Logistic Regression on titanic dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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",
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       "      <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",
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       "    <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": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\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",
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       "      <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",
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       "      <td>False</td>\n",
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       "      <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",
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       "      <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",
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       "      <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f381d9d1d0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False, cbar=False, cmap='viridis' )\n",
    "# that yello thing is missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f381dd7f98>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f381e30b00>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',hue='Sex', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f382ec5e48>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',hue='Pclass', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning:\n",
      "\n",
      "The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f382eb1908>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['Age'].dropna(), kde=False, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f382f67b00>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Age'].plot('hist', bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f383079390>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f3830bf9b0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(bins=40, figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import cufflinks as cf\n",
    "# cf.go_offline()\n",
    "# train['Fare'].iplot(kind='hist', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f38319e518>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xq3vvvVfHjx/XuHHjdMkll1gxNAAAgO0sKVg9e/bUI4888r3XN23aZMVwneTl5am2ttbycdygpqZG0n8+3ovuJSQk8D4BAH40191otLa2Vjvf/1DtPm62djKe49/881fuOWBzEmfz+hvtjgAACDOuK1iS1O6L09cjfmN3DLhErw9ftDsCACDM8LBnAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGPajClZdXZ1effVVHThwQMFg0OpMAAAAYS3qZF9QWFiosrIyffXVV7rmmmu0d+9e5ebmhiIbAABAWDrpDNZLL72kZ555Rn379tXs2bO1a9euUOQCAAAIWyctWCcuCXo8HklSz549rU0EAAAQ5k56ifCqq67SzJkztX//ft16662aOHFiKHIBAACErZMWrPT0dF1++eX6+OOPNWTIEJ1//vmhyAUAABC2TlqwFi5c2PH/27dvV3R0tM466yzNnDlT/fr16/a4hoYGXXfddXr66acVFRWlBQsWyOPxaNiwYVqyZIm8Xu4QAQAA3OmkLefo0aM644wzNHnyZJ199tn68ssv1dbWpuzs7G6PCQQCys3NVa9evSRJK1asUFZWljZu3KhgMKjy8nJzPwEAAIDDnLRgNTY26u6779b48eN15513KhAIKCsrS83Nzd0es3LlSk2fPl1nnHGGJKmqqkqjR4+WJE2YMEE7duwwFB8AAMB5TnqJsKWlRXv27NHQoUO1Z88e+f1+NTU1ye/3d/n1mzdvVlxcnMaPH68nn3xS0jefRDzxKcSYmJgfLGfV1dU/5efo0F0u4FT4/f5TPjdhjxO/E/j3g5txnjvPSQtWbm6u5s+fr4MHD6pXr1669tprVVxcrNtvv73Lr//rX/8qj8ejN998U9XV1crOzlZjY2PH/tbWVsXGxnY7XmJi4k/4Mf7D5/NJOnxK3wP4bz6f75TPTdjjm98Jp/67BXAyznN7VFZWdrvvpJcIL774Yi1dulSXX365jhw5ooaGBs2cOVMpKSldfv2GDRtUWFio9evXKzExUStXrtSECRNUUVEh6ZuF8klJST/xRwEAAHC+bmew2tra9NJLL2nDhg3q2bOnWlpaVF5e3rFw/X+RnZ2txYsXa82aNRoyZEi35QwAAMANui1Yv/rVr/Sb3/xGq1ev1qBBg3TLLbf8z+Vq/fr1Hf9fWFj401MCAACEkW4L1qxZs/Tiiy9q3759mjp1ascjcwAAAPDDul2Dddttt6moqEjp6el68cUX9cEHH2jVqlX6+OOPQ5kPAAAg7Jx0kfvo0aO1atUqlZWV6ayzztJ9990XilwAAABh60c/ryY2Nlbp6enasmWLlXkAAADCHg8EBAAAMIyCBQAAYBgFCwAAwLCTPion3DQ2Nsrrb1CvD1+0OwpcwutvUGNjT7tjAADCCDNYAAAAhrluBisuLk6fNrXp6xG/sTsKXKLXhy8qLi7O7hgAgDDCDBYAAIBhrpvBAiJBXl6eamtr7Y4RFmpqaiRJmZmZNidxvoSEBN4nwBAKFhCGamtr9fEH72pgn+N2R3G82KBHkvR13Ts2J3G2vS097I4AuAoFCwhTA/scV05Si90x4BLL/9HH7giAq7AGCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLMruAAAAdCUvL0+1tbV2xwgLNTU1kqTMzEybkzhfQkJCSN4nChYAwJFqa2u1s2qn1N/uJGHg2+tRO/fttDeH0x0K3VAULACAc/WX2q9otzsFXML7SuhWRrEGCwAAwDAKFgAAgGEULAAAAMMsWYN1/Phx5eTk6NNPP1WPHj20YsUKBYNBLViwQB6PR8OGDdOSJUvk9dLvAACA+1hSsF5++WVJ0nPPPaeKioqOgpWVlaUxY8YoNzdX5eXlSk5OtmJ4AAAAW1kyhTRx4kQ98MADkqT9+/crPj5eVVVVGj16tCRpwoQJ2rFjhxVDAwAA2M6y2zRERUUpOztbZWVlysvL08svvyyPxyNJiomJUXNzc5fHVVdXn9K4fr//lI4HuuL3+0/53DTJ7/ezgBLGOfE8B0wL1Xlu6X2wVq5cqXvvvVc33HCDjh492vF6a2urYmNjuzwmMTHxlMb0+XySDp/S9wD+m8/nO+Vz0ySfz6ev7Q4B13Hiea4mu1PAbUye55WVld3us+SP4C1btmjdunWSpN69e8vj8ejCCy9URUWFJGn79u1KSkqyYmgAAADbWTKDNWnSJC1cuFAzZ87UsWPHtGjRIg0dOlSLFy/WmjVrNGTIEKWkpFgxNAAAgO0sKVg+n0+PPfbY914vLCy0Yjgg4jQ2NurfzT20/B997I4Cl/isuYdOb2y0OwbgGqyTBQAAMIyHPQNhKC4uTr7De5ST1GJ3FLjE8n/0Ua+4OLtjAK7BDBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY5spPEXr9jer14Yt2x3A8T+CIJCkY3dvmJM7m9TdKOsvuGACAMOK6gpWQkGB3hLBRU1MjSRo2lPLww87ivAIA/E9cV7AyMzPtjhA2TrxXeXl5NicBAMBdWIMFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGGuexYhAMAdGhsbpUOS9xXmAmDIIamxd2NIhuKsBQAAMIwZLACAI8XFxemzI5+p/Yp2u6PAJbyveBUXFxeasUIyCgAAQARhBgsIU3tbemj5P/rYHcPxvmrzSJL69QzanMTZ9rb00HC7QwAuQsECwlBCQoLdEcLGv2pqJElnDhpmcxJnGy7OK8AkChYQhjIzM+2OEDZOvFd5eXk2JwEQSViDBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDM+I1GA4GAFi1apH379qmtrU133HGHEhIStGDBAnk8Hg0bNkxLliyR10u3AwAA7mS8YBUVFal///5atWqVmpqadO211+qXv/ylsrKyNGbMGOXm5qq8vFzJycmmhwYAAHAE49NIqampuuuuuzq2e/TooaqqKo0ePVqSNGHCBO3YscP0sAAAAI5hfAYrJiZGktTS0qLMzExlZWVp5cqV8ng8Hfubm5u7Pb66utp0JHTD7/dL4j2Hu3Geh68T/3aASX6/PyS/Dyx52PMXX3yhuXPnasaMGbr66qu1atWqjn2tra2KjY3t9tjExEQrIqELPp9PEu853I3zPHz5fD6pye4UcBufz2fs90FlZWW3+4xfIqyvr9ecOXM0f/58TZ06VZI0YsQIVVRUSJK2b9+upKQk08MCAAA4hvGCtXbtWh0+fFiPP/640tPTlZ6erqysLOXn52vatGkKBAJKSUkxPSwAAIBjGL9EmJOTo5ycnO+9XlhYaHooAAAAR+JmVAAAAIZRsAAAAAyjYAEAABhmyW0aAAAw4pDkfYW5gJP6+tv/9rI1hfMdknR2aIaiYAEAHCkhIcHuCGGjpqZGkjTs7GE2J3G4s0N3XlGwAACOlJmZaXeEsHHivcrLy7M5CU5g3hUAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMs6xg7dq1S+np6ZKkzz77TDfeeKNmzJihJUuWqL293aphAQAAbGdJwXrqqaeUk5Ojo0ePSpJWrFihrKwsbdy4UcFgUOXl5VYMCwAA4AiWFKyBAwcqPz+/Y7uqqkqjR4+WJE2YMEE7duywYlgAAABHiLLim6akpOjzzz/v2A4Gg/J4PJKkmJgYNTc3d3tsdXW1FZHQBb/fL4n3HO7GeY5IwHnuPJYUrP/m9f5noqy1tVWxsbHdfm1iYmIoIkGSz+eTxHsOd+M8RyTgPLdHZWVlt/tC8inCESNGqKKiQpK0fft2JSUlhWJYAAAAW4SkYGVnZys/P1/Tpk1TIBBQSkpKKIYFAACwhWWXCM855xxt2rRJkjR48GAVFhZaNRQAAICjcKNRAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYFmV3AADuUVpaquLiYrtjdFJTUyNJyszMtDlJZ5MnT1ZqaqrdMQBYhIIFwNUGDBhgdwQAEYiCBcCY1NRUx83KvP3227rvvvt09913a9SoUXbHARAhWIMFwNWWLl2q9vZ2LV682O4oACIIBQuAa7399ttqaWmRJLW0tKiystLmRAAiBQULgGstXbq00zazWABChYIFwLVOzF51tw0AVqFgAXCtPn36/OA2AFiFggXAtW699dZO27fffrtNSQBEGgoWANd64YUXOm0///zzNiUBEGkoWABcq66u7ge3AcAqFCwArjVo0KAf3AYAq1CwALjWrFmzOm3fdNNNNiUBEGkoWABc649//GOn7XXr1tmUBECk4VmEIVJaWqri4mK7Y3RSU1MjScrMzLQ5SWeTJ0923PPsEJ7279//g9sAYBUKVgQbMGCA3REAAHClkBWs9vZ2LV26VP/85z/Vs2dPLV++XOedd16ohrddamoqszIA4AJckfjxIvmKRMjWYP39739XW1ub/vznP+uee+7Rww8/HKqhAUSoK664otP2lVdeaU8QwGIDBgzgqoTDhGwGq7KyUuPHj5ckjRw5Uh988EGohgYQoTIzM/XKK6902gZOFVck8GOErGC1tLR0eg5Yjx49dOzYMUVFdY5QXV0dqkgAIsCll16qd999V6NGjdLBgwd18OBBuyMBiAAhK1h9+vRRa2trx3Z7e/v3ypUkJSYmhioSgAiQk5OjZcuWKScnh0soAIyqrKzsdl/I1mBdeuml2r59uyTpvffe0/Dhw0M1NIAIFh8fr/z8fMoVgJAK2QxWcnKy3njjDU2fPl3BYFAPPfRQqIYGAAAIqZAVLK/Xq/vvvz9UwwEAANiGR+UAAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMCxkj8r5sX7oydQAAADhwBMMBoN2hwAAAHATLhECAAAYRsECAAAwjIIVwXbt2qX09HS7YwCWCAQCmj9/vmbMmKGpU6eqvLzc7kiAccePH9fChQs1ffp0zZw5U3v37rU7Er7luEXuCI2nnnpKRUVF6t27t91RAEsUFRWpf//+WrVqlZqamnTttdfq17/+td2xAKNefvllSdJzzz2niooKrVixQk888YTNqSAxgxWxBg4cqPz8fLtjAJZJTU3VXXfd1bHdo0cPG9MA1pg4caIeeOABSdL+/fsVHx9vcyKcwAxWhEpJSdHnn39udwzAMjExMZKklpYWZWZmKisry+ZEgDWioqKUnZ2tsrIy5eXl2R0H32IGC4BrffHFF5o1a5amTJmiq6++2u44gGVWrlyprVu3avHixfL7/XbHgShYAFyqvr5ec+bM0fz58zV16lS74wCW2LJli9atWydJ6t27tzweD5fDHYKCBcCV1q5dq8OHD+vxxx9Xenq60tPT9fXXX9sdCzBq0qRJ+vDDDzVz5kzdfPPNWrRokU477TS7Y0HcyR0AAMD766DYAAACfUlEQVQ4ZrAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWgLBRUVGhyy67rOO2CzfccIPWr1/f5demp6drz549IU4IAN/gUTkAwsrYsWP16KOPSpLa2tqUmpqqKVOmKDY21uZkAPAfFCwAYaulpUVer1cfffSRVq9erWAwqDPPPFOrV6/u+JoDBw5o6dKlOnr0qA4dOqS5c+dq4sSJevTRR/XWW2+pvb1dV111lWbPnq0NGzZoy5Yt8nq9uvTSS5WdnW3jTwcgnFGwAISVt956S+np6fJ4PIqOjtbixYu1fPlyPfrooxo6dKg2bNjQ6dLgJ598optuukljxozRu+++q/z8fE2cOFFbtmxRYWGhzjzzTG3evFmStHnzZi1evFgjR47Uxo0bdezYMUVF8WsSwP+O3xwAwsp3LxGesGjRIg0dOlSSNHPmzE77Tj/9dD3xxBN6/vnn5fF4dOzYMUnSmjVrtGbNGtXX12v8+PGSpBUrVujpp5/W6tWrNXLkSPGgCwA/FYvcAYS9M844Q3V1dZKkJ598UmVlZR37HnvsMU2ZMkWrVq3SmDFjFAwG1dbWptLSUq1Zs0YFBQV64YUXtG/fPm3atEnLli1TYWGhqqurtXPnTpt+IgDhjhksAGFv2bJlWrRokbxer04//XTNnj1bf/rTnyRJqampevDBB7Vu3Tr9/Oc/V1NTk3r27Kl+/fppypQp6tevn/7v//5Pv/jFL3T++edr6tSp+tnPfqYzzzxTl1xyic0/GYBwxcOeAQAADOMSIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCw/wf2BJ+21j53aQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='Pclass', y='Age', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "    else:\n",
    "        return Age\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age' ]= train[['Age', 'Pclass']].apply(impute_age, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f38339a5f8>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f38321f6d8>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting to categorical features\n",
    "**Creating a dummy vaiable **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(train['Sex'], drop_first=True)# weather or not this person is a male "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "embark = pd.get_dummies(train['Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.concat ([train, sex, embark], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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>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",
       "      <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>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",
       "      <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>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>S</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>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 14 columns</p>\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",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "5                                     Moran, Mr. James    male  24.0      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                      Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                                Rice, Master. Eugene    male   2.0      4   \n",
       "17                        Williams, Mr. Charles Eugene    male  29.0      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female  24.0      0   \n",
       "20                                Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                             Emir, Mr. Farred Chehab    male  24.0      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  24.0      0   \n",
       "29                                 Todoroff, Mr. Lalio    male  24.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.0      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  24.0      8   \n",
       "864                             Gill, Mr. John William    male  24.0      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.0      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.0      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male  24.0      0   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.0      1   \n",
       "870                                  Balkic, Mr. Cerin    male  26.0      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.0      0   \n",
       "877                               Petroff, Mr. Nedelio    male  19.0      0   \n",
       "878                                 Laleff, Mr. Kristo    male  24.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "881                                 Markun, Mr. Johann    male  33.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "883                      Banfield, Mr. Frederick James    male  28.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  24.0      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket      Fare Embarked  male  Q  S  \n",
       "0        0         A/5 21171    7.2500        S     1  0  1  \n",
       "1        0          PC 17599   71.2833        C     0  0  0  \n",
       "2        0  STON/O2. 3101282    7.9250        S     0  0  1  \n",
       "3        0            113803   53.1000        S     0  0  1  \n",
       "4        0            373450    8.0500        S     1  0  1  \n",
       "5        0            330877    8.4583        Q     1  1  0  \n",
       "6        0             17463   51.8625        S     1  0  1  \n",
       "7        1            349909   21.0750        S     1  0  1  \n",
       "8        2            347742   11.1333        S     0  0  1  \n",
       "9        0            237736   30.0708        C     0  0  0  \n",
       "10       1           PP 9549   16.7000        S     0  0  1  \n",
       "11       0            113783   26.5500        S     0  0  1  \n",
       "12       0         A/5. 2151    8.0500        S     1  0  1  \n",
       "13       5            347082   31.2750        S     1  0  1  \n",
       "14       0            350406    7.8542        S     0  0  1  \n",
       "15       0            248706   16.0000        S     0  0  1  \n",
       "16       1            382652   29.1250        Q     1  1  0  \n",
       "17       0            244373   13.0000        S     1  0  1  \n",
       "18       0            345763   18.0000        S     0  0  1  \n",
       "19       0              2649    7.2250        C     0  0  0  \n",
       "20       0            239865   26.0000        S     1  0  1  \n",
       "21       0            248698   13.0000        S     1  0  1  \n",
       "22       0            330923    8.0292        Q     0  1  0  \n",
       "23       0            113788   35.5000        S     1  0  1  \n",
       "24       1            349909   21.0750        S     0  0  1  \n",
       "25       5            347077   31.3875        S     0  0  1  \n",
       "26       0              2631    7.2250        C     1  0  0  \n",
       "27       2             19950  263.0000        S     1  0  1  \n",
       "28       0            330959    7.8792        Q     0  1  0  \n",
       "29       0            349216    7.8958        S     1  0  1  \n",
       "..     ...               ...       ...      ...   ... .. ..  \n",
       "861      0             28134   11.5000        S     1  0  1  \n",
       "862      0             17466   25.9292        S     0  0  1  \n",
       "863      2          CA. 2343   69.5500        S     0  0  1  \n",
       "864      0            233866   13.0000        S     1  0  1  \n",
       "865      0            236852   13.0000        S     0  0  1  \n",
       "866      0     SC/PARIS 2149   13.8583        C     0  0  0  \n",
       "867      0          PC 17590   50.4958        S     1  0  1  \n",
       "868      0            345777    9.5000        S     1  0  1  \n",
       "869      1            347742   11.1333        S     1  0  1  \n",
       "870      0            349248    7.8958        S     1  0  1  \n",
       "871      1             11751   52.5542        S     0  0  1  \n",
       "872      0               695    5.0000        S     1  0  1  \n",
       "873      0            345765    9.0000        S     1  0  1  \n",
       "874      0         P/PP 3381   24.0000        C     0  0  0  \n",
       "875      0              2667    7.2250        C     0  0  0  \n",
       "876      0              7534    9.8458        S     1  0  1  \n",
       "877      0            349212    7.8958        S     1  0  1  \n",
       "878      0            349217    7.8958        S     1  0  1  \n",
       "879      1             11767   83.1583        C     0  0  0  \n",
       "880      1            230433   26.0000        S     0  0  1  \n",
       "881      0            349257    7.8958        S     1  0  1  \n",
       "882      0              7552   10.5167        S     0  0  1  \n",
       "883      0  C.A./SOTON 34068   10.5000        S     1  0  1  \n",
       "884      0   SOTON/OQ 392076    7.0500        S     1  0  1  \n",
       "885      5            382652   29.1250        Q     0  1  0  \n",
       "886      0            211536   13.0000        S     1  0  1  \n",
       "887      0            112053   30.0000        S     0  0  1  \n",
       "888      2        W./C. 6607   23.4500        S     0  0  1  \n",
       "889      0            111369   30.0000        C     1  0  0  \n",
       "890      0            370376    7.7500        Q     1  1  0  \n",
       "\n",
       "[889 rows x 14 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex', 'Embarked', 'Name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Ticket', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train.drop('PassengerId',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('train_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now time to apply Logistic Regression using python\n",
    "\n",
    "\n",
    "** Gonna tell us weather or not a Passenger survived on titanic by examine the features of them**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = train.drop('Survived', axis=1)\n",
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th>257</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
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       "2    1\n",
       "3    1\n",
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       "6    0\n",
       "7    0\n",
       "8    0\n",
       "9    1\n",
       "10   1\n",
       "11   1\n",
       "12   0\n",
       "13   1\n",
       "14   0\n",
       "15   0\n",
       "16   1\n",
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       "18   1\n",
       "19   0\n",
       "20   0\n",
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       "22   0\n",
       "23   0\n",
       "24   0\n",
       "25   0\n",
       "26   1\n",
       "27   1\n",
       "28   0\n",
       "29   0\n",
       "..  ..\n",
       "237  0\n",
       "238  1\n",
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       "240  0\n",
       "241  0\n",
       "242  0\n",
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       "244  1\n",
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       "255  0\n",
       "256  0\n",
       "257  0\n",
       "258  0\n",
       "259  1\n",
       "260  1\n",
       "261  0\n",
       "262  0\n",
       "263  0\n",
       "264  0\n",
       "265  1\n",
       "266  1\n",
       "\n",
       "[267 rows x 1 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.91      0.85       163\n",
      "          1       0.82      0.65      0.73       104\n",
      "\n",
      "avg / total       0.81      0.81      0.80       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[148,  15],\n",
       "       [ 36,  68]], dtype=int64)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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