Titanic_ExploratoryAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\shrath\\\\Desktop\\\\important\\\\DataScience_practice_SampleData\\\\Titanic_dataset_analysis'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:/Users/shrath/Desktop/important/DataScience_practice_SampleData/Titanic_dataset_analysis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "5          False     False   False  False  False   True  False  False   False   \n",
       "6          False     False   False  False  False  False  False  False   False   \n",
       "7          False     False   False  False  False  False  False  False   False   \n",
       "8          False     False   False  False  False  False  False  False   False   \n",
       "9          False     False   False  False  False  False  False  False   False   \n",
       "10         False     False   False  False  False  False  False  False   False   \n",
       "11         False     False   False  False  False  False  False  False   False   \n",
       "12         False     False   False  False  False  False  False  False   False   \n",
       "13         False     False   False  False  False  False  False  False   False   \n",
       "14         False     False   False  False  False  False  False  False   False   \n",
       "15         False     False   False  False  False  False  False  False   False   \n",
       "16         False     False   False  False  False  False  False  False   False   \n",
       "17         False     False   False  False  False   True  False  False   False   \n",
       "18         False     False   False  False  False  False  False  False   False   \n",
       "19         False     False   False  False  False   True  False  False   False   \n",
       "20         False     False   False  False  False  False  False  False   False   \n",
       "21         False     False   False  False  False  False  False  False   False   \n",
       "22         False     False   False  False  False  False  False  False   False   \n",
       "23         False     False   False  False  False  False  False  False   False   \n",
       "24         False     False   False  False  False  False  False  False   False   \n",
       "25         False     False   False  False  False  False  False  False   False   \n",
       "26         False     False   False  False  False   True  False  False   False   \n",
       "27         False     False   False  False  False  False  False  False   False   \n",
       "28         False     False   False  False  False   True  False  False   False   \n",
       "29         False     False   False  False  False   True  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "861        False     False   False  False  False  False  False  False   False   \n",
       "862        False     False   False  False  False  False  False  False   False   \n",
       "863        False     False   False  False  False   True  False  False   False   \n",
       "864        False     False   False  False  False  False  False  False   False   \n",
       "865        False     False   False  False  False  False  False  False   False   \n",
       "866        False     False   False  False  False  False  False  False   False   \n",
       "867        False     False   False  False  False  False  False  False   False   \n",
       "868        False     False   False  False  False   True  False  False   False   \n",
       "869        False     False   False  False  False  False  False  False   False   \n",
       "870        False     False   False  False  False  False  False  False   False   \n",
       "871        False     False   False  False  False  False  False  False   False   \n",
       "872        False     False   False  False  False  False  False  False   False   \n",
       "873        False     False   False  False  False  False  False  False   False   \n",
       "874        False     False   False  False  False  False  False  False   False   \n",
       "875        False     False   False  False  False  False  False  False   False   \n",
       "876        False     False   False  False  False  False  False  False   False   \n",
       "877        False     False   False  False  False  False  False  False   False   \n",
       "878        False     False   False  False  False   True  False  False   False   \n",
       "879        False     False   False  False  False  False  False  False   False   \n",
       "880        False     False   False  False  False  False  False  False   False   \n",
       "881        False     False   False  False  False  False  False  False   False   \n",
       "882        False     False   False  False  False  False  False  False   False   \n",
       "883        False     False   False  False  False  False  False  False   False   \n",
       "884        False     False   False  False  False  False  False  False   False   \n",
       "885        False     False   False  False  False  False  False  False   False   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "5    False   True     False  \n",
       "6    False  False     False  \n",
       "7    False   True     False  \n",
       "8    False   True     False  \n",
       "9    False   True     False  \n",
       "10   False  False     False  \n",
       "11   False  False     False  \n",
       "12   False   True     False  \n",
       "13   False   True     False  \n",
       "14   False   True     False  \n",
       "15   False   True     False  \n",
       "16   False   True     False  \n",
       "17   False   True     False  \n",
       "18   False   True     False  \n",
       "19   False   True     False  \n",
       "20   False   True     False  \n",
       "21   False  False     False  \n",
       "22   False   True     False  \n",
       "23   False  False     False  \n",
       "24   False   True     False  \n",
       "25   False   True     False  \n",
       "26   False   True     False  \n",
       "27   False  False     False  \n",
       "28   False   True     False  \n",
       "29   False   True     False  \n",
       "..     ...    ...       ...  \n",
       "861  False   True     False  \n",
       "862  False  False     False  \n",
       "863  False   True     False  \n",
       "864  False   True     False  \n",
       "865  False   True     False  \n",
       "866  False   True     False  \n",
       "867  False  False     False  \n",
       "868  False   True     False  \n",
       "869  False   True     False  \n",
       "870  False   True     False  \n",
       "871  False  False     False  \n",
       "872  False  False     False  \n",
       "873  False   True     False  \n",
       "874  False   True     False  \n",
       "875  False   True     False  \n",
       "876  False   True     False  \n",
       "877  False   True     False  \n",
       "878  False   True     False  \n",
       "879  False  False     False  \n",
       "880  False   True     False  \n",
       "881  False   True     False  \n",
       "882  False   True     False  \n",
       "883  False   True     False  \n",
       "884  False   True     False  \n",
       "885  False   True     False  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4c65d5f8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df45e251d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4cb17fd0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4cb06940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4cc1f5c0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M96nb7b7jbUE/qufixYt69tln9ZOf/ERTpkyRJA0dOlTV1dWSpH379ikpKUkjRozQRx99JK/Xq/r6enm9XnbzAEAnCPoWf3l5ua5evaqVK1dq5cqVkqRFixZp6dKlKisrU3x8vNLS0hQeHq6kpCSlp6fL6/W2+UJ3AIB9LJ/P5wv1EHfjdrs1cuTIgNaxlV09fk+PSAj1CEC7+OrFLwTjqxfv1k4+wAUAhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhiH8AGAYwg8AhnGEegDAZFsPnQ71CF1GSqgHMIhtW/xHjhxRZmamJKmurk7Tp0/XjBkzVFBQIK/XK0lasWKFpkyZooyMDB09etSuUQAAt7Al/GvWrNHixYt1/fp1SVJRUZGys7O1bt06+Xw+7dy5U8ePH9fBgwdVVVWlsrIyFRYW2jEKAOB/2BL+2NhYLV++3H/5+PHjGjVqlCQpNTVVBw4ckNvtVkpKiizLUkxMjFpbW3X58mU7xgEA3MKWffxpaWk6c+aM/7LP55NlWZKkyMhINTY2qqmpSdHR0f773Lze5XLdtr6ampoAJ+oW4PL3j8CfSwQXr03czu73aaf8cTcs7Iv/WHg8HvXs2VNOp1Mej6fN9VFRUe0un5iYGNDvr+UPaH6BPpcILl6baE8w3qdut/uOt3XK4ZxDhw5VdXW1JGnfvn1KSkrSiBEj9NFHH8nr9aq+vl5er7fdrX0AQHB1yhZ/bm6u8vLyVFZWpvj4eKWlpSk8PFxJSUlKT0+X1+tVfn5+Z4wCAMazLfz9+vXThg0bJElxcXH61a9+ddt9srKylJWVZdcIAIB28MldADAM4QcAwxB+ADAM5+oxzMUd60I9QpfR+6kZoR4BCAm2+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAxD+AHAMIQfAAzjCPUAXq9XS5YsUW1trbp166alS5dqwIABoR4LAO5bId/i37Fjh27cuKFf//rX+vGPf6zi4uJQjwQA97WQh9/tdmvMmDGSpCeeeEJ//vOfQzwRANzfQr6rp6mpSU6n0385PDxcLS0tcji+GM3tdgf0O/oHtPT9pe6hx0I9QpdRF+DrKhh4bX6B1+YX7H5thjz8TqdTHo/Hf9nr9baJ/siRI0MxFgDct0K+q2fEiBHat2+fJOnw4cMaMmRIiCcCgPub5fP5fKEc4OZRPSdOnJDP59Mbb7yhhISEUI4EAPe1kIcfnYPDZtHVHTlyRG+++aYqKytDPcp9L+T7+NE5bj1s9vDhwyouLtaqVatCPRYgSVqzZo22bdumiIiIUI9ihJDv40fn4LBZdGWxsbFavnx5qMcwBuE3xJ0OmwW6grS0tDZH88FehN8QX3bYLABzEH5DcNgsgJvY5DPEuHHjtH//fmVkZPgPmwVgJg7nBADDsKsHAAxD+AHAMIQfAAxD+AHAMIQfAAxD+GGk1atXa/bs2Xr22Wc1Z86cgE5hsWzZMtXX13/l5V9++WVVV1d/5eWB/y+O44dxTp06pV27dmn9+vWyLEs1NTXKzc3Vtm3bvtL6Fi1aFOQJAXuxxQ/juFwu1dfXa+PGjbpw4YISExO1ceNGZWZm6vTp05Kk9evXa/ny5Tpz5owmTpyozMxMrVmzRt/+9rd186MvhYWF+vDDD/3LTZ48WWfOnJEkbd++XUuXLlVjY6NeeuklZWZmKjMzU7W1tZKk9957T08//bTmzp2rurq60DwRMBbhh3FcLpdWrVqlQ4cOKT09XRMmTNDu3bvveP+Ghgb94he/0Ny5c/XYY4/pj3/8o27cuKGDBw9q7Nix/vtNmTJFW7dulSRt2bJF06ZNU3l5uUaPHq3Kykq9/vrrWrJkiRobG/Xuu+9qw4YNWrlypZqbm21/zMCt2NUD49TV1cnpdKqoqEiSdOzYMc2bN0+9e/f23+fWD7T369dP3bp1kyRNmzZNW7ZsUUNDg771rW+1OdHdpEmTNH36dE2dOlVNTU0aMmSITpw4oY8//ljbt2+XJF29elWfffaZBg0a5F/n8OHDbX/MwK3Y4odxamtrtWTJEl2/fl2SFBcXp6ioKEVHR6uhoUGS9Omnn/rvHxb2xdvkySefVE1NjTZt2qQpU6a0Wa/T6dSwYcNUVFSkyZMnS5Li4+M1e/ZsVVZW6q233tLEiRPVv39/nTp1SteuXVNra6tqamrsfshAG2zxwzjjx4/X6dOnNXXqVPXo0UM+n085OTl64IEH9NOf/lRf+9rX1Ldv33aXtSxLaWlpOnDgQLtfXTl16lQ999xz/pPgvfDCC1q0aJE2bNigpqYmzZ8/Xy6XSz/60Y+UkZEhl8vFt06h03GSNgAwDLt6AMAwhB8ADEP4AcAwhB8ADEP4AcAwhB8ADEP4AcAwhB8ADPN/wkN3i8hwrjQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4cca6240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train,hue='Sex',palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4cad6c50>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4cbf8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train,hue='Pclass',palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4cef8550>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4d02a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(),kde=False,color='darkred',bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4d047208>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4ce9fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4d0d3e80>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4d14e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assigning mean values of Age to NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\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",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1df4d24f908>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4d3b55f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting Categorial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    889 non-null int64\n",
      "Survived       889 non-null int64\n",
      "Pclass         889 non-null int64\n",
      "Name           889 non-null object\n",
      "Sex            889 non-null object\n",
      "Age            889 non-null float64\n",
      "SibSp          889 non-null int64\n",
      "Parch          889 non-null int64\n",
      "Ticket         889 non-null object\n",
      "Fare           889 non-null float64\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sex=pd.get_dummies(train['Sex'],drop_first=True)\n",
    "Embarked=pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>male</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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      ],
      "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Embarked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <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>0</th>\n",
       "      <td>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
       "1            2         1       1  38.0      1      0  71.2833\n",
       "2            3         1       3  26.0      0      0   7.9250\n",
       "3            4         1       1  35.0      1      0  53.1000\n",
       "4            5         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.concat([train,Sex,Embarked],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\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>4</td>\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>5</td>\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": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a logistic regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train-Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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>4</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>5</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": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('Survived',axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Survived'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(train.drop('Survived',axis=1),train['Survived'],test_size=0.30,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>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>807</th>\n",
       "      <td>808</td>\n",
       "      <td>3</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>652</td>\n",
       "      <td>2</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26.00</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>690</th>\n",
       "      <td>691</td>\n",
       "      <td>1</td>\n",
       "      <td>31.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>236</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>247.5208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>340</td>\n",
       "      <td>1</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>3</td>\n",
       "      <td>33.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>756</td>\n",
       "      <td>2</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>805</td>\n",
       "      <td>3</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>268</td>\n",
       "      <td>3</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>460</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>624</td>\n",
       "      <td>3</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>348</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>190</td>\n",
       "      <td>3</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>782</td>\n",
       "      <td>1</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>57.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>682</td>\n",
       "      <td>1</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>780</td>\n",
       "      <td>1</td>\n",
       "      <td>43.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>426</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</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>280</th>\n",
       "      <td>281</td>\n",
       "      <td>3</td>\n",
       "      <td>65.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>626</td>\n",
       "      <td>1</td>\n",
       "      <td>61.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.3208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>425</td>\n",
       "      <td>3</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.2125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>251</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</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>842</th>\n",
       "      <td>843</td>\n",
       "      <td>1</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>558</td>\n",
       "      <td>1</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>227.5250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>420</td>\n",
       "      <td>3</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>11.00</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>373</td>\n",
       "      <td>3</td>\n",
       "      <td>19.00</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",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>37.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>508</td>\n",
       "      <td>1</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>625</td>\n",
       "      <td>3</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>854</td>\n",
       "      <td>1</td>\n",
       "      <td>16.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>40.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>3</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "      <td>14.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>706</td>\n",
       "      <td>2</td>\n",
       "      <td>39.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>734</td>\n",
       "      <td>2</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>318</td>\n",
       "      <td>2</td>\n",
       "      <td>54.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>759</td>\n",
       "      <td>3</td>\n",
       "      <td>34.00</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",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>646</td>\n",
       "      <td>1</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76.7292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>554</td>\n",
       "      <td>3</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>395</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>601</td>\n",
       "      <td>2</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>577</td>\n",
       "      <td>2</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>841</td>\n",
       "      <td>3</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>3</td>\n",
       "      <td>45.00</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",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>525</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>2</td>\n",
       "      <td>42.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass    Age  SibSp  Parch      Fare  male  Q  S\n",
       "807          808       3  18.00      0      0    7.7750     0  0  1\n",
       "651          652       2  18.00      0      1   23.0000     0  0  1\n",
       "2              3       3  26.00      0      0    7.9250     0  0  1\n",
       "690          691       1  31.00      1      0   57.0000     1  0  1\n",
       "196          197       3  24.00      0      0    7.7500     1  1  0\n",
       "888          889       3  24.00      1      2   23.4500     0  0  1\n",
       "258          259       1  35.00      0      0  512.3292     0  0  0\n",
       "39            40       3  14.00      1      0   11.2417     0  0  0\n",
       "235          236       3  24.00      0      0    7.5500     0  0  1\n",
       "299          300       1  50.00      0      1  247.5208     0  0  0\n",
       "339          340       1  45.00      0      0   35.5000     1  0  1\n",
       "881          882       3  33.00      0      0    7.8958     1  0  1\n",
       "755          756       2   0.67      1      1   14.5000     1  0  1\n",
       "804          805       3  27.00      0      0    6.9750     1  0  1\n",
       "267          268       3  25.00      1      0    7.7750     1  0  1\n",
       "459          460       3  24.00      0      0    7.7500     1  1  0\n",
       "65            66       3  24.00      1      1   15.2458     1  0  0\n",
       "623          624       3  21.00      0      0    7.8542     1  0  1\n",
       "347          348       3  24.00      1      0   16.1000     0  0  1\n",
       "189          190       3  36.00      0      0    7.8958     1  0  1\n",
       "29            30       3  24.00      0      0    7.8958     1  0  1\n",
       "781          782       1  17.00      1      0   57.0000     0  0  1\n",
       "618          619       2   4.00      2      1   39.0000     0  0  1\n",
       "681          682       1  27.00      0      0   76.7292     1  0  0\n",
       "779          780       1  43.00      0      1  211.3375     0  0  1\n",
       "627          628       1  21.00      0      0   77.9583     0  0  1\n",
       "425          426       3  24.00      0      0    7.2500     1  0  1\n",
       "280          281       3  65.00      0      0    7.7500     1  1  0\n",
       "625          626       1  61.00      0      0   32.3208     1  0  1\n",
       "424          425       3  18.00      1      1   20.2125     1  0  1\n",
       "..           ...     ...    ...    ...    ...       ...   ... .. ..\n",
       "250          251       3  24.00      0      0    7.2500     1  0  1\n",
       "842          843       1  30.00      0      0   31.0000     0  0  0\n",
       "557          558       1  37.00      0      0  227.5250     1  0  0\n",
       "419          420       3  10.00      0      2   24.1500     0  0  1\n",
       "59            60       3  11.00      5      2   46.9000     1  0  1\n",
       "372          373       3  19.00      0      0    8.0500     1  0  1\n",
       "137          138       1  37.00      1      0   53.1000     1  0  1\n",
       "507          508       1  37.00      0      0   26.5500     1  0  1\n",
       "624          625       3  21.00      0      0   16.1000     1  0  1\n",
       "853          854       1  16.00      0      1   39.4000     0  0  1\n",
       "49            50       3  18.00      1      0   17.8000     0  0  1\n",
       "40            41       3  40.00      1      0    9.4750     0  0  1\n",
       "478          479       3  22.00      0      0    7.5208     1  0  1\n",
       "5              6       3  24.00      0      0    8.4583     1  1  0\n",
       "111          112       3  14.50      1      0   14.4542     0  0  0\n",
       "705          706       2  39.00      0      0   26.0000     1  0  1\n",
       "733          734       2  23.00      0      0   13.0000     1  0  1\n",
       "317          318       2  54.00      0      0   14.0000     1  0  1\n",
       "758          759       3  34.00      0      0    8.0500     1  0  1\n",
       "886          887       2  27.00      0      0   13.0000     1  0  1\n",
       "645          646       1  48.00      1      0   76.7292     1  0  0\n",
       "553          554       3  22.00      0      0    7.2250     1  0  0\n",
       "394          395       3  24.00      0      2   16.7000     0  0  1\n",
       "76            77       3  24.00      0      0    7.8958     1  0  1\n",
       "600          601       2  24.00      2      1   27.0000     0  0  1\n",
       "576          577       2  34.00      0      0   13.0000     0  0  1\n",
       "840          841       3  20.00      0      0    7.9250     1  0  1\n",
       "338          339       3  45.00      0      0    8.0500     1  0  1\n",
       "524          525       3  24.00      0      0    7.2292     1  0  0\n",
       "865          866       2  42.00      0      0   13.0000     0  0  1\n",
       "\n",
       "[622 rows x 9 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "807    0\n",
       "651    1\n",
       "2      1\n",
       "690    1\n",
       "196    0\n",
       "888    0\n",
       "258    1\n",
       "39     1\n",
       "235    0\n",
       "299    1\n",
       "339    0\n",
       "881    0\n",
       "755    1\n",
       "804    1\n",
       "267    1\n",
       "459    0\n",
       "65     1\n",
       "623    0\n",
       "347    1\n",
       "189    0\n",
       "29     0\n",
       "781    1\n",
       "618    1\n",
       "681    1\n",
       "779    1\n",
       "627    1\n",
       "425    0\n",
       "280    0\n",
       "625    0\n",
       "424    0\n",
       "      ..\n",
       "250    0\n",
       "842    1\n",
       "557    0\n",
       "419    0\n",
       "59     0\n",
       "372    0\n",
       "137    0\n",
       "507    1\n",
       "624    0\n",
       "853    1\n",
       "49     0\n",
       "40     0\n",
       "478    0\n",
       "5      0\n",
       "111    0\n",
       "705    0\n",
       "733    0\n",
       "317    0\n",
       "758    0\n",
       "886    0\n",
       "645    1\n",
       "553    1\n",
       "394    1\n",
       "76     0\n",
       "600    1\n",
       "576    1\n",
       "840    0\n",
       "338    1\n",
       "524    0\n",
       "865    1\n",
       "Name: Survived, Length: 622, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>511</th>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</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",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>614</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>616</td>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>338</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>134.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>719</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>55.5</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",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>827</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>419</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>615</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",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>748</td>\n",
       "      <td>2</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91.0792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>657</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>641</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>270</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135.6333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>497</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78.2667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>322</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</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>490</th>\n",
       "      <td>491</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.9667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>186</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>481</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>46.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>725</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>658</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>239</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>735</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>761</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>809</td>\n",
       "      <td>2</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>227</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>763</td>\n",
       "      <td>3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>599</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>637</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>547</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>604</td>\n",
       "      <td>3</td>\n",
       "      <td>44.0</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",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>755</td>\n",
       "      <td>2</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>271</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>723</td>\n",
       "      <td>2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>3</td>\n",
       "      <td>29.0</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",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>415</td>\n",
       "      <td>3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.4042</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>260</td>\n",
       "      <td>2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>820</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>829</td>\n",
       "      <td>3</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>733</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>670</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>635</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass   Age  SibSp  Parch      Fare  male  Q  S\n",
       "511          512       3  24.0      0      0    8.0500     1  0  1\n",
       "613          614       3  24.0      0      0    7.7500     1  1  0\n",
       "615          616       2  24.0      1      2   65.0000     0  0  1\n",
       "337          338       1  41.0      0      0  134.5000     0  0  0\n",
       "718          719       3  24.0      0      0   15.5000     1  1  0\n",
       "152          153       3  55.5      0      0    8.0500     1  0  1\n",
       "826          827       3  24.0      0      0   56.4958     1  0  1\n",
       "418          419       2  30.0      0      0   13.0000     1  0  1\n",
       "614          615       3  35.0      0      0    8.0500     1  0  1\n",
       "241          242       3  24.0      1      0   15.5000     0  1  0\n",
       "747          748       2  30.0      0      0   13.0000     0  0  1\n",
       "291          292       1  19.0      1      0   91.0792     0  0  0\n",
       "656          657       3  24.0      0      0    7.8958     1  0  1\n",
       "128          129       3  24.0      1      1   22.3583     0  0  0\n",
       "868          869       3  24.0      0      0    9.5000     1  0  1\n",
       "640          641       3  20.0      0      0    7.8542     1  0  1\n",
       "269          270       1  35.0      0      0  135.6333     0  0  1\n",
       "496          497       1  54.0      1      0   78.2667     0  0  0\n",
       "142          143       3  24.0      1      0   15.8500     0  0  1\n",
       "321          322       3  27.0      0      0    7.8958     1  0  1\n",
       "158          159       3  24.0      0      0    8.6625     1  0  1\n",
       "490          491       3  24.0      1      0   19.9667     1  0  1\n",
       "185          186       1  37.0      0      0   50.0000     1  0  1\n",
       "110          111       1  47.0      0      0   52.0000     1  0  1\n",
       "480          481       3   9.0      5      2   46.9000     1  0  1\n",
       "724          725       1  27.0      1      0   53.1000     1  0  1\n",
       "139          140       1  24.0      0      0   79.2000     1  0  0\n",
       "657          658       3  32.0      1      1   15.5000     0  1  0\n",
       "7              8       3   2.0      3      1   21.0750     1  0  1\n",
       "238          239       2  19.0      0      0   10.5000     1  0  1\n",
       "..           ...     ...   ...    ...    ...       ...   ... .. ..\n",
       "476          477       2  34.0      1      0   21.0000     1  0  1\n",
       "327          328       2  36.0      0      0   13.0000     0  0  1\n",
       "734          735       2  23.0      0      0   13.0000     1  0  1\n",
       "760          761       3  24.0      0      0   14.5000     1  0  1\n",
       "808          809       2  39.0      0      0   13.0000     1  0  1\n",
       "226          227       2  19.0      0      0   10.5000     1  0  1\n",
       "762          763       3  20.0      0      0    7.2292     1  0  0\n",
       "866          867       2  27.0      1      0   13.8583     0  0  0\n",
       "598          599       3  24.0      0      0    7.2250     1  0  0\n",
       "636          637       3  32.0      0      0    7.9250     1  0  1\n",
       "85            86       3  33.0      3      0   15.8500     0  0  1\n",
       "24            25       3   8.0      3      1   21.0750     0  0  1\n",
       "546          547       2  19.0      1      0   26.0000     0  0  1\n",
       "603          604       3  44.0      0      0    8.0500     1  0  1\n",
       "754          755       2  48.0      1      2   65.0000     0  0  1\n",
       "270          271       1  37.0      0      0   31.0000     1  0  1\n",
       "728          729       2  25.0      1      0   26.0000     1  0  1\n",
       "722          723       2  34.0      0      0   13.0000     1  0  1\n",
       "873          874       3  47.0      0      0    9.0000     1  0  1\n",
       "90            91       3  29.0      0      0    8.0500     1  0  1\n",
       "414          415       3  44.0      0      0    7.9250     1  0  1\n",
       "197          198       3  42.0      0      1    8.4042     1  0  1\n",
       "84            85       2  17.0      0      0   10.5000     0  0  1\n",
       "259          260       2  50.0      0      1   26.0000     0  0  1\n",
       "819          820       3  10.0      3      2   27.9000     1  0  1\n",
       "792          793       3  24.0      8      2   69.5500     0  0  1\n",
       "828          829       3  24.0      0      0    7.7500     1  1  0\n",
       "732          733       2  29.0      0      0    0.0000     1  0  1\n",
       "669          670       1  37.0      1      0   52.0000     0  0  1\n",
       "634          635       3   9.0      3      2   27.9000     0  0  1\n",
       "\n",
       "[267 rows x 9 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training & Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel=LogisticRegression()\n",
    "logmodel.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[151,  12],\n",
       "       [ 36,  68]], dtype=int64)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8202247191011236"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  }
 ],
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