Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</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>13.00</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>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>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</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>23.45</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>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>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</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>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()\n",
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n",
      "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
      "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train.columns)\n",
    "print(test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "print(len(train.columns))\n",
    "print(len(test.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age             True\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Embarked'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_backup = train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_backup.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed3321d30>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed34236a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed34ac470>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking using heatmap if there is any null data\n",
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed3551eb8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "##EDA using seaborn on other variables\n",
    "\n",
    "sns.countplot(x='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed352a908>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed341ae10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Looks like few people survived, we will gather more info\n",
    "\n",
    "sns.countplot(x='Survived', data=train, hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed365b1d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Looks like female survived are more, we will gather more info\n",
    "\n",
    "sns.countplot(x='Survived', data=train, hue='Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(train['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed36e1438>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "714"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train['Age'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed37bc5c0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(), bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed386ca58>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(), kde=True, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed3934828>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(), kde=False, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed39bbba8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed3a0c5c0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(bins=40, figsize=(10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed34b02b0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x='Age', y='Pclass', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed52e8898>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x='Pclass', y='Age', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(train['Age']).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "886    27.0\n",
       "887    19.0\n",
       "888     NaN\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_values_fill(cols):\n",
    "    \n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        \n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "        \n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        \n",
    "        else:\n",
    "            return 24\n",
    "        \n",
    "    else:\n",
    "        return Age\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Applying Func on Age Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train['Age'] = train[['Age', 'Pclass']].apply(age_values_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ##What happened above is we didn't provide the axix column and all the rows got updated to Null Values and later we applied the func on the column this changed the coulmn to only 3 values as seen below\n",
    "\n",
    "# train['Age'].nunique()\n",
    "# train['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age', 'Pclass']].apply(age_values_fill, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "886    27.0\n",
       "887    19.0\n",
       "888    24.0\n",
       "889    26.0\n",
       "890    32.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed55dd0f0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Now checking the same on heatmap\n",
    "\n",
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Can see values of Age are not any more null, cabin and embarked now \n",
    "train.drop('Cabin', axis = 1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed5765cf8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed55133c8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVYAAAEnCAYAAADy0WvnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deVRTZ/4G8CcYAh4IClJqreKOVStHcRtrsVXHLo5j1WpZLI671aoVUHEr4oZWBJdTtaN1wyogjrajtNrRUajaFteiVrSglVo3FBcShAi5vz/4JQNaLZBvNMLzOSfnkNz4vTcID+997/u+V6UoigIiIhJj97QPgIiosmGwEhEJY7ASEQljsBIRCWOwEhEJY7ASEQlTP25jo2XRT+o4iOgZd358qMU1jFe9yvxeu9rnLN6ftTw2WImIniQjjGV+ry2fbjNYichmFCllD1ZbDi9bPjYiqmKMqBwTQRmsRGQzytMVYMsYrERkM4oqydIlDFYishn32WIlIpLFPlYiImHsCiAiElY5OgIYrERkQ4rYFUBEJKuocuQqg5WIbMd9qJ72IYhgsBKRzTCyxUpEJKuILVYiIlkMViIiYUaFwUpEJMqAak/7EEQwWInIZki1WI1GIyIiInD27FloNBrMnTsX9evXN2/fsmUL4uPjoVarMXr0aHTt2hWXL1/G5MmToSgKatSogejoaFSvXr1C+7flRbiJqIopgqrMj8fZs2cPDAYDEhISEBoaigULFpi3ZWdnY+PGjYiPj8eaNWsQExMDg8GA9evX4+2338amTZvQtGlTbN26tcKfg8FKRDajSLEr8+Nxjh49Cl9fXwBA69atcerUKfO2tLQ0tGnTBhqNBlqtFp6enkhPT0fz5s1x9+5dAIBOp4NaXfETenYFEJHNMAq19XQ6HZydnc3Pq1WrhsLCQqjVauh0Omi1WvM2Jycn6HQ61K5dG9HR0di5cycMBgPGjh1b4f0zWInIZpRnuFVCQgISEhLMz/38/ODn5wcAcHZ2hl6vN28zGo3mFuiD2/R6PbRaLcLDwzF//nz4+vpi//79CAsLw6pVqyr0ORisRGQz7itlHxVQMkgf5OPjg3379qFnz544ceIEvLz+d1ttb29vLFmyBAUFBTAYDMjMzISXlxdcXFzMLVkPDw9zt0BFMFiJyGYUCXUF9OjRAwcPHoS/vz8URUFkZCTWrVsHT09PdO/eHUFBQQgMDISiKAgODoaDgwM+/vhjzJ49G0ajEYqiIDw8vML7VynKo1eWbbQsusKFiahqOT8+1OIaO857l/m9f2+UZvH+rIUtViKyGVIXr542BisR2YwiTmklIpIl1cf6tDFYichm3FcqRyRVjk9BRJUCuwKIiITx4hURkbA/WwPgWcFgJSKbYeQdBIiIZBl48YqISBZvzUJEJIzjWImIhBl58YqISBZvf01EJIwtViIiYeVZ6NqWMViJyGZwggARkTBOECAiEsYWKxGRME4QICISxotXRETCuGwgEZEwLnRNRCSMfaxERMI484qISBjXCiAiElZo5KgAIiJRnHlFRCSMowKIiITx4hURkTAOtyIiEsY+ViIiYRwVQEQkjF0BRETC2BVARCSMLVYiImGVJVgrx6AxIqoUCo12ZX48jtFoRHh4OPz8/BAUFISLFy+W2r5lyxb069cP7733Hvbt21dq2+HDh/Haa69Z9DnYYiUimyHVx7pnzx4YDAYkJCTgxIkTWLBgAVauXAkAyM7OxsaNG/Gvf/0LBQUFCAwMROfOnaHRaHDlyhWsXbsWhYWFFu2fLVYishlGRVXmx+McPXoUvr6+AIDWrVvj1KlT5m1paWlo06YNNBoNtFotPD09kZ6ejoKCAsycORMREREWfw62WInIZpSnjzUhIQEJCQnm535+fvDz8wMA6HQ6ODs7m7dVq1YNhYWFUKvV0Ol00Gq15m1OTk7Q6XSYPXs2hg4diueff97iz8FgJSKbUZ5gLRmkD3J2doZer/9fXaMRarX6D7fp9XrY29vjyJEjyMrKwvLly3Hnzh0EBwdj8eLFFfocDFYishlSowJ8fHywb98+9OzZEydOnICXl5d5m7e3N5YsWYKCggIYDAZkZmbC29sbu3fvNr+nc+fOFQ5VgMFKRDakSGh1qx49euDgwYPw9/eHoiiIjIzEunXr4Onpie7duyMoKAiBgYFQFAXBwcFwcHAQ2a+JSlEU5VEbGy2LFt0ZEVVe58eHWlzj1T2Ty/zeA39daPH+rIUtViKyGUolmSDAYCUim1FZZl4xWInIZrDFSkQkjC1WIiJhvJkgEZEwdgUQEQljVwARkbBHj6p/tjBYichmsCuAiEhY0Z8sYP2sYLASkc1gVwARkTB2BRARCWOwEhEJqyQ9AQxWIrIdbLESEQlTjAxWIiJRHBVARCSMXQFERNIYrEREstgVQEQkjcFKRCSLowKIiITx4hURkTR2BRARSWOLlYhIFlusRETCePGKiEgWx7ESEUljsBIRCeNwKyIiWSq2WImIhDFYiYiEcVQAEZEwtliJiIQxWImIhAmNCjAajYiIiMDZs2eh0Wgwd+5c1K9f37x9y5YtiI+Ph1qtxujRo9G1a1fk5ORg4sSJyM/Ph4eHB+bPn4/q1atXaP92Ip+CiEiASin743H27NkDg8GAhIQEhIaGYsGCBeZt2dnZ2LhxI+Lj47FmzRrExMTAYDBgxYoV6NWrFzZv3owWLVogISGhwp+DwUpEtkMpx+Mxjh49Cl9fXwBA69atcerUKfO2tLQ0tGnTBhqNBlqtFp6enkhPTy/1b7p06YJDhw5V+GOwK4CIbIbUOFadTgdnZ2fz82rVqqGwsBBqtRo6nQ5arda8zcnJCTqdrtTrTk5OyM3NrfD+GaxEZDvK0ceakJBQ6nTdz88Pfn5+AABnZ2fo9XrzNqPRCLVa/Yfb9Ho9tFqt+XVHR0fo9Xq4uLhU+GMwWInIdpSjxVoySB/k4+ODffv2oWfPnjhx4gS8vLzM27y9vbFkyRIUFBTAYDAgMzMTXl5e8PHxQXJyMvr164eUlBS0bdu2wh+DwUpEtkOoK6BHjx44ePAg/P39oSgKIiMjsW7dOnh6eqJ79+4ICgpCYGAgFEVBcHAwHBwcMHr0aISFhWHLli1wdXVFdHR0hfevUpRHL9TVaFnFCxNR1XJ+fKjFNRrHxJT5vZkhIRbvz1rYYiUi22F82gcgg8FKRDaDq1sREUnjeqxERMLYYiUiksWuACIiaQxWIiJZKo4KICISxhYrEZGsytLHymUDiYiEscVKRLajkrRYGaxEZDN48YqISBpbrEREsirLxSsGKxHZDgYrEZEstliJiKQxWImIZHFUABGRNLZYiYiEMViJiGTx4hURkTQGKxGRLLZYiYikcVQAEZGsynGPVgYrEdkSdgUQEcliHysRkTQGKxGRLE5pJSKSxhYrEZEs9rESEUljsBIRyWKLlYhIGoOViEgWRwUQEUmrJC1Wu6d9AEREJipFKfOjvPLz8zFu3DgEBgZixIgRyMnJeeg9n376Kfr37w9/f3+kpaWV2rZjxw74+fmVaV8MViKyHUo5HuUUFxcHLy8vbN68GX369MGKFStKbT99+jRSU1ORmJiImJgYzJo1y7ztzJkz2Lp1K5QyBjqDlYhshkop+6O8jh49Cl9fXwBAly5d8P333z+0/dVXX4VKpUKdOnVQVFSEnJwc3Lp1C4sWLcK0adPKvC/2sRKR7ShHYCYkJCAhIcH83M/Pz3yqnpiYiA0bNpR6f61ataDVagEATk5OyM3NLbVdp9OhZs2a5udOTk64ffu2OVQdHBzKfGwMViKyGeUZFVAySB80YMAADBgwoNRrY8eOhV6vBwDo9Xq4uLiU2u7s7GzebnqPTqfDxYsXERERgYKCAmRkZGDevHmYPn36Y4+NXQFEZDOs2RXg4+OD5ORkAEBKSgratm370PYDBw7AaDTi8uXLMBqN8Pb2RlJSEjZu3IiYmBg0adLkT0MVYIuViGyJFYdbBQQEICwsDAEBAbC3t0d0dDQAYOHChXjrrbfg7e2Ndu3awc/PD0ajEeHh4RXel0p5zGWuRsuiK1yYiKqW8+NDLa7xl/djyvzeH74IsXh/1sIWKxHZjgqMT7VFDFYishmc0kpEJIzBSkQkrXL0BDBYich2cD1WIiJpvHhFRCSLLVYiImG8eEVEJI1dAUREstgVQEQkjcFKRCSLLVYiImnGypGsDFYishkcFUBEJI2jAoiIZLGPlYhIGoOViEiWil0BRESyVEUMViIiWZUjVxmsRGRD2BVARCSLowKIiKSxxUpEJIszr4iIpHGtACIiWRzHSkQkjcFKRCSMfaxERLLYFUBEJM1YOZqsDFYish2VI1cZrERkO9gVQEQkjcFKRCSMwUpEJIzBSkQky5oLXefn52PSpEm4efMmnJyc8Mknn8DNza3Uez799FPs378farUa06ZNg7e3N86cOYOZM2eiWrVqaNCgAebNmwc7O7vH7uvxW4mIniRFKfujnOLi4uDl5YXNmzejT58+WLFiRantp0+fRmpqKhITExETE4NZs2YBKA7bDz/8EHFxcTAYDNi/f/+f7ovBSkS2w6iU/VFOR48eha+vLwCgS5cu+P777x/a/uqrr0KlUqFOnTooKipCTk4Omjdvjtu3b0NRFOj1eqjVf36iz64AIrIdQn2siYmJ2LBhQ6nXatWqBa1WCwBwcnJCbm5uqe06nQ41a9Y0Pze9p0GDBpg9ezZWrlwJrVaLjh07/un+GaxEZDvKEawJCQlISEgwP/fz84Ofnx8AYMCAARgwYECp948dOxZ6vR4AoNfr4eLiUmq7s7OzebvpPVqtFvPmzcOmTZvQtGlTbNq0CQsWLMDMmTMfe2wMViKyHeUI1pJBWhY+Pj5ITk6Gt7c3UlJS0LZt24e2R0VFYdiwYbh69SqMRiPc3NxQo0YNODs7AwA8PDxw7NixP90Xg5WIbEdRkdVKBwQEICwsDAEBAbC3t0d0dDQAYOHChXjrrbfg7e2Ndu3awc/PD0ajEeHh4QCAuXPnIjg4GGq1Gvb29pgzZ86f7kulKI/+E9FoWbTQRyKiyu78+FCLa7zdILjM7/3m18UW789a2GIlItvBW7MQEQnjzCsiImEMViIiYVa8ePUkMViJyHawxUpEJIzBSkQkjKMCiIhkKUrluOkVg5WIbAdbrEREwjgqgIhIGC9eERHJUozsYyUiksUWKxGRMF68IiISxuFWRESyFI4KICKSpVSSroDH3kGAiIjKz+5pHwARUWXDYCUiEsZgJSISxmAlIhLGYCUiEsZgJSISxmAlIhLGYKUy+/XXX5GcnIyrV6+Cw5+JHo3B+v+MRiOKiopw5MgRGAwGq+zjWQ6mL774AjNnzsTixYuxa9cuzJkzR3wfRqMRN27ceOa+NyZXr14t9fz8+fNW2Y9Op8PZs2eRl5dnlfpkOYumtE6dOvWR2+bPn29JaQDA4cOHH7mtffv2Ftc3iYqKQr169XD58mWcPn0a7u7u+OSTT8TqA8XB9J///Ad37txBnz59kJWVhfDwcLH6ixYtQkhICOzs7JCbm4vp06dj2bJlYvWTkpKwefNmDBo0CIMHD8a7774rVhsAvv32WyxYsAAuLi7Q6/WIiIhA586dRfdx6dIl7N69G/fu3TO/NnbsWIvrnjt3DteuXcOiRYswadIkAEBRURFiYmLw1VdfWVy/pF27duGzzz5DUVER3nrrLahUKowZM8biut26dYNKpTI/V6vVKCwshEajwTfffGNx/arGomDt2bMnACAuLg5t2rSBj48PTp48iZMnT4ocXFxcHAAgKysL9+/fR6tWrfDzzz/DyckJGzduFNkHABw9ehSTJk1CUFAQNm7ciH/84x9itU2sHUwajQaDBw/GoEGDsGzZMgwZMkS0vqkVafrl02g0ovVXrFiBxMRE1KpVCzdu3MAHH3wgHqyhoaHw9fWFu7u7aN27d+/i66+/xs2bN5GUlASg+PsUGBgouh8AWL9+PbZs2YJhw4ZhzJgxePfdd0WCddeuXVAUBbNmzYK/vz+8vb3x888/Y/PmzQJHXfVYFKy+vr4AgHXr1mHEiBEAgLZt24r9UsfExAAARo4ciRUrVkCtVqOoqAgjR44UqW9iNBqRlpaGunXrwmAwICcnR7Q+YP1gGjduHMLCwjBhwgRMmzYNffv2Fa3/t7/9DQMHDsTly5cxYsQI/PWvfxWtX7NmTdSqVQsA4O7uDmdnZ9H6AODo6CjSQn1Qu3bt0K5dO5w+fRotW7bEnTt34OLiUqoFKMXOzg4ajQYqlQoqlQrVq1cXqWv6efztt9/g7e0NAGjRogUuXLggUr+qEVndKi8vD99//z1atWqF48eP4/79+xJlzbKzs81fFxUViQffO++8gzlz5iAyMhJRUVEYNGiQaH0A6NWrl1WD6f3330fLli2xd+9eRERE4MyZM6L9oEFBQXjllVdw7tw5NGrUCM2aNROrDQDOzs4YNmwY2rdvj1OnTiE/P9/8hzUkJMSi2qZwcHd3x44dO9CyZUtz6DVs2NCyAy9Br9ejV69e5tP0OnXqYMCAAWL1geIQDw0NxbVr1xAeHo5WrVqJ1tdqtViyZAm8vb1x/PhxvPjii6L1qwqR1a0yMzOxdOlSZGRkoHHjxggPD8dzzz0ncXwAgE2bNiE2NhZeXl7IyMjAuHHjzN0Q0q5cuYIXXnjBKrUzMzOtFkz79+/H66+/bn4eGxsr+gfiwf50e3t71K5dGwMHDkSNGjUsrr99+/ZHbrO09R0UFPSHr6tUKsTGxlpUu6SBAwdi+fLlGDduHD7//HMEBARg27ZtYvUBIDc3F8ePHzf/HHXr1k20fl5eHrZv346MjAw0atQIgYGBqFatmug+qgKRFmvjxo1FL5Q8aODAgXjnnXdw/vx51K1bF25ubqL1Y2Nj4ejoiLt372Lbtm3w9fV97IW5ikhLS0NSUhIKCgrw448/AgAiIiLE6rdr1w5LlizB9evX8frrr+O1114Tqw0ABQUFqFevHtq1a4effvoJJ0+ehJubG8LCwvDZZ59ZVDs9PR19+/aFwWBAYmIiNBoN3n33XdjZyQxaMfXHFxQUIDMzEy1atMCePXvEv0d2dnaoWbMmVCoVHBwc4OTkJFofKO4Wi4uLQ5cuXcRrA4CDgwM0Gg1cXV3h5eWFO3fuiP++VQUW/eS++uqrj3xI+uWXXzBy5EhMnz4dW7duxb59+0TrJyUloU+fPkhJSUFSUhLOnDkjWh8AwsLC0KxZM6t9j6ZNm4a6devi119/hbu7O6ZPny5aPycnB8HBwfD19cXYsWNx//59TJgwAbm5uRbVXbduHT7++GMUFhZi4cKFOHjwIM6ePYvIyEihI/+fSZMm4aeffgJQ3D0wZcoU0fqenp6Ijo7G7du3sWrVKtSpU0e0PgDUqFEDGzZsQEpKCg4cOIADBw6I1g8PD8fly5dx8OBB6PV6hIWFidavKixqsZr+U+/evQsXFxeRA/ojc+fOxfz58zFjxgz0798fw4cPR9euXcXqq1QqZGdnw93dHSqVCnfu3BGrbVK/fn3069dPvK7J7du30b9/f/z73/+Gj4+P+FhQnU6HzMxMNG7cGJmZmcjLy8OtW7csHkuZkpKC+Ph4qFQq7Ny5E7t370aNGjXg7+8vdOT/c+3aNQQEBAAARowY8cgugoqaNWsWEhMT0bZtW1SvXh1z584VrQ8Arq6uSE9PR3p6uvk1yT/SWVlZmDdvHo4cOYJu3bph1apVYrWrEpGugFGjRpmHRllL/fr1oVKp4ObmJn6K1bFjR7z//vuIjo5GZGQk3njjDdH6APDmm28iODgYjRs3Nr8mfYU6MzMTQPFAdanTaJPw8HBMmjQJ169fh6OjI/r27Yuvv/4aH3zwgUV17ezsUK1aNZw+fRr16tUz99daa5LAhQsX0LBhQ2RlZcEofA/7I0eOmIP73r17mDNnDmbPni26jwfHh1+/fl20vunisEqlgk6nE/85qipEgtV0etKwYUPzf4TkX9EaNWogPj4e9+7dQ1JSknjrODg4GMHBwQCAVq1awd7eXrQ+AGzevBk9evSwWst+xowZmDZtGjIyMjBmzBjx1pK3tzciIiLwxRdf4ODBg7h58yY+/PBDkdoXLlzAtm3bzBdifvnlF6v8Qk+fPh0TJkzAzZs34eHhIR56S5cuhZOTE4xGI6ZPn47evXuL1geAZcuWYfPmzbh//z7y8/PRoEED89hZCRMmTEBAQACys7Ph5+cn3qVUVYgEq7VPTyIjI/HZZ5/B1dUVp06dwrx588RqA8DevXvNP6yKouD27dvYsWOH6D5q1KghPv4WAE6fPo3p06cjMTERw4YNQ0REBPR6Pa5cuYIWLVpYXN9gMCApKQmbNm2CRqOBTqfD3r174ejoKHD0wEcffYTJkyfjxRdfREhICFJTUzFp0iQsXbpUpH5Jhw8fFp8JVdLy5csxZswYGAwGLF26tNTZiZSUlBSkpKQgMjISQ4YMwaxZs0Trt2nTBrt370ZOTg5cXV3x22+/idavKkSCdf78+bhw4QKysrLQrFkzeHh4SJQ1W7ZsGd577z00adJEtK7J8uXL8fHHHyM+Ph4dO3bEoUOHxPfh6uqK8PBwtGjRwjyG0s/Pz+K6ixcvxoIFC2Bvb48lS5Zg9erVqF+/PoYPH47u3btbXL9bt27o1asXFi1ahAYNGmD48OFioQoUt4QTExPNz1u3bo09e/ZY5awhOTkZgwcPFh8+FB0dXWpc7HfffWcOcEvH4D6oZs2a0Gg00Ov1qF+/fqnpuRJCQ0OxbNkyuLm5IT4+HuvWrcPu3btF91EViARryXnwffv2xcWLF0Xnwfv4+CAqKgp6vR79+vVDz549RX+5XV1d0aZNG8THx6Nfv37iYw+B4j5iALhx44ZoXUVR8NJLL+HatWu4d+8eWrZsCQBip9KDBg3Czp078fvvv6N///5W6/s8efIkZs6ciRs3bqBOnTqYNWuW+FjfW7duwdfXF3Xr1jXPXIqPj7e4bqNGjcxfN2zYEB06dLC45qPUrl0bW7duRfXq1REdHQ2dTidav1OnTpg0aRJyc3Oh1WqxZcsW0fpVhiLA399fMRqNyvvvv68oiqL069dPouxDrl27pkyYMEFp27ataN1Ro0YpqampSkhIiJKSkqK88cYbovVNrl27pvz+++/KpUuXlGPHjonUHDx4sKIoipKYmKhMnTpVURRFKSgoUHr37i1S3+THH39UJk6cqHTo0EFZuHChcvbsWdH6fn5+yi+//KIoiqKkp6crAQEBovUVRVEuXbr00EPS8ePHlQ0bNiiKoighISHKqVOnxGovX75cURRFKSoqUo4dO6bk5uYqsbGx5u+ZpQoKCsyPzz//XBk2bJj5OZWfSItVsfI8+MuXL2P79u349ttv0aJFC6xevVq0/qxZs3D+/HmMHj0aS5cuxfjx40XrA8XjTE+cOIF79+4hPz8f9erVE2kNdOrUCf7+/rh69SpWrlyJrKwsREREiM9M69ChAzp06IC7d+/iq6++wuTJk/Hll1+K1XdwcDB39TRr1swqXQGFhYXYtWuXecr19evXRS9gzZ07FwsWLABQfBFoypQp2LRpk0jtH374AWPGjIGdnR0WL16M2NhY0eFippWygP/9Ppte27t3r9h+qgqRYLX2Ah3jxo3DgAEDsGnTJtHFOUouMFG7dm0AxSMErLF4xvnz55GUlITw8HAEBwfjo48+Eqk7cuRIdO/eHW5ubnB1dUVWVhYCAgLQo0cPkfoPcnFxQVBQkNgvdUJCAoDiZeoiIiLQvn17pKWlWWURlrCwMHTt2hXHjh2Dh4eH+HqmarXa/MehXr16oiMblBJdMIoVumP++9//AgC++uorvPPOO+L1qxqRYLXWAh1Xr15F7dq1ERUVZR7Eb1qQRWLxjJL9wCqVCoqimENVcg45ADg5OUGlUiEvLw9ubm6iC9WUvPrs6ekJT09PsdrWZvr/bNOmDYDiP3ZarRbNmzcX35ejoyNGjRqFX3/9FfPnzxdf1q9OnTqIiYlB69atkZaWJnoRt+Qfe2v84TdJTExksAoQCdaS8+pTUlLEFuhYt24dpk6dipkzZ5Z6XWrxjCc1hxwAWrZsiTVr1sDDwwPBwcEoKioS38ezqH///qhdu/YTWZ5OURRkZ2cjLy8PeXl54jPs5s+fj7i4OCQnJ6NJkyYi66SanD59Gv7+/lAUBRkZGeavpS7AmRgMBvTp06fUmPTo6Gix+lWFyOpWISEhDy3Q0bx5c6Snp1u8QAcA7NmzB926dbPaLJDx48ejU6dOCAgIwOrVq5Geni72w1SyH1Kn08HBwQH5+fnIz883r2Fblc2fPx9Tp05FUFCQeTpxtWrV4OzsLHrWoNPpcObMGWRkZMDDwwMzZsxAnz59RObCnzx5Eq1atfrDeftS47l///33R26TXNovNTX1odesOcqhshJpsebk5JjXzvT19cXQoUMxYcIEDBw4UKI8Dh06hKVLl6Jbt27o378/6tWrJ1LXxJpzyE3TTE0URcG2bdvg6OjIYAXQu3dv9OnTB1u2bMH+/fsREREBrVYrNqsLKB4OuHbtWqjVasyYMQNdunQRGeNrEhAQgFOnTv3hDCipYH1S66J6eXnhwIEDKCwshKIouH79OoO1AkSC9cEFOvR6vcgCHSbh4eEwGAzYu3cvZs+ejfv372P9+vUitU2sNYc8NDTU/PXFixcxZcoUvP7665g2bZrYPp5lpgkOGo3GKhMcAGDnzp3YtWsXdDodJk+eLL7knql/WOI+b0/b+PHj0aBBA5w7dw4ODg5idyioakSCteQCHS+88ALCw8NFFugoKS0tDQcOHMDNmzfx5ptvitXV6XSYOHGiVeeQA8WLdW/YsAFTp04VXZnrWac8YoKD5AUajUYDjUYjftHQ5NKlS+YztgdJz7x6EmbPno2pU6di3rx5YmedVY1IsHp7ez80W0nylhE9e/bESy+9hAEDBoiuE/BHp4jSrl27hqlTp6JGjRpITEwUWW2/MjGdHXz33Xfo1KkTgOILKNa6tbM1hio5OjqK3uLlaSsoKMC9e/fMo1io/ESC9cD+EvEAAAUUSURBVMsvv8SqVatQUFBgfk1yUHG/fv0wfPhwsXom1j5FBIrvdWVvb4+//OUvD7WEebX1yUxwyMjIQGhoqPmKesnuGYn/A3d3d/GbNz4tAwcOxPr169G5c2e89tpraNu27dM+pGeSSLCuXr0aK1eutNq9olJSUjBkyBDxxTOsfYoIFC/wQo/2JCY4LFmyxPy1NRbQfvnll8VrPi2mbrZbt27h7bfftspEjapAJFjr1atnXmTEGqy1eEZJ1jhFBDhUpSysPcHB2v8Hlen2JcnJyZg9ezZcXFyQl5eH2bNno2PHjk/7sJ45IuNYJ0yYAJ1Oh+bNm5svOkh22v/RGD6J4SevvPIKOnXqBEVR8MMPP5j7+ACeplPVNGDAAPzzn/+Em5sbsrOz8eGHH3KFqwoQabFaY6ZSSX90a2SJ25pY+xSR6Fnj5ORkvivrc889x+FWFSQSrH//+9+xfft2XLlyBR07dkTTpk0lypq5u7sDKD5d//nnn8XGmfI0naiYabhYUVERRo0ahbZt2yItLU18pbqqQiRYZ86cCQ8PDxw6dAgvv/wywsLCRJf2e7A1aY0RAkRVmWm4WMlhY5Kz06oakWC19i1zSy7Qcf36dVy5ckW0PlFVZxoulpubi9TU1FJDJ6n8RILV2rfMDQ8PNy/QUbNmzUp1FZbIlgwdOhRNmjSBVqsFUDwDTnrR9KpAJFgfvGWu1Dx40x1ISy7QkZ+fb7Uxp0RVnVarrRRrHjxtIsOtTHJycuDi4gK1WiSvMXz4cEycOBEvvfQSevbsiaioKPMCHdLjWIkIWLt2LapXr17qjsjt27d/ikf0bBJJwG+++QZGoxEGgwFRUVEYNmwYhg0bZnHdRy3QYa11WYmquiNHjsBgMODw4cMAirsCGKzlJxKsa9euxapVqxASEoL9+/dj6NChIsH6qAU69Hq9xbWJ6GF5eXniS3JWRSLB6uDgAKB4cLFGoxELvid1B1IiKta0aVPs3LkTLVq0MM+irEwrdz0pIn2sU6ZMQWpqKj7++GOcPn0a2dnZmDVrlsTxITMzs9QCHWfPnrXaHUiJqjrTLXJMsWAwGMx30qWyE7t4pdfr4eTkhBs3bphnShHRs2HChAnmKd5r1qwxd+UFBQWZb7pJZSdyFejw4cM4evQokpOT4e/vjx07dkiUJaIn5ObNm+avk5OTzV9b81bblZlIsEZFRaFBgwaIjY1FXFwch0IRPcOstYRmVSISrA4ODqhVqxbUajWee+45GAwGibJE9ISUbJmylWo5kVEBzs7OGDJkCAIDA7Fp0yar3UmAiKzjj25foyjKQ7dvp7IRuXhlMBiQlZWFJk2a4Ny5c2jQoAGXGyN6hqSmpj5yG5fXLD+RYL148SJ27dplnsN//fp1q9xCmojoWSDSx2paberYsWO4dOkSbt++LVGWiOiZJBKsjo6OGDVqFJ5//nksWLAAN27ckChLRPRMEglWRVGQnZ2NvLw85OXl4c6dOxJliYieSRYHq06nw9ixY7Fnzx707t0b3bt3R5cuXSSOjYjomWTRxasvvvgCa9euhVqtxowZMxioRESwsMW6c+dO7Nq1C/Hx8YiNjZU6JiKiZ5pFwarRaKDRaODm5sbbpRAR/T+xpfg5v5iIqJhFfayvvPIKOnXqBEVR8MMPP5hX+QeA6OhokQMkInrWWBSsnAZHRPQw0bu0EhGRYB8rEREVY7ASEQljsBIRCWOwEhEJY7ASEQn7Px7QUqStvMECAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "889"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now there are no null values and we are to deal with categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   female  male\n",
       "0       0     1\n",
       "1       1     0\n",
       "2       1     0\n",
       "3       1     0\n",
       "4       0     1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sex = pd.get_dummies(train['Sex'])\n",
    "Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Do not require both the coumns as that will create a multicollinearity between them\n",
    "\n",
    "Sex = pd.get_dummies(train['Sex'], drop_first=True)\n",
    "Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Embark = pd.get_dummies(train['Embarked'], drop_first=True)\n",
    "Embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Now replacing the original values with categorical dummy values and also dropping unnecessary columns which do not impact the modelling\n",
    "\n",
    "train.drop(['Sex', 'Embarked', 'Name', 'Ticket', 'PassengerId'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0         0       3  22.0      1      0   7.2500\n",
       "1         1       1  38.0      1      0  71.2833\n",
       "2         1       3  26.0      0      0   7.9250\n",
       "3         1       1  35.0      1      0  53.1000\n",
       "4         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.concat([train, Sex, Embark], axis =1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 101)\n",
    "\n",
    "\n",
    "#X = train.drop(['Survived'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop(['Survived'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pclass   Age  SibSp  Parch      Fare  male  Q  S\n",
      "307       1  17.0      1      0  108.9000     0  0  0\n",
      "229       3  24.0      3      1   25.4667     0  0  1\n",
      "82        3  24.0      0      0    7.7875     0  1  0\n",
      "353       3  25.0      1      0   17.8000     1  0  1\n",
      "707       1  42.0      0      0   26.2875     1  0  1\n",
      "     Pclass   Age  SibSp  Parch    Fare  male  Q  S\n",
      "511       3  24.0      0      0    8.05     1  0  1\n",
      "613       3  24.0      0      0    7.75     1  1  0\n",
      "615       2  24.0      1      2   65.00     0  0  1\n",
      "337       1  41.0      0      0  134.50     0  0  0\n",
      "718       3  24.0      0      0   15.50     1  1  0\n"
     ]
    }
   ],
   "source": [
    "print(X_train.head())\n",
    "print(X_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(711, 8)\n",
      "(178, 8)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(711,)\n",
      "(178,)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[98,  9],\n",
       "       [26, 45]], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([98,  9], dtype=int64)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[0][0]+cm[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm[0][1]+cm[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.085714285714285"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = (cm[0][0]+cm[1][1])/(cm[0][1]+cm[1][0])\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.92      0.85       107\n",
      "           1       0.83      0.63      0.72        71\n",
      "\n",
      "    accuracy                           0.80       178\n",
      "   macro avg       0.81      0.77      0.78       178\n",
      "weighted avg       0.81      0.80      0.80       178\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PERFORMING SAME ON TEST DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Age'] = test[['Age', 'Pclass']].apply(age_values_fill, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed626be48>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Now checking the same on heatmap\n",
    "\n",
    "sns.heatmap(test.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('Cabin', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed63051d0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed63a06a0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     1\n",
       "3     1\n",
       "4     0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Do not require both the coumns as that will create a multicollinearity between them\n",
    "\n",
    "Sex = pd.get_dummies(test['Sex'], drop_first=True)\n",
    "Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Q  S\n",
       "0  1  0\n",
       "1  0  1\n",
       "2  1  0\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Embark = pd.get_dummies(test['Embarked'], drop_first=True)\n",
    "Embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['Sex', 'Embarked', 'Name', 'Ticket', 'PassengerId'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0       3  34.5      0      0   7.8292     1  1  0\n",
       "1       3  47.0      1      0   7.0000     0  0  1\n",
       "2       2  62.0      0      0   9.6875     1  1  0\n",
       "3       3  27.0      0      0   8.6625     1  0  1\n",
       "4       3  22.0      1      1  12.2875     0  0  1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.concat([test, Sex, Embark], axis =1)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pred = lr.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_backup = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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, 0, 1, 0, 0, 1, 1, 0, 0, 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, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 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, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pred = pd.DataFrame(test_data_pred, columns = ['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived\n",
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         1"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_pred.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived\n",
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         1"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_submission = pd.concat([test_backup['PassengerId'], test_data_pred], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892       0.0\n",
       "1          893       0.0\n",
       "2          894       0.0\n",
       "3          895       0.0\n",
       "4          896       1.0"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_submission.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aed9900400>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(final_submission.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aeda53e160>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(final_submission.isnull(), yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "print(final_submission[final_submission[\"Survived\"].isnull()][final_submission.columns[final_submission.isnull().any()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_submission.at[417, 'Survived'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "print(final_submission[final_submission[\"Survived\"].isnull()][final_submission.columns[final_submission.isnull().any()]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "413         1305       1.0\n",
       "414         1306       0.0\n",
       "415         1307       0.0\n",
       "416         1308       0.0\n",
       "417         1309       1.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_submission.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat = final_submission.round(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 2 columns):\n",
      "PassengerId    418 non-null int32\n",
      "Survived       418 non-null int32\n",
      "dtypes: int32(2)\n",
      "memory usage: 3.3 KB\n"
     ]
    }
   ],
   "source": [
    "sat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "sat.to_csv('Kaggle_Submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</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>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 0\n",
       "895                 0\n",
       "896                 1\n",
       "897                 0\n",
       "898                 1\n",
       "899                 0\n",
       "900                 1\n",
       "901                 0\n",
       "902                 0\n",
       "903                 0\n",
       "904                 1\n",
       "905                 0\n",
       "906                 1\n",
       "907                 1\n",
       "908                 0\n",
       "909                 0\n",
       "910                 1\n",
       "911                 1\n",
       "912                 0\n",
       "913                 0\n",
       "914                 1\n",
       "915                 0\n",
       "916                 1\n",
       "917                 0\n",
       "918                 1\n",
       "919                 0\n",
       "920                 0\n",
       "921                 0\n",
       "...               ...\n",
       "1280                0\n",
       "1281                0\n",
       "1282                1\n",
       "1283                0\n",
       "1284                0\n",
       "1285                0\n",
       "1286                1\n",
       "1287                0\n",
       "1288                1\n",
       "1289                0\n",
       "1290                0\n",
       "1291                1\n",
       "1292                0\n",
       "1293                1\n",
       "1294                0\n",
       "1295                0\n",
       "1296                0\n",
       "1297                0\n",
       "1298                0\n",
       "1299                1\n",
       "1300                1\n",
       "1301                1\n",
       "1302                1\n",
       "1303                1\n",
       "1304                0\n",
       "1305                1\n",
       "1306                0\n",
       "1307                0\n",
       "1308                0\n",
       "1309                1\n",
       "\n",
       "[418 rows x 1 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat.set_index('PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "sag = sat.to_csv('Kaggle_Submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "sag = pd.read_csv(\"Kaggle_Submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "mekk = pd.DataFrame(sag[['PassengerId', 'Survived']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 2 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Survived       418 non-null int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 6.6 KB\n"
     ]
    }
   ],
   "source": [
    "mekk.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "mekk.to_csv('Final_KG', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}