Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Cabin</th>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <td>54.0</td>\n",
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       "      <td>51.8625</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
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       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
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       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <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",
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       "      <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>G6</td>\n",
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       "      <th>11</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
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       "      <td>S</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
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       "      <td>15</td>\n",
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       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
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       "      <td>4</td>\n",
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       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
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       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <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",
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       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
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       "      <td>Fynney, Mr. Joseph J</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
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       "      <td>23</td>\n",
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       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
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       "      <td>25</td>\n",
       "      <td>0</td>\n",
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       "      <td>Palsson, Miss. Torborg Danira</td>\n",
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       "      <td>1</td>\n",
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       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>26</td>\n",
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       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
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       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
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       "    <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",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
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       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>C</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>A24</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>D35</td>\n",
       "      <td>S</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>B51 B53 B55</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>C</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>NaN</td>\n",
       "      <td>C</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>C50</td>\n",
       "      <td>C</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>S</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>NaN</td>\n",
       "      <td>Q</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>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 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   NaN      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   NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female   NaN      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   NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male   NaN      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   NaN      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   NaN      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   NaN      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   NaN      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        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  \n",
       "5        0            330877    8.4583          NaN        Q  \n",
       "6        0             17463   51.8625          E46        S  \n",
       "7        1            349909   21.0750          NaN        S  \n",
       "8        2            347742   11.1333          NaN        S  \n",
       "9        0            237736   30.0708          NaN        C  \n",
       "10       1           PP 9549   16.7000           G6        S  \n",
       "11       0            113783   26.5500         C103        S  \n",
       "12       0         A/5. 2151    8.0500          NaN        S  \n",
       "13       5            347082   31.2750          NaN        S  \n",
       "14       0            350406    7.8542          NaN        S  \n",
       "15       0            248706   16.0000          NaN        S  \n",
       "16       1            382652   29.1250          NaN        Q  \n",
       "17       0            244373   13.0000          NaN        S  \n",
       "18       0            345763   18.0000          NaN        S  \n",
       "19       0              2649    7.2250          NaN        C  \n",
       "20       0            239865   26.0000          NaN        S  \n",
       "21       0            248698   13.0000          D56        S  \n",
       "22       0            330923    8.0292          NaN        Q  \n",
       "23       0            113788   35.5000           A6        S  \n",
       "24       1            349909   21.0750          NaN        S  \n",
       "25       5            347077   31.3875          NaN        S  \n",
       "26       0              2631    7.2250          NaN        C  \n",
       "27       2             19950  263.0000  C23 C25 C27        S  \n",
       "28       0            330959    7.8792          NaN        Q  \n",
       "29       0            349216    7.8958          NaN        S  \n",
       "..     ...               ...       ...          ...      ...  \n",
       "861      0             28134   11.5000          NaN        S  \n",
       "862      0             17466   25.9292          D17        S  \n",
       "863      2          CA. 2343   69.5500          NaN        S  \n",
       "864      0            233866   13.0000          NaN        S  \n",
       "865      0            236852   13.0000          NaN        S  \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "867      0          PC 17590   50.4958          A24        S  \n",
       "868      0            345777    9.5000          NaN        S  \n",
       "869      1            347742   11.1333          NaN        S  \n",
       "870      0            349248    7.8958          NaN        S  \n",
       "871      1             11751   52.5542          D35        S  \n",
       "872      0               695    5.0000  B51 B53 B55        S  \n",
       "873      0            345765    9.0000          NaN        S  \n",
       "874      0         P/PP 3381   24.0000          NaN        C  \n",
       "875      0              2667    7.2250          NaN        C  \n",
       "876      0              7534    9.8458          NaN        S  \n",
       "877      0            349212    7.8958          NaN        S  \n",
       "878      0            349217    7.8958          NaN        S  \n",
       "879      1             11767   83.1583          C50        C  \n",
       "880      1            230433   26.0000          NaN        S  \n",
       "881      0            349257    7.8958          NaN        S  \n",
       "882      0              7552   10.5167          NaN        S  \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "885      5            382652   29.1250          NaN        Q  \n",
       "886      0            211536   13.0000          NaN        S  \n",
       "887      0            112053   30.0000          B42        S  \n",
       "888      2        W./C. 6607   23.4500          NaN        S  \n",
       "889      0            111369   30.0000         C148        C  \n",
       "890      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {\n",
    "    'size': 'xx-large',\n",
    "    'weight': 'bold'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'CORELATIONS')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(data.corr(),annot = True, linewidths=1,\n",
    "           linecolor='w')\n",
    "plt.xlabel('Columns',fontdict=font)\n",
    "plt.ylabel('ROWS',fontdict=font)\n",
    "plt.title('CORELATIONS', fontdict=font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_data = data[['PassengerId','Pclass','Fare']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "survival_data = data['Survived'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, x_test, Y, y_test = train_test_split(numerical_data,\n",
    "                                       survival_data,\n",
    "                                       test_size=0.3,\n",
    "                                       random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Akash Malik\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "coefficients = model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff_dict = {\n",
    "    'PassengerId': coefficients[0][0],\n",
    "    'Pclass': coefficients[0][1],\n",
    "    'Fare' : coefficients[0][2]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "coeff_dict = pd.Series(coeff_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cOXPytre9LUnym7/5m7npppuGNzEAwChQ85mwZcuWpa2tLR0dHZk2bVrWrl07aM2Pf/zjzJ07Nxs3bszGjRsFGADA/1dThHV3d2fr1q2ZOXNmkmT+/Pnp6OgYtO6RRx7J448/nrPPPjuLFi3KT3/60+FNCwAwStR0OXL37t1pbGxMQ8MrD69Wq+ns7By0bvz48Zk3b17OO++83HffffnkJz+ZTZs25Ygjjjjo55o8ubGWERmGarWp9AjA67B/MhTeL4e2A0bY5s2bs3LlygHbpkyZkkqlMmDbr99OkgsuuKD/z2eeeWZWr16d7du35+STTz7oAXfteiG9vX0HvZ7hqVab0tX1fOkxOAw4uJdh/+RgOZ6PrLq6ypBPHB0wwlpbW9Pa2jpgW3d3d1paWtLT05P6+vp0dXWlubl50GNvvfXWzJkzJ5MmTUqS9PX19Z89AwAYy2r6TNi4ceMyffr0bNq0KUmyYcOGzJgxY9C6rVu35vbbb0+SPPTQQ+nt7c2JJ544jHEBAEaHmn86cunSpVm/fn1mz56dbdu2ZcmSJUmSdevW5dprr02StLe354EHHsicOXPy5S9/OatXr05dnV9NBgBQ6evrO6Q/cOUzYSPLZwg4WNVqU7avWFB6jDHlxPY77J8cNMfzkVXLZ8KclgIAKECEAQAUIMIAAAoQYQAABYgwAIACRBgAQAEiDACgABEGAFCACAMAKECEAQAUIMIAAAoQYQAABYgwAIACRBgAQAEiDACgABEGAFCACAMAKECEAQAUIMIAAAoQYQAABYgwAIACRBgAQAEiDACggIbSAwCHp/37Xs6J7XeUHmNM2b/v5dIjAG8gEQbUZPd/70uyr/QYNalWm9LV9XzpMYAxzuVIAIACRBgAQAEiDACgABEGAFCACAMAKECEAQAUIMIAAAoQYQAABYgwAIACDvnfmF9XVyk9wpjjNWcs8D5nLPA+Hzm1vNaVvr6+vjdhFgAA/g8uRwIAFCDCAAAKEGEAAAWIMACAAkQYAEABIgwAoAARBgBQgAgDAChAhAEAFHDI/7NFY9VTTz2VWbNm5Xd+53dSqVTS3d2d5ubmrFy5Mscee2zp8Ybs+uuvT5JccMEFA7Y/9dRTWbRoUb7//e+XGIvD1FD3j+985zt56KGHcsUVVxSYFt44r37vv9oNN9yQt771rYWmolYi7BDW3NycjRs39t++4oorsmrVqlx99dUFp4JDg/2DserX3/scvkTYYaSlpSVXX311Nm/enG984xt56aWXsm/fvnzpS1/Ke97znnzjG9/InXfembq6upxyyin5whe+kMceeyyXXXZZ9u/fn/Hjx2flypV5+9vfni1btuS6667L/v3781u/9VtZvnx5Jk2alLPOOivz5s3LD37wg7z44ov58pe/nGnTpuXxxx/PxRdfnJ6enkyfPj1btmzJ9773vTzzzDO57LLL8stf/jKVSiUXXXRR3vve9+b666/Pww8/nF/84hf50z/90wHfx09+8pO0t7cnSU4++eQSLyWj0K/2jwceeCBXXHFF+vr6ctxxx2X16tUD1r0R+w8cah5//PEsX748e/fuzbPPPpu/+qu/yvnnnz/oWHzGGWfk8ssvz3PPPZcjjzwyn//85/Oud72r9Phjlgg7THR3d+e73/1uTj311HzrW9/KDTfckLe85S25/fbb87WvfS1r1qzJjTfemPvuuy/19fVpb29PZ2dnbrnllnzsYx9La2tr7rzzzjz88MM56qijsnr16nzzm9/M0UcfnW9961u56qqrsmLFiiTJxIkTc/vtt+fWW2/NjTfemOuvvz4XX3xxLrzwwpx55pm5+eab09PTkyRZsWJFFixYkPe///3ZuXNn2trasmHDhiTJvn37smnTpiT/ezkySf7hH/4hF198cc4444ysWbMmDz744Ai/mow2v9o/pk2bls985jO56aabMnXq1KxevTp33nlnJkyYkCTp7e0d9v4jwiht586dOfvss/tvz507N52dnfmbv/mb/P7v/36efPLJzJs3L+eff36Sgcfi8847L5dddlne9a535YknnsgnP/nJfPe73y3yfSDCDmmv3tH27duXU045JRdddFEaGhry/e9/Pz//+c/z0EMPpa6uLvX19Xn3u9+dc845J+9///vzsY99LMccc0zOPPPMfOELX8h9992Xs846K+973/uyZcuW/OIXv8iiRYuSvPIX09FHH93/vH/4h3+YJHnnO9+Zu+++O88991yefvrpnHnmmUmSBQsW5Jvf/GaS5IEHHsj27dtz3XXXJUn279+fJ598MklyyimnDPqenn322ezcuTNnnHFGkmT+/Pm544473oyXj1HutfaPtra2PPbYY5k6dWqS5KKLLkryymfCkqSuri5r1qwZ1v4Dpb3W5cienp7cd999ufHGG/P4449n7969/ff96li8Z8+e/PjHP84ll1zSf9/evXuze/fuTJo0aWSGZwARdgh7rR1tz549WbBgQebNm5fTTjstJ510Um677bYkydq1a/Pwww9ny5Yt+cu//MtcddVVmTVrVt797nfnP/7jP3LzzTfnnnvuyR/90R/lPe95T2644YYkycsvv5w9e/b0P8f48eOTJJVKJUlSX1+fvr6+15yxt7c3t9xySyZOnJjklb8YJ0+enH//93/PkUceOWh9pVIZ8LXq6+trfXkY415r/3jsscf637dJ8vzzzw94b+/ZsyfnnHPOsPafL37xiyPzDcIQLFmyJEcddVTe9773Zfbs2fm3f/u3/vt+dSzu7e3NEUccMWC/+eUvf9l//Gbk+RUVh5n//M//TKVSyV//9V+npaUl3/ve99LT05Nnn302s2fPzu/+7u/mwgsvzBlnnJGf/vSnWbJkSR555JGcd955ufDCC/OTn/wkv/d7v5eHH344P//5z5O88pfPqlWrXvc5m5qa8tu//du59957kyR33XVX/32nn356/uVf/iVJ8sQTT2Tu3Ll58cUXX/drTZo0Kccdd1zuueeeJBlwoIDhOuGEE7Jr16488cQTSZKvf/3rWbduXf/9b8T+A4ei+++/P5/+9KfzgQ98IFu2bEmS/o+N/EpTU1Pe/va390fY/fffn4ULF474rPwvZ8IOMyeffHKmTp2a1tbWVCqV/MEf/EF+9KMf5S1veUvOPffcnHPOOfmN3/iNnHDCCVmwYEFOO+20tLe3Z82aNRk3blwuv/zyVKvVfOlLX8qSJUvS29ubY445JldeeeX/+byrVq3KpZdemq985Ss56aST+v/P6nOf+1wuu+yyzJ07t39dY2Pj//m1rrzyylxyySX5yle+klNPPfWNeWEgr5zFvfLKK/P3f//36e7uztve9rasWrWq/zMvb8T+A4eiCy64IG1tbRk/fnxOPvnkHH/88XnqqacGrbvyyitz+eWX5+tf/3rGjRuXa665ZsDZY0ZWpe/1rjPBq/zjP/5jPvKRj6S5uTl333137rrrrgEftgcAhsaZMA7Kcccdl49//ONpaGjIUUcd1f+TlABAbZwJAwAowAfzAQAKEGEAAAWIMACAAkQYAEABIgwAoAARBgBQwP8DDl3S3t0Fbt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "coeff_dict.plot(kind='bar')\n",
    "plt.xticks(rotation=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion = confusion_matrix(predictions, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[134,  58],\n",
       "       [ 23,  53]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = accuracy_score(predictions,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6977611940298507"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = data_test[['PassengerId','Pclass','Fare']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions =  model.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test['Fare'] = data_test['Fare'].fillna(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Fare           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_df ={\n",
    "    'PassengerId' : data_test['PassengerId'],\n",
    "    'Survived' : test_predictions\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_df = pd.DataFrame(test_predictions_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_df = test_predictions_df.set_index('PassengerId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>0</td>\n",
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       "      <th>893</th>\n",
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       "    <tr>\n",
       "      <th>894</th>\n",
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       "      <th>895</th>\n",
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       "      <th>896</th>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "PassengerId          \n",
       "892                 0\n",
       "893                 0\n",
       "894                 0\n",
       "895                 0\n",
       "896                 0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions_df.to_csv('a.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  }
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