n",
    "- Lower class individuals have less chance of survival and most lower class people have embarked their journey from Southampton.\n",
    "\n",
    "**Actions to be taken**\n",
    "\n",
    "1. Remove 'PassengerID', 'Ticket' , 'Name' columns.\n",
    "2. Create a new feature \"Cabin_Flag\" with value as 1 & 0 for NaN and values respectively. Then remove 'Cabin'.\n",
    "3. 'Fare' with 0 should be updated with mean per their respective class.\n",
    "4. Missing values for 'Emabarked' can be replaced with mode.\n",
    "5. Missing 'Age' values should be replaced with mean.\n",
    "6. Create a new feature 'Family' and aggregate the values from 'SibSp' & 'Parch'. Then remove 'SibSp' & 'Parch'.\n",
    "7. Treat the outliers in 'Age' & 'Fare'.\n",
    "8. Create dummies for 'Sex' and 'Embarked'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36f3571",
   "metadata": {},
   "source": [
    "# 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "028f0d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.preprocessing import KBinsDiscretizer, FunctionTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer, make_column_selector\n",
    "#from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "217f08cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.pipeline import Pipeline\n",
    "from imblearn.under_sampling import TomekLinks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97975e0d",
   "metadata": {},
   "source": [
    "***Getting X and y seperated***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1fb601bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['Survived']\n",
    "X_train = train.drop(['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c9724060",
   "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>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>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>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>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>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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "98218a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "886    0\n",
       "887    1\n",
       "888    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d645edb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat_columns = ['PassengerId', 'Pclass', 'Name', 'Sex', 'SibSp', 'Parch', 'Ticket', 'Cabin', 'Embarked']\n",
    "num_columns = ['Age', 'Fare', 'Parch', 'SibSp']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de364ab0",
   "metadata": {},
   "source": [
    "***Treating the ouliers***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ec6f1e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_treatment(X, columns):\n",
    "    new_X = X.copy()\n",
    "    X_treat = new_X[num_columns].fillna(value=0)\n",
    "    for column in columns:\n",
    "        Q1 = np.percentile(X_treat[column], 25.)\n",
    "        Q2 = np.percentile(X_treat[column], 50.)\n",
    "        Q3 = np.percentile(X_treat[column], 75.)\n",
    "        \n",
    "        cut_off = (Q3-Q1) * 1.5\n",
    "        upper, lower = Q3 + cut_off, Q1 - cut_off\n",
    "        print(upper, lower)\n",
    "        new_X.loc[(new_X[column] < lower), column] = lower\n",
    "        new_X.loc[(new_X[column] > upper), column] = upper\n",
    "    \n",
    "    return new_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "38e9c356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X_train[num_columns] = X_train[num_columns].fillna(value=0) #Filling all NaN values with zeros - as of now\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9446d13d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.5 -37.5\n",
      "65.6344 -26.724\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_out = outlier_treatment(X_train, ['Age', 'Fare'])\n",
    "dist_plot(train_out, ['Age', 'Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e9775a79",
   "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>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>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>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>65.6344</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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>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>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                               Name  \\\n",
       "0              1       3                            Braund, Mr. Owen Harris   \n",
       "1              2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2              3       3                             Heikkinen, Miss. Laina   \n",
       "3              4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4              5       3                           Allen, Mr. William Henry   \n",
       "..           ...     ...                                                ...   \n",
       "886          887       2                              Montvila, Rev. Juozas   \n",
       "887          888       1                       Graham, Miss. Margaret Edith   \n",
       "888          889       3           Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890       1                              Behr, Mr. Karl Howell   \n",
       "890          891       3                                Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1    female  38.0      1      0          PC 17599  65.6344   C85        C  \n",
       "2    female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3    female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4      male  35.0      0      0            373450   8.0500   NaN        S  \n",
       "..      ...   ...    ...    ...               ...      ...   ...      ...  \n",
       "886    male  27.0      0      0            211536  13.0000   NaN        S  \n",
       "887  female  19.0      0      0            112053  30.0000   B42        S  \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "889    male  26.0      0      0            111369  30.0000  C148        C  \n",
       "890    male  32.0      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf0e040",
   "metadata": {},
   "source": [
    "***Removing and creating applicable columns***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ea09cdd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_out.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8cef9e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CategoryTransformer(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        self.cols_to_remove = ['PassengerId', 'Ticket', 'Name']\n",
    "        self.attr_combo = ['Cabin', 'SibSp', 'Parch']\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        temp = X.copy()\n",
    "        temp['Cabin_Flag'] = temp['Cabin'].apply(lambda x: 0 if x is np.nan else 1)\n",
    "        temp['Family'] = temp['SibSp'] + temp['Parch']\n",
    "        for i in range(len(self.attr_combo)):\n",
    "            self.cols_to_remove.append(self.attr_combo[i])\n",
    "        new_X = temp.drop(self.cols_to_remove, axis=1)\n",
    "        other_cols = ['Age', 'Fare', 'Embarked', 'Sex']\n",
    "        trans_X = new_X.drop(other_cols, axis=1)\n",
    "        return trans_X.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "25dd4d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 0, 1],\n",
       "       [1, 1, 1],\n",
       "       [3, 0, 0],\n",
       "       ...,\n",
       "       [3, 0, 3],\n",
       "       [1, 1, 0],\n",
       "       [3, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_trans = CategoryTransformer()\n",
    "train_attr = cat_trans.transform(train_out)\n",
    "\n",
    "train_attr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63db76b2",
   "metadata": {},
   "source": [
    "***Data Pipeline creation***\n",
    "\n",
    "1. Imputing missing values for numeric columns - Age, Fare\n",
    "2. Unwanted feature removal & new columns creation\n",
    "3. OneHotEncoding for Embarked & Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "00eeeee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cols = ['Age', 'Fare']\n",
    "cat_cols = ['Embarked', 'Sex']\n",
    "all_cols = list(train_out.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "daec759e",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer1 = SimpleImputer(missing_values=0, strategy='mean')\n",
    "imputer2 = SimpleImputer(strategy='mean')\n",
    "imputer3 = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "encoder = OneHotEncoder(dtype = 'int')\n",
    "transformer = CategoryTransformer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e668e53b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer2&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;imputer1&#x27;,\n",
       "                                                  SimpleImputer(missing_values=0))]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;Fare&#x27;]),\n",
       "                                (&#x27;clean&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                                  CategoryTransformer())]),\n",
       "                                 [&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;, &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;,\n",
       "                                  &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;, &#x27;Fare&#x27;, &#x27;Cabin&#x27;,\n",
       "                                  &#x27;Embarked&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer3&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;encoder&#x27;,\n",
       "                                                  OneHotEncoder(dtype=&#x27;int&#x27;))]),\n",
       "                                 [&#x27;Embarked&#x27;, &#x27;Sex&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer2&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;imputer1&#x27;,\n",
       "                                                  SimpleImputer(missing_values=0))]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;Fare&#x27;]),\n",
       "                                (&#x27;clean&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                                  CategoryTransformer())]),\n",
       "                                 [&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;, &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;,\n",
       "                                  &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;, &#x27;Fare&#x27;, &#x27;Cabin&#x27;,\n",
       "                                  &#x27;Embarked&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer3&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;encoder&#x27;,\n",
       "                                                  OneHotEncoder(dtype=&#x27;int&#x27;))]),\n",
       "                                 [&#x27;Embarked&#x27;, &#x27;Sex&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;Fare&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(missing_values=0)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">clean</label><div class=\"sk-toggleable__content\"><pre>[&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;, &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;, &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;, &#x27;Fare&#x27;, &#x27;Cabin&#x27;, &#x27;Embarked&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CategoryTransformer</label><div class=\"sk-toggleable__content\"><pre>CategoryTransformer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Embarked&#x27;, &#x27;Sex&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(dtype=&#x27;int&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('imputer2', SimpleImputer()),\n",
       "                                                 ('imputer1',\n",
       "                                                  SimpleImputer(missing_values=0))]),\n",
       "                                 ['Age', 'Fare']),\n",
       "                                ('clean',\n",
       "                                 Pipeline(steps=[('transformer',\n",
       "                                                  CategoryTransformer())]),\n",
       "                                 ['PassengerId', 'Pclass', 'Name', 'Sex', 'Age',\n",
       "                                  'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin',\n",
       "                                  'Embarked']),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('imputer3',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('encoder',\n",
       "                                                  OneHotEncoder(dtype='int'))]),\n",
       "                                 ['Embarked', 'Sex'])])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline = Pipeline([('imputer2', SimpleImputer(strategy='mean')),\n",
    "                         ('imputer1', SimpleImputer(missing_values=0, strategy='mean'))\n",
    "                       ])\n",
    "\n",
    "clean_pipeline = Pipeline([('transformer', CategoryTransformer())])\n",
    "\n",
    "cat_pipeline = Pipeline([('imputer3', SimpleImputer(strategy='most_frequent')),\n",
    "                        ('encoder', OneHotEncoder(dtype = 'int'))])\n",
    "\n",
    "\n",
    "data_pipeline = ColumnTransformer([\n",
    "    ('num', num_pipeline, num_cols),\n",
    "    ('clean', clean_pipeline, all_cols),\n",
    "    ('cat', cat_pipeline, cat_cols)\n",
    "])\n",
    "\n",
    "data_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "79803d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_pipeline = Pipeline([('imputer1', SimpleImputer(missing_values=0, strategy='mean')),\n",
    "#                        ('imputer2', SimpleImputer(strategy='mean'))])\n",
    "\n",
    "# clean_pipeline = Pipeline([('transformer', CategoryTransformer())])\n",
    "\n",
    "# cat_pipeline = Pipeline([('imputer3', SimpleImputer(strategy='most_frequent')),\n",
    "#                         ('encoder', OneHotEncoder(dtype = 'int'))])\n",
    "\n",
    "\n",
    "# data_pipeline = ColumnTransformer([\n",
    "#     ('num', num_pipeline, num_cols),\n",
    "#     ('clean', clean_pipeline, all_cols),\n",
    "#     ('cat', cat_pipeline, cat_cols)\n",
    "# ])\n",
    "\n",
    "# data_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "371add67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.        ,  7.25      ,  3.        , ...,  1.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [38.        , 65.6344    ,  1.        , ...,  0.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [26.        ,  7.925     ,  3.        , ...,  1.        ,\n",
       "         1.        ,  0.        ],\n",
       "       ...,\n",
       "       [29.69701681, 23.45      ,  3.        , ...,  1.        ,\n",
       "         1.        ,  0.        ],\n",
       "       [26.        , 30.        ,  1.        , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [32.        ,  7.75      ,  3.        , ...,  0.        ,\n",
       "         0.        ,  1.        ]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prepared = data_pipeline.fit_transform(train_out)\n",
    "\n",
    "data_prepared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6db1c640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.  ,  7.25,  3.  ,  0.  ,  1.  ,  0.  ,  0.  ,  1.  ,  0.  ,\n",
       "        1.  ])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prepared[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8b11d924",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n",
    "smotek = SMOTETomek()\n",
    "\n",
    "X_smote, y_smote = smote.fit_resample(data_prepared, y_train)\n",
    "\n",
    "X_smotek, y_smotek = smotek.fit_resample(data_prepared, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d86a4592",
   "metadata": {},
   "source": [
    "# 3.Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "21921a13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f6fee142",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from imblearn.ensemble import BalancedBaggingClassifier\n",
    "from imblearn.ensemble import BalancedRandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f6ed19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "15a5e958",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_set():\n",
    "    model_set = []\n",
    "    \n",
    "    model_set.append(('LR', LogisticRegression()))\n",
    "    model_set.append(('DT', DecisionTreeClassifier(random_state=seed)))\n",
    "    model_set.append(('RF', RandomForestClassifier(random_state=seed)))\n",
    "    model_set.append(('SV', SVC(random_state=seed)))\n",
    "    model_set.append(('EX', ExtraTreesClassifier(random_state=seed)))\n",
    "    model_set.append(('KN', KNeighborsClassifier()))\n",
    "    model_set.append(('GB', GradientBoostingClassifier(random_state=seed)))\n",
    "    model_set.append(('AB', AdaBoostClassifier(random_state=seed)))\n",
    "    model_set.append(('NB', GaussianNB()))\n",
    "    model_set.append(('BRF', BalancedRandomForestClassifier(random_state=seed)))\n",
    "    \n",
    "    return model_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "83cf1283",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_models(X, y, modelset, scale = None):\n",
    "    cols = ['Model', 'Accuracy Mean', 'Accuracy STD', 'F1 Score Mean', 'F1 Score STD']\n",
    "    \n",
    "    models = []\n",
    "    \n",
    "    accuracy_mean = []\n",
    "    accuracy_std = []\n",
    "    \n",
    "    f1_score_mean = []\n",
    "    f1_score_std = []\n",
    "    \n",
    "    if scale == 'MinMax':\n",
    "        scale = MinMaxScaler()\n",
    "        X = scale.fit_transform(X)\n",
    "    if scale == 'Standard':\n",
    "        scale = StandardScaler()\n",
    "        X = scale.fit_transform(X)\n",
    "    \n",
    "    for name, model in modelset:\n",
    "        kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
    "        cv_acc = cross_val_score(model, X, y, cv=kfold, scoring='accuracy')\n",
    "        cv_f1 = cross_val_score(model, X, y, cv=kfold, scoring='f1')\n",
    "        \n",
    "        models.append(model.__class__.__name__)\n",
    "        \n",
    "        accuracy_mean.append(cv_acc.mean())\n",
    "        accuracy_std.append(cv_acc.std())\n",
    "        \n",
    "        f1_score_mean.append(cv_f1.mean())\n",
    "        f1_score_std.append(cv_f1.std())\n",
    "    \n",
    "    \n",
    "    score_matrix = list(zip(models, accuracy_mean, accuracy_std, f1_score_mean, f1_score_std))\n",
    "    df = pd.DataFrame(data = score_matrix, columns=cols)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ca6492",
   "metadata": {},
   "source": [
    "***Base Model evaluation scores without resampling***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "644d7742",
   "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>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.799064</td>\n",
       "      <td>0.037585</td>\n",
       "      <td>0.730350</td>\n",
       "      <td>0.058506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.765331</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>0.702622</td>\n",
       "      <td>0.053360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.815918</td>\n",
       "      <td>0.025841</td>\n",
       "      <td>0.756316</td>\n",
       "      <td>0.042669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.691311</td>\n",
       "      <td>0.029392</td>\n",
       "      <td>0.482603</td>\n",
       "      <td>0.056587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.795693</td>\n",
       "      <td>0.033083</td>\n",
       "      <td>0.730129</td>\n",
       "      <td>0.048515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.723945</td>\n",
       "      <td>0.037999</td>\n",
       "      <td>0.628993</td>\n",
       "      <td>0.049982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.827116</td>\n",
       "      <td>0.023920</td>\n",
       "      <td>0.756547</td>\n",
       "      <td>0.039828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.805768</td>\n",
       "      <td>0.030879</td>\n",
       "      <td>0.739623</td>\n",
       "      <td>0.047969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.765431</td>\n",
       "      <td>0.038417</td>\n",
       "      <td>0.715620</td>\n",
       "      <td>0.039644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.802422</td>\n",
       "      <td>0.029201</td>\n",
       "      <td>0.751337</td>\n",
       "      <td>0.046309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.799064      0.037585       0.730350   \n",
       "1          DecisionTreeClassifier       0.765331      0.045112       0.702622   \n",
       "2          RandomForestClassifier       0.815918      0.025841       0.756316   \n",
       "3                             SVC       0.691311      0.029392       0.482603   \n",
       "4            ExtraTreesClassifier       0.795693      0.033083       0.730129   \n",
       "5            KNeighborsClassifier       0.723945      0.037999       0.628993   \n",
       "6      GradientBoostingClassifier       0.827116      0.023920       0.756547   \n",
       "7              AdaBoostClassifier       0.805768      0.030879       0.739623   \n",
       "8                      GaussianNB       0.765431      0.038417       0.715620   \n",
       "9  BalancedRandomForestClassifier       0.802422      0.029201       0.751337   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.058506  \n",
       "1      0.053360  \n",
       "2      0.042669  \n",
       "3      0.056587  \n",
       "4      0.048515  \n",
       "5      0.049982  \n",
       "6      0.039828  \n",
       "7      0.047969  \n",
       "8      0.039644  \n",
       "9      0.046309  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = model_set()\n",
    "score_models(data_prepared, y_train, models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fa757f",
   "metadata": {},
   "source": [
    "***Base Model evaluation with SMOTE***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dd086c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.788749</td>\n",
       "      <td>0.022972</td>\n",
       "      <td>0.784768</td>\n",
       "      <td>0.026128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.810601</td>\n",
       "      <td>0.034161</td>\n",
       "      <td>0.809841</td>\n",
       "      <td>0.034064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.847957</td>\n",
       "      <td>0.024222</td>\n",
       "      <td>0.846425</td>\n",
       "      <td>0.025806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.661176</td>\n",
       "      <td>0.047051</td>\n",
       "      <td>0.620726</td>\n",
       "      <td>0.058292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.834254</td>\n",
       "      <td>0.025703</td>\n",
       "      <td>0.833783</td>\n",
       "      <td>0.024486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.770517</td>\n",
       "      <td>0.030191</td>\n",
       "      <td>0.780005</td>\n",
       "      <td>0.031287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.848849</td>\n",
       "      <td>0.030432</td>\n",
       "      <td>0.842808</td>\n",
       "      <td>0.033815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.817882</td>\n",
       "      <td>0.039681</td>\n",
       "      <td>0.816507</td>\n",
       "      <td>0.039873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.765046</td>\n",
       "      <td>0.032393</td>\n",
       "      <td>0.768306</td>\n",
       "      <td>0.029077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.846138</td>\n",
       "      <td>0.027528</td>\n",
       "      <td>0.844098</td>\n",
       "      <td>0.030377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.788749      0.022972       0.784768   \n",
       "1          DecisionTreeClassifier       0.810601      0.034161       0.809841   \n",
       "2          RandomForestClassifier       0.847957      0.024222       0.846425   \n",
       "3                             SVC       0.661176      0.047051       0.620726   \n",
       "4            ExtraTreesClassifier       0.834254      0.025703       0.833783   \n",
       "5            KNeighborsClassifier       0.770517      0.030191       0.780005   \n",
       "6      GradientBoostingClassifier       0.848849      0.030432       0.842808   \n",
       "7              AdaBoostClassifier       0.817882      0.039681       0.816507   \n",
       "8                      GaussianNB       0.765046      0.032393       0.768306   \n",
       "9  BalancedRandomForestClassifier       0.846138      0.027528       0.844098   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.026128  \n",
       "1      0.034064  \n",
       "2      0.025806  \n",
       "3      0.058292  \n",
       "4      0.024486  \n",
       "5      0.031287  \n",
       "6      0.033815  \n",
       "7      0.039873  \n",
       "8      0.029077  \n",
       "9      0.030377  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(X_smote, y_smote, models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fad7426",
   "metadata": {},
   "source": [
    "***Base Model evaluation with SMOTETomek***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d3000b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.809338</td>\n",
       "      <td>0.035629</td>\n",
       "      <td>0.805428</td>\n",
       "      <td>0.039026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.846806</td>\n",
       "      <td>0.030793</td>\n",
       "      <td>0.847844</td>\n",
       "      <td>0.028746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.871598</td>\n",
       "      <td>0.027680</td>\n",
       "      <td>0.870586</td>\n",
       "      <td>0.029881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.667065</td>\n",
       "      <td>0.040909</td>\n",
       "      <td>0.640159</td>\n",
       "      <td>0.050203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.856785</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>0.857195</td>\n",
       "      <td>0.033312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.760930</td>\n",
       "      <td>0.050260</td>\n",
       "      <td>0.768569</td>\n",
       "      <td>0.047452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.864706</td>\n",
       "      <td>0.027821</td>\n",
       "      <td>0.861515</td>\n",
       "      <td>0.030334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.843914</td>\n",
       "      <td>0.028686</td>\n",
       "      <td>0.842802</td>\n",
       "      <td>0.028554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.790526</td>\n",
       "      <td>0.038196</td>\n",
       "      <td>0.791164</td>\n",
       "      <td>0.040187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.868618</td>\n",
       "      <td>0.030445</td>\n",
       "      <td>0.866758</td>\n",
       "      <td>0.033406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.809338      0.035629       0.805428   \n",
       "1          DecisionTreeClassifier       0.846806      0.030793       0.847844   \n",
       "2          RandomForestClassifier       0.871598      0.027680       0.870586   \n",
       "3                             SVC       0.667065      0.040909       0.640159   \n",
       "4            ExtraTreesClassifier       0.856785      0.032544       0.857195   \n",
       "5            KNeighborsClassifier       0.760930      0.050260       0.768569   \n",
       "6      GradientBoostingClassifier       0.864706      0.027821       0.861515   \n",
       "7              AdaBoostClassifier       0.843914      0.028686       0.842802   \n",
       "8                      GaussianNB       0.790526      0.038196       0.791164   \n",
       "9  BalancedRandomForestClassifier       0.868618      0.030445       0.866758   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.039026  \n",
       "1      0.028746  \n",
       "2      0.029881  \n",
       "3      0.050203  \n",
       "4      0.033312  \n",
       "5      0.047452  \n",
       "6      0.030334  \n",
       "7      0.028554  \n",
       "8      0.040187  \n",
       "9      0.033406  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(X_smotek, y_smotek, models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18c3dc5",
   "metadata": {},
   "source": [
    "***Scoring Models with MinMaxScaler without resampling***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f80f0d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.794569</td>\n",
       "      <td>0.029987</td>\n",
       "      <td>0.723052</td>\n",
       "      <td>0.048290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.764207</td>\n",
       "      <td>0.046099</td>\n",
       "      <td>0.700491</td>\n",
       "      <td>0.055904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.814794</td>\n",
       "      <td>0.028652</td>\n",
       "      <td>0.754390</td>\n",
       "      <td>0.046466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.810275</td>\n",
       "      <td>0.029737</td>\n",
       "      <td>0.703444</td>\n",
       "      <td>0.061947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.795693</td>\n",
       "      <td>0.033083</td>\n",
       "      <td>0.730129</td>\n",
       "      <td>0.048515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.806904</td>\n",
       "      <td>0.024315</td>\n",
       "      <td>0.733069</td>\n",
       "      <td>0.040632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.827116</td>\n",
       "      <td>0.023920</td>\n",
       "      <td>0.756547</td>\n",
       "      <td>0.039828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.805768</td>\n",
       "      <td>0.030879</td>\n",
       "      <td>0.739623</td>\n",
       "      <td>0.047969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.765431</td>\n",
       "      <td>0.038417</td>\n",
       "      <td>0.715620</td>\n",
       "      <td>0.039644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.803546</td>\n",
       "      <td>0.029645</td>\n",
       "      <td>0.752382</td>\n",
       "      <td>0.046908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.794569      0.029987       0.723052   \n",
       "1          DecisionTreeClassifier       0.764207      0.046099       0.700491   \n",
       "2          RandomForestClassifier       0.814794      0.028652       0.754390   \n",
       "3                             SVC       0.810275      0.029737       0.703444   \n",
       "4            ExtraTreesClassifier       0.795693      0.033083       0.730129   \n",
       "5            KNeighborsClassifier       0.806904      0.024315       0.733069   \n",
       "6      GradientBoostingClassifier       0.827116      0.023920       0.756547   \n",
       "7              AdaBoostClassifier       0.805768      0.030879       0.739623   \n",
       "8                      GaussianNB       0.765431      0.038417       0.715620   \n",
       "9  BalancedRandomForestClassifier       0.803546      0.029645       0.752382   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.048290  \n",
       "1      0.055904  \n",
       "2      0.046466  \n",
       "3      0.061947  \n",
       "4      0.048515  \n",
       "5      0.040632  \n",
       "6      0.039828  \n",
       "7      0.047969  \n",
       "8      0.039644  \n",
       "9      0.046908  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(data_prepared, y_train, models, scale = 'MinMax')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcd1ff7",
   "metadata": {},
   "source": [
    "***Scoring Models with MinMaxScaler with SMOTE***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "022caf50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.785104</td>\n",
       "      <td>0.026753</td>\n",
       "      <td>0.783762</td>\n",
       "      <td>0.027475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.810601</td>\n",
       "      <td>0.034161</td>\n",
       "      <td>0.809841</td>\n",
       "      <td>0.034064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.846138</td>\n",
       "      <td>0.024678</td>\n",
       "      <td>0.844379</td>\n",
       "      <td>0.025790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.806080</td>\n",
       "      <td>0.032318</td>\n",
       "      <td>0.798189</td>\n",
       "      <td>0.034198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.834254</td>\n",
       "      <td>0.025703</td>\n",
       "      <td>0.833783</td>\n",
       "      <td>0.024486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.826964</td>\n",
       "      <td>0.029098</td>\n",
       "      <td>0.823673</td>\n",
       "      <td>0.029357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.848849</td>\n",
       "      <td>0.030432</td>\n",
       "      <td>0.842808</td>\n",
       "      <td>0.033815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.817882</td>\n",
       "      <td>0.039681</td>\n",
       "      <td>0.816507</td>\n",
       "      <td>0.039873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.765046</td>\n",
       "      <td>0.032393</td>\n",
       "      <td>0.768306</td>\n",
       "      <td>0.029077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.844320</td>\n",
       "      <td>0.026906</td>\n",
       "      <td>0.841647</td>\n",
       "      <td>0.030011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.785104      0.026753       0.783762   \n",
       "1          DecisionTreeClassifier       0.810601      0.034161       0.809841   \n",
       "2          RandomForestClassifier       0.846138      0.024678       0.844379   \n",
       "3                             SVC       0.806080      0.032318       0.798189   \n",
       "4            ExtraTreesClassifier       0.834254      0.025703       0.833783   \n",
       "5            KNeighborsClassifier       0.826964      0.029098       0.823673   \n",
       "6      GradientBoostingClassifier       0.848849      0.030432       0.842808   \n",
       "7              AdaBoostClassifier       0.817882      0.039681       0.816507   \n",
       "8                      GaussianNB       0.765046      0.032393       0.768306   \n",
       "9  BalancedRandomForestClassifier       0.844320      0.026906       0.841647   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.027475  \n",
       "1      0.034064  \n",
       "2      0.025790  \n",
       "3      0.034198  \n",
       "4      0.024486  \n",
       "5      0.029357  \n",
       "6      0.033815  \n",
       "7      0.039873  \n",
       "8      0.029077  \n",
       "9      0.030011  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "score_models(X_smote, y_smote, models, scale = 'MinMax')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8beb5f0",
   "metadata": {},
   "source": [
    "***Scoring Models with MinMaxScaler with SMOTETomek***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b220c3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.803397</td>\n",
       "      <td>0.039974</td>\n",
       "      <td>0.800989</td>\n",
       "      <td>0.042914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.846806</td>\n",
       "      <td>0.030793</td>\n",
       "      <td>0.847844</td>\n",
       "      <td>0.028746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.871598</td>\n",
       "      <td>0.028379</td>\n",
       "      <td>0.870252</td>\n",
       "      <td>0.030944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.824189</td>\n",
       "      <td>0.041971</td>\n",
       "      <td>0.815653</td>\n",
       "      <td>0.046990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.856785</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>0.857195</td>\n",
       "      <td>0.033312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.810348</td>\n",
       "      <td>0.034048</td>\n",
       "      <td>0.805508</td>\n",
       "      <td>0.035507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.864706</td>\n",
       "      <td>0.027821</td>\n",
       "      <td>0.861515</td>\n",
       "      <td>0.030334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.843914</td>\n",
       "      <td>0.028686</td>\n",
       "      <td>0.842802</td>\n",
       "      <td>0.028554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.790526</td>\n",
       "      <td>0.038196</td>\n",
       "      <td>0.791164</td>\n",
       "      <td>0.040187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.867628</td>\n",
       "      <td>0.031763</td>\n",
       "      <td>0.865534</td>\n",
       "      <td>0.035104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.803397      0.039974       0.800989   \n",
       "1          DecisionTreeClassifier       0.846806      0.030793       0.847844   \n",
       "2          RandomForestClassifier       0.871598      0.028379       0.870252   \n",
       "3                             SVC       0.824189      0.041971       0.815653   \n",
       "4            ExtraTreesClassifier       0.856785      0.032544       0.857195   \n",
       "5            KNeighborsClassifier       0.810348      0.034048       0.805508   \n",
       "6      GradientBoostingClassifier       0.864706      0.027821       0.861515   \n",
       "7              AdaBoostClassifier       0.843914      0.028686       0.842802   \n",
       "8                      GaussianNB       0.790526      0.038196       0.791164   \n",
       "9  BalancedRandomForestClassifier       0.867628      0.031763       0.865534   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.042914  \n",
       "1      0.028746  \n",
       "2      0.030944  \n",
       "3      0.046990  \n",
       "4      0.033312  \n",
       "5      0.035507  \n",
       "6      0.030334  \n",
       "7      0.028554  \n",
       "8      0.040187  \n",
       "9      0.035104  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(X_smotek, y_smotek, models, scale = 'MinMax')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c202985d",
   "metadata": {},
   "source": [
    "***Scoring Models with StandardScaler without resampling***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "99104654",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.799076</td>\n",
       "      <td>0.035815</td>\n",
       "      <td>0.731140</td>\n",
       "      <td>0.056581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.765331</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>0.701325</td>\n",
       "      <td>0.055559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.815918</td>\n",
       "      <td>0.025841</td>\n",
       "      <td>0.755319</td>\n",
       "      <td>0.044225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.810275</td>\n",
       "      <td>0.032184</td>\n",
       "      <td>0.720590</td>\n",
       "      <td>0.054237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.795693</td>\n",
       "      <td>0.033083</td>\n",
       "      <td>0.730129</td>\n",
       "      <td>0.048515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.803546</td>\n",
       "      <td>0.030485</td>\n",
       "      <td>0.733545</td>\n",
       "      <td>0.042208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.827116</td>\n",
       "      <td>0.023920</td>\n",
       "      <td>0.756547</td>\n",
       "      <td>0.039828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.805768</td>\n",
       "      <td>0.030879</td>\n",
       "      <td>0.739623</td>\n",
       "      <td>0.047969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.765431</td>\n",
       "      <td>0.038417</td>\n",
       "      <td>0.715620</td>\n",
       "      <td>0.039644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.802422</td>\n",
       "      <td>0.029201</td>\n",
       "      <td>0.751337</td>\n",
       "      <td>0.046309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.799076      0.035815       0.731140   \n",
       "1          DecisionTreeClassifier       0.765331      0.045112       0.701325   \n",
       "2          RandomForestClassifier       0.815918      0.025841       0.755319   \n",
       "3                             SVC       0.810275      0.032184       0.720590   \n",
       "4            ExtraTreesClassifier       0.795693      0.033083       0.730129   \n",
       "5            KNeighborsClassifier       0.803546      0.030485       0.733545   \n",
       "6      GradientBoostingClassifier       0.827116      0.023920       0.756547   \n",
       "7              AdaBoostClassifier       0.805768      0.030879       0.739623   \n",
       "8                      GaussianNB       0.765431      0.038417       0.715620   \n",
       "9  BalancedRandomForestClassifier       0.802422      0.029201       0.751337   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.056581  \n",
       "1      0.055559  \n",
       "2      0.044225  \n",
       "3      0.054237  \n",
       "4      0.048515  \n",
       "5      0.042208  \n",
       "6      0.039828  \n",
       "7      0.047969  \n",
       "8      0.039644  \n",
       "9      0.046309  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(data_prepared, y_train, models, scale = 'Standard')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72fb26a0",
   "metadata": {},
   "source": [
    "***Scoring Models with StandardScaler and SMOTE***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0dc4106b",
   "metadata": {},
   "outputs": [
    {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.786931</td>\n",
       "      <td>0.029668</td>\n",
       "      <td>0.783851</td>\n",
       "      <td>0.031631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.811510</td>\n",
       "      <td>0.033987</td>\n",
       "      <td>0.810585</td>\n",
       "      <td>0.033840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.846138</td>\n",
       "      <td>0.025011</td>\n",
       "      <td>0.843963</td>\n",
       "      <td>0.027029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.826080</td>\n",
       "      <td>0.028539</td>\n",
       "      <td>0.820868</td>\n",
       "      <td>0.031379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.834254</td>\n",
       "      <td>0.025703</td>\n",
       "      <td>0.833783</td>\n",
       "      <td>0.024486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.829691</td>\n",
       "      <td>0.035625</td>\n",
       "      <td>0.830007</td>\n",
       "      <td>0.035721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.848849</td>\n",
       "      <td>0.030432</td>\n",
       "      <td>0.842808</td>\n",
       "      <td>0.033815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.817882</td>\n",
       "      <td>0.039681</td>\n",
       "      <td>0.816507</td>\n",
       "      <td>0.039873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.765046</td>\n",
       "      <td>0.032393</td>\n",
       "      <td>0.768306</td>\n",
       "      <td>0.029077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.847048</td>\n",
       "      <td>0.026102</td>\n",
       "      <td>0.844948</td>\n",
       "      <td>0.028710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.786931      0.029668       0.783851   \n",
       "1          DecisionTreeClassifier       0.811510      0.033987       0.810585   \n",
       "2          RandomForestClassifier       0.846138      0.025011       0.843963   \n",
       "3                             SVC       0.826080      0.028539       0.820868   \n",
       "4            ExtraTreesClassifier       0.834254      0.025703       0.833783   \n",
       "5            KNeighborsClassifier       0.829691      0.035625       0.830007   \n",
       "6      GradientBoostingClassifier       0.848849      0.030432       0.842808   \n",
       "7              AdaBoostClassifier       0.817882      0.039681       0.816507   \n",
       "8                      GaussianNB       0.765046      0.032393       0.768306   \n",
       "9  BalancedRandomForestClassifier       0.847048      0.026102       0.844948   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.031631  \n",
       "1      0.033840  \n",
       "2      0.027029  \n",
       "3      0.031379  \n",
       "4      0.024486  \n",
       "5      0.035721  \n",
       "6      0.033815  \n",
       "7      0.039873  \n",
       "8      0.029077  \n",
       "9      0.028710  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(X_smote, y_smote, models, scale = 'Standard')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860b5795",
   "metadata": {},
   "source": [
    "***Scoring Models with StandardScaler and SMOTETomek***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ba581227",
   "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>Model</th>\n",
       "      <th>Accuracy Mean</th>\n",
       "      <th>Accuracy STD</th>\n",
       "      <th>F1 Score Mean</th>\n",
       "      <th>F1 Score STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.810328</td>\n",
       "      <td>0.039469</td>\n",
       "      <td>0.807013</td>\n",
       "      <td>0.042711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.848787</td>\n",
       "      <td>0.028733</td>\n",
       "      <td>0.849373</td>\n",
       "      <td>0.027709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.874568</td>\n",
       "      <td>0.027307</td>\n",
       "      <td>0.873096</td>\n",
       "      <td>0.029990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.828140</td>\n",
       "      <td>0.041173</td>\n",
       "      <td>0.820838</td>\n",
       "      <td>0.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.856785</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>0.857195</td>\n",
       "      <td>0.033312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.816308</td>\n",
       "      <td>0.035341</td>\n",
       "      <td>0.813652</td>\n",
       "      <td>0.035573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.865696</td>\n",
       "      <td>0.027862</td>\n",
       "      <td>0.862369</td>\n",
       "      <td>0.030476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.843914</td>\n",
       "      <td>0.028686</td>\n",
       "      <td>0.842802</td>\n",
       "      <td>0.028554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.790526</td>\n",
       "      <td>0.038196</td>\n",
       "      <td>0.791164</td>\n",
       "      <td>0.040187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>0.870598</td>\n",
       "      <td>0.029576</td>\n",
       "      <td>0.868734</td>\n",
       "      <td>0.032730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model  Accuracy Mean  Accuracy STD  F1 Score Mean  \\\n",
       "0              LogisticRegression       0.810328      0.039469       0.807013   \n",
       "1          DecisionTreeClassifier       0.848787      0.028733       0.849373   \n",
       "2          RandomForestClassifier       0.874568      0.027307       0.873096   \n",
       "3                             SVC       0.828140      0.041173       0.820838   \n",
       "4            ExtraTreesClassifier       0.856785      0.032544       0.857195   \n",
       "5            KNeighborsClassifier       0.816308      0.035341       0.813652   \n",
       "6      GradientBoostingClassifier       0.865696      0.027862       0.862369   \n",
       "7              AdaBoostClassifier       0.843914      0.028686       0.842802   \n",
       "8                      GaussianNB       0.790526      0.038196       0.791164   \n",
       "9  BalancedRandomForestClassifier       0.870598      0.029576       0.868734   \n",
       "\n",
       "   F1 Score STD  \n",
       "0      0.042711  \n",
       "1      0.027709  \n",
       "2      0.029990  \n",
       "3      0.045000  \n",
       "4      0.033312  \n",
       "5      0.035573  \n",
       "6      0.030476  \n",
       "7      0.028554  \n",
       "8      0.040187  \n",
       "9      0.032730  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_models(X_smotek, y_smotek, models, scale = 'Standard')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5207e7e",
   "metadata": {},
   "source": [
    "- Apart from GradientBoosting, overall SMOTETomek gives better accuracy and F1 Score with MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4ebfe42a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets take a look on the scores for ensemble as well\n",
    "\n",
    "#LR + RF + SVC\n",
    "#EX + KN + GB\n",
    "#AB + SVC + NB\n",
    "#KN + AB + GB\n",
    "#KN + SVC + GB + RF + AB\n",
    "\n",
    "# def create_ensembles():\n",
    "#     ensembles = []\n",
    "#     ensembles.append(('VC1', VotingClassifier([('LR', LogisticRegression()), ('RF', RandomForestClassifier()), ('SV', SVC())], \n",
    "#                                              voting='hard')))\n",
    "#     ensembles.append(('VC2', VotingClassifier([('EX', ExtraTreesClassifier()), ('KN', KNeighborsClassifier()), ('GB', GradientBoostingClassifier())], \n",
    "#                                              voting='hard')))\n",
    "#     ensembles.append(('VC3', VotingClassifier([('AB', AdaBoostClassifier()), ('SV', SVC()), ('NB', GaussianNB())], \n",
    "#                                              voting='hard')))\n",
    "#     ensembles.append(('VC4', VotingClassifier([('KN', KNeighborsClassifier()), ('AB', AdaBoostClassifier()), ('GB', GradientBoostingClassifier())], \n",
    "#                                              voting='hard')))\n",
    "#     ensembles.append(('VC5', VotingClassifier([('KN', KNeighborsClassifier()), ('SVC', SVC()), ('GB', GradientBoostingClassifier()), ('RF', RandomForestClassifier()),\n",
    "#                                               ('AB', AdaBoostClassifier())], voting='hard')))\n",
    "#     return ensembles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0965f1d",
   "metadata": {},
   "source": [
    "***Ensemble with default scaling***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "25540dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ensemble_list = create_ensembles()\n",
    "# score_models(data_prepared, y_train, ensemble_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec25312e",
   "metadata": {},
   "source": [
    "***Ensemble with scaled data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "00b9b739",
   "metadata": {},
   "outputs": [],
   "source": [
    "# score_models(data_prepared, y_train, ensemble_list, scale = 'MinMax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "50622efb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# score_models(data_prepared, y_train, ensemble_list, scale = 'Standard')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1517437a",
   "metadata": {},
   "source": [
    "- Lets scale the date with MinMaxScaler with SMOTETomek\n",
    "- Promising algorithms:\n",
    "    * GradientBoostingClassfier\n",
    "    * RandomForestClassifier\n",
    "    * AdaBoostClassfier\n",
    "    * SVC\n",
    "    * KNeighborsClassifier\n",
    "    * BalancedBaggingClassifier\n",
    "    * VotingClassifier(with the above models)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53110aad",
   "metadata": {},
   "source": [
    "# 4. Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c6208f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_smotek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0ead4ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "\n",
    "gb = GradientBoostingClassifier(random_state=seed)\n",
    "models.append(gb)\n",
    "\n",
    "rf = RandomForestClassifier(random_state=seed)\n",
    "models.append(rf)\n",
    "\n",
    "ab = AdaBoostClassifier(random_state=seed)\n",
    "models.append(ab)\n",
    "\n",
    "sv = SVC(random_state=seed)\n",
    "models.append(sv)\n",
    "\n",
    "kn = KNeighborsClassifier()\n",
    "models.append(kn)\n",
    "\n",
    "brf = BalancedRandomForestClassifier(random_state=seed)\n",
    "models.append(brf)\n",
    "\n",
    "\n",
    "param_grid = [\n",
    "    \n",
    "    {\n",
    "        'loss': ['log_loss', 'exponential'],\n",
    "        'n_estimators': [100, 200, 400, 500, 600],\n",
    "        'max_features': [2, 3, 4],\n",
    "        'learning_rate': [0.1, 0.5, 1]\n",
    "    },\n",
    "    {\n",
    "        'n_estimators': [100, 200, 400, 500, 600],\n",
    "        'max_features': [2, 3, 4],\n",
    "        'criterion': ['gini', 'entropy']\n",
    "    },\n",
    "    {\n",
    "        'n_estimators': [100, 200, 400, 500, 600],\n",
    "        'learning_rate': [0.1, 0.5, 1]\n",
    "    },\n",
    "    {\n",
    "        'C': [1, 1.5, 2.0],\n",
    "        'kernel': ['linear', 'poly', 'rbf']\n",
    "    },\n",
    "    {\n",
    "        'n_neighbors': [5, 8, 10],\n",
    "        'weights': ['uniform', 'distance']\n",
    "    },\n",
    "    {\n",
    "        'n_estimators': [100, 200, 400, 500, 600],\n",
    "        'criterion': ['gini', 'entropy'],\n",
    "        'max_features': ['auto', 'sqrt', 'log2']\n",
    "    }\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "abb89171",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "32cea915",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bc7a3348",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3d02160a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{GradientBoostingClassifier(random_state=42): {'loss': ['log_loss',\n",
       "   'exponential'],\n",
       "  'n_estimators': [100, 200, 400, 500, 600],\n",
       "  'max_features': [2, 3, 4],\n",
       "  'learning_rate': [0.1, 0.5, 1]},\n",
       " RandomForestClassifier(random_state=42): {'n_estimators': [100,\n",
       "   200,\n",
       "   400,\n",
       "   500,\n",
       "   600],\n",
       "  'max_features': [2, 3, 4],\n",
       "  'criterion': ['gini', 'entropy']},\n",
       " AdaBoostClassifier(random_state=42): {'n_estimators': [100,\n",
       "   200,\n",
       "   400,\n",
       "   500,\n",
       "   600],\n",
       "  'learning_rate': [0.1, 0.5, 1]},\n",
       " SVC(random_state=42): {'C': [1, 1.5, 2.0],\n",
       "  'kernel': ['linear', 'poly', 'rbf']},\n",
       " KNeighborsClassifier(): {'n_neighbors': [5, 8, 10],\n",
       "  'weights': ['uniform', 'distance']},\n",
       " BalancedRandomForestClassifier(random_state=42): {'n_estimators': [100,\n",
       "   200,\n",
       "   400,\n",
       "   500,\n",
       "   600],\n",
       "  'criterion': ['gini', 'entropy'],\n",
       "  'max_features': ['auto', 'sqrt', 'log2']}}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_param_map = dict(zip(models, param_grid))\n",
    "model_param_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4175a14f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier(learning_rate=0.5, max_features=4, random_state=42)\n",
      "0.868653367799834\n",
      "RandomForestClassifier(criterion='entropy', max_features=4, n_estimators=400,\n",
      "                       random_state=42)\n",
      "0.8775447495488464\n",
      "AdaBoostClassifier(learning_rate=1, n_estimators=500, random_state=42)\n",
      "0.8656489294249623\n",
      "SVC(C=1, random_state=42)\n",
      "0.8162171389552748\n",
      "KNeighborsClassifier(weights='distance')\n",
      "0.8271472467443788\n",
      "BalancedRandomForestClassifier(max_features='log2', n_estimators=400,\n",
      "                               random_state=42)\n",
      "0.8745744525191436\n"
     ]
    }
   ],
   "source": [
    "best_estimator_list = []\n",
    "best_score_list = []\n",
    "for best in model_param_map.items():\n",
    "    rand_search = RandomizedSearchCV(best[0], best[1], scoring='accuracy', cv=5, random_state=seed)\n",
    "    rand_search.fit(X_train_scaled, y_smotek)\n",
    "    best_estimator_list.append(rand_search.best_estimator_)\n",
    "    print(rand_search.best_estimator_)\n",
    "    best_score_list.append(rand_search.best_score_)\n",
    "    print(rand_search.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4c26f3db",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_models = []\n",
    "best_gb = GradientBoostingClassifier(loss='exponential', max_features=3, n_estimators=400,\n",
    "                           random_state=42)\n",
    "best_models.append(best_gb)\n",
    "best_rf = RandomForestClassifier(criterion='entropy', max_features=4, n_estimators=400,\n",
    "                       random_state=42)\n",
    "best_models.append(best_rf)\n",
    "best_ab = AdaBoostClassifier(learning_rate=1, n_estimators=500, random_state=42)\n",
    "best_models.append(best_ab)\n",
    "best_svc = SVC(C=2.0, kernel='poly', random_state=42)\n",
    "best_models.append(best_svc)\n",
    "best_kn = KNeighborsClassifier(n_neighbors=10, weights='distance')\n",
    "best_models.append(best_kn)\n",
    "best_brf = BalancedRandomForestClassifier(criterion='entropy', n_estimators=600,random_state=42)\n",
    "best_models.append(best_brf)\n",
    "\n",
    "best_vc = VotingClassifier([('best_gb', best_gb),\n",
    "                           ('best_rf', best_rf),\n",
    "                           ('best_ab', best_ab),\n",
    "                           ('best_svc', best_svc),\n",
    "                           ('best_kn', best_kn)])\n",
    "best_models.append(best_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "eb21de0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_model_name(model):\n",
    "#     temp = str(model)\n",
    "#     end = temp.index('(')\n",
    "#     title = temp[0 : end]\n",
    "#     return title\n",
    "\n",
    "def get_confusion_matrix(models, X_train, y_train):\n",
    "    plt.figure(figsize=(20,16))\n",
    "    kfold = StratifiedKFold(shuffle=True, random_state=42)\n",
    "    for idx, model in enumerate(models):\n",
    "        f = plt.subplot(6, 6, idx+1)\n",
    "        y_train_pred = cross_val_predict(model, X_train, y_train, cv=kfold)\n",
    "        conf_mx = confusion_matrix(y_train, y_train_pred)\n",
    "        title = model.__class__.__name__\n",
    "        f.set_title(title)\n",
    "        g = sns.heatmap(conf_mx)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "\n",
    "def get_classification_scores(models, X_train, y_train):\n",
    "    model_names = []\n",
    "    accuracy_scores = []\n",
    "    recall_scores = []\n",
    "    precision_scores = []\n",
    "    f1_scores = []\n",
    "    roc_auc_scores = []\n",
    "    kfold = StratifiedKFold(shuffle=True, random_state=42)\n",
    "    for model in models:\n",
    "        model_names.append(model.__class__.__name__)\n",
    "        y_train_pred = cross_val_predict(model, X_train, y_train, cv=kfold)\n",
    "        accuracy_scores.append(round(accuracy_score(y_train, y_train_pred) * 100, 2).astype('str') + '%')\n",
    "        recall_scores.append(round(recall_score(y_train, y_train_pred) * 100, 2).astype('str') + '%')\n",
    "        precision_scores.append(round(precision_score(y_train, y_train_pred) * 100, 2).astype('str') + '%')\n",
    "        f1_scores.append(round(f1_score(y_train, y_train_pred) * 100, 2).astype('str') + '%')\n",
    "        roc_auc_scores.append(round(roc_auc_score(y_train, y_train_pred) * 100, 2).astype('str') + '%')\n",
    "    \n",
    "    metrics = list(zip(model_names, accuracy_scores, recall_scores, precision_scores, f1_scores, roc_auc_scores))\n",
    "    df = pd.DataFrame(data=metrics, columns = ['Estimators', 'Accuracy', 'Recall', 'Precision', 'F1 Score', 'ROC-AUC-Score'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7286e677",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1152 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(best_models, X_train_scaled, y_smotek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b960e314",
   "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>Estimators</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>ROC-AUC-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>86.76%</td>\n",
       "      <td>83.6%</td>\n",
       "      <td>89.24%</td>\n",
       "      <td>86.33%</td>\n",
       "      <td>86.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>87.25%</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>88.08%</td>\n",
       "      <td>87.11%</td>\n",
       "      <td>87.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>85.97%</td>\n",
       "      <td>85.38%</td>\n",
       "      <td>86.4%</td>\n",
       "      <td>85.88%</td>\n",
       "      <td>85.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SVC</td>\n",
       "      <td>81.32%</td>\n",
       "      <td>75.69%</td>\n",
       "      <td>85.3%</td>\n",
       "      <td>80.21%</td>\n",
       "      <td>81.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>83.99%</td>\n",
       "      <td>83.2%</td>\n",
       "      <td>84.54%</td>\n",
       "      <td>83.86%</td>\n",
       "      <td>83.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BalancedRandomForestClassifier</td>\n",
       "      <td>86.96%</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>87.55%</td>\n",
       "      <td>86.85%</td>\n",
       "      <td>86.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VotingClassifier</td>\n",
       "      <td>86.86%</td>\n",
       "      <td>84.58%</td>\n",
       "      <td>88.61%</td>\n",
       "      <td>86.55%</td>\n",
       "      <td>86.86%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Estimators Accuracy  Recall Precision F1 Score  \\\n",
       "0      GradientBoostingClassifier   86.76%   83.6%    89.24%   86.33%   \n",
       "1          RandomForestClassifier   87.25%  86.17%    88.08%   87.11%   \n",
       "2              AdaBoostClassifier   85.97%  85.38%     86.4%   85.88%   \n",
       "3                             SVC   81.32%  75.69%     85.3%   80.21%   \n",
       "4            KNeighborsClassifier   83.99%   83.2%    84.54%   83.86%   \n",
       "5  BalancedRandomForestClassifier   86.96%  86.17%    87.55%   86.85%   \n",
       "6                VotingClassifier   86.86%  84.58%    88.61%   86.55%   \n",
       "\n",
       "  ROC-AUC-Score  \n",
       "0        86.76%  \n",
       "1        87.25%  \n",
       "2        85.97%  \n",
       "3        81.32%  \n",
       "4        83.99%  \n",
       "5        86.96%  \n",
       "6        86.86%  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_classification_scores(best_models, X_train_scaled, y_smotek)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708b7e8f",
   "metadata": {},
   "source": [
    "***RandomForestClassifier gives better metrics***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fccc5a02",
   "metadata": {},
   "source": [
    "# 5. Model Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e2bc0365",
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_pipeline = Pipeline([('preproc', data_pipeline),\n",
    "#                           ('scaler', MinMaxScaler()),\n",
    "#                           ('best_gb', GradientBoostingClassifier(loss='exponential', max_features=4, n_estimators=200))])\n",
    "\n",
    "\n",
    "# final_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "871ce3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preproc&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer2&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;imputer1&#x27;,\n",
       "                                                                   SimpleImputer(missing_values=0))]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;Fare&#x27;]),\n",
       "                                                 (&#x27;clean&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                                                   CategoryTransformer())]),\n",
       "                                                  [&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;,\n",
       "                                                   &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;,\n",
       "                                                   &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;,\n",
       "                                                   &#x27;Fare&#x27;, &#x27;Cabin&#x27;,\n",
       "                                                   &#x27;Embarked&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline...\n",
       "                                                                          max_features=3,\n",
       "                                                                          n_estimators=400,\n",
       "                                                                          random_state=42)),\n",
       "                                              (&#x27;best_rf&#x27;,\n",
       "                                               RandomForestClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                                      max_features=4,\n",
       "                                                                      n_estimators=400,\n",
       "                                                                      random_state=42)),\n",
       "                                              (&#x27;best_ab&#x27;,\n",
       "                                               AdaBoostClassifier(learning_rate=1,\n",
       "                                                                  n_estimators=500,\n",
       "                                                                  random_state=42)),\n",
       "                                              (&#x27;best_svc&#x27;,\n",
       "                                               SVC(C=2.0, kernel=&#x27;poly&#x27;,\n",
       "                                                   random_state=42)),\n",
       "                                              (&#x27;best_kn&#x27;,\n",
       "                                               KNeighborsClassifier(n_neighbors=10,\n",
       "                                                                    weights=&#x27;distance&#x27;))]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preproc&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer2&#x27;,\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  (&#x27;imputer1&#x27;,\n",
       "                                                                   SimpleImputer(missing_values=0))]),\n",
       "                                                  [&#x27;Age&#x27;, &#x27;Fare&#x27;]),\n",
       "                                                 (&#x27;clean&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                                                   CategoryTransformer())]),\n",
       "                                                  [&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;,\n",
       "                                                   &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;,\n",
       "                                                   &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;,\n",
       "                                                   &#x27;Fare&#x27;, &#x27;Cabin&#x27;,\n",
       "                                                   &#x27;Embarked&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline...\n",
       "                                                                          max_features=3,\n",
       "                                                                          n_estimators=400,\n",
       "                                                                          random_state=42)),\n",
       "                                              (&#x27;best_rf&#x27;,\n",
       "                                               RandomForestClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                                      max_features=4,\n",
       "                                                                      n_estimators=400,\n",
       "                                                                      random_state=42)),\n",
       "                                              (&#x27;best_ab&#x27;,\n",
       "                                               AdaBoostClassifier(learning_rate=1,\n",
       "                                                                  n_estimators=500,\n",
       "                                                                  random_state=42)),\n",
       "                                              (&#x27;best_svc&#x27;,\n",
       "                                               SVC(C=2.0, kernel=&#x27;poly&#x27;,\n",
       "                                                   random_state=42)),\n",
       "                                              (&#x27;best_kn&#x27;,\n",
       "                                               KNeighborsClassifier(n_neighbors=10,\n",
       "                                                                    weights=&#x27;distance&#x27;))]))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preproc: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer2&#x27;, SimpleImputer()),\n",
       "                                                 (&#x27;imputer1&#x27;,\n",
       "                                                  SimpleImputer(missing_values=0))]),\n",
       "                                 [&#x27;Age&#x27;, &#x27;Fare&#x27;]),\n",
       "                                (&#x27;clean&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;transformer&#x27;,\n",
       "                                                  CategoryTransformer())]),\n",
       "                                 [&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;, &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;,\n",
       "                                  &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;, &#x27;Fare&#x27;, &#x27;Cabin&#x27;,\n",
       "                                  &#x27;Embarked&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer3&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;encoder&#x27;,\n",
       "                                                  OneHotEncoder(dtype=&#x27;int&#x27;))]),\n",
       "                                 [&#x27;Embarked&#x27;, &#x27;Sex&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;Fare&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(missing_values=0)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">clean</label><div class=\"sk-toggleable__content\"><pre>[&#x27;PassengerId&#x27;, &#x27;Pclass&#x27;, &#x27;Name&#x27;, &#x27;Sex&#x27;, &#x27;Age&#x27;, &#x27;SibSp&#x27;, &#x27;Parch&#x27;, &#x27;Ticket&#x27;, &#x27;Fare&#x27;, &#x27;Cabin&#x27;, &#x27;Embarked&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CategoryTransformer</label><div class=\"sk-toggleable__content\"><pre>CategoryTransformer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Embarked&#x27;, &#x27;Sex&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(dtype=&#x27;int&#x27;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SMOTETomek</label><div class=\"sk-toggleable__content\"><pre>SMOTETomek()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">best_vc: VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;best_gb&#x27;,\n",
       "                              GradientBoostingClassifier(loss=&#x27;exponential&#x27;,\n",
       "                                                         max_features=3,\n",
       "                                                         n_estimators=400,\n",
       "                                                         random_state=42)),\n",
       "                             (&#x27;best_rf&#x27;,\n",
       "                              RandomForestClassifier(criterion=&#x27;entropy&#x27;,\n",
       "                                                     max_features=4,\n",
       "                                                     n_estimators=400,\n",
       "                                                     random_state=42)),\n",
       "                             (&#x27;best_ab&#x27;,\n",
       "                              AdaBoostClassifier(learning_rate=1,\n",
       "                                                 n_estimators=500,\n",
       "                                                 random_state=42)),\n",
       "                             (&#x27;best_svc&#x27;,\n",
       "                              SVC(C=2.0, kernel=&#x27;poly&#x27;, random_state=42)),\n",
       "                             (&#x27;best_kn&#x27;,\n",
       "                              KNeighborsClassifier(n_neighbors=10,\n",
       "                                                   weights=&#x27;distance&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>best_gb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(loss=&#x27;exponential&#x27;, max_features=3, n_estimators=400,\n",
       "                           random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>best_rf</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_features=4, n_estimators=400,\n",
       "                       random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>best_ab</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(learning_rate=1, n_estimators=500, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>best_svc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=2.0, kernel=&#x27;poly&#x27;, random_state=42)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>best_kn</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=10, weights=&#x27;distance&#x27;)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preproc',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer2',\n",
       "                                                                   SimpleImputer()),\n",
       "                                                                  ('imputer1',\n",
       "                                                                   SimpleImputer(missing_values=0))]),\n",
       "                                                  ['Age', 'Fare']),\n",
       "                                                 ('clean',\n",
       "                                                  Pipeline(steps=[('transformer',\n",
       "                                                                   CategoryTransformer())]),\n",
       "                                                  ['PassengerId', 'Pclass',\n",
       "                                                   'Name', 'Sex', 'Age',\n",
       "                                                   'SibSp', 'Parch', 'Ticket',\n",
       "                                                   'Fare', 'Cabin',\n",
       "                                                   'Embarked']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline...\n",
       "                                                                          max_features=3,\n",
       "                                                                          n_estimators=400,\n",
       "                                                                          random_state=42)),\n",
       "                                              ('best_rf',\n",
       "                                               RandomForestClassifier(criterion='entropy',\n",
       "                                                                      max_features=4,\n",
       "                                                                      n_estimators=400,\n",
       "                                                                      random_state=42)),\n",
       "                                              ('best_ab',\n",
       "                                               AdaBoostClassifier(learning_rate=1,\n",
       "                                                                  n_estimators=500,\n",
       "                                                                  random_state=42)),\n",
       "                                              ('best_svc',\n",
       "                                               SVC(C=2.0, kernel='poly',\n",
       "                                                   random_state=42)),\n",
       "                                              ('best_kn',\n",
       "                                               KNeighborsClassifier(n_neighbors=10,\n",
       "                                                                    weights='distance'))]))])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pipeline = Pipeline([('preproc', data_pipeline),\n",
    "                           ('sampling', smotek),\n",
    "                           ('scaler', MinMaxScaler()),\n",
    "                           ('best_vc', best_vc)])\n",
    "\n",
    "final_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d8e561bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pipeline.fit(train_out, y_train)\n",
    "predictions = final_pipeline.predict(test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c18fe561",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>1224</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>1113</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>1129</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1016</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1029</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "332         1224         0\n",
       "221         1113         0\n",
       "237         1129         0\n",
       "210         1102         0\n",
       "124         1016         0\n",
       "2            894         0\n",
       "19           911         1\n",
       "247         1139         0\n",
       "137         1029         0\n",
       "82           974         0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame({'PassengerId':test['PassengerId'],'Survived':predictions})\n",
    "\n",
    "submission.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "543b739d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved file: Titanic_Survival_Predictions.csv\n"
     ]
    }
   ],
   "source": [
    "filename = 'Titanic_Survival_Predictions.csv'\n",
    "\n",
    "submission.to_csv(filename,index=False)\n",
    "\n",
    "print('Saved file: ' + filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d9a66f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c40bde0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['predict_survivor_V2.pkl']"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(final_pipeline, 'predict_survivor_V2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f11e8fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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