Assignment- Data Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import SelectKBest, chi2, RFE\n",
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('train.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
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       "      <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": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      S\n",
       "1      C\n",
       "2      S\n",
       "3      S\n",
       "4      S\n",
       "5      Q\n",
       "6      S\n",
       "7      S\n",
       "8      S\n",
       "9      C\n",
       "10     S\n",
       "11     S\n",
       "12     S\n",
       "13     S\n",
       "14     S\n",
       "15     S\n",
       "16     Q\n",
       "17     S\n",
       "18     S\n",
       "19     C\n",
       "20     S\n",
       "21     S\n",
       "22     Q\n",
       "23     S\n",
       "24     S\n",
       "25     S\n",
       "26     C\n",
       "27     S\n",
       "28     Q\n",
       "29     S\n",
       "      ..\n",
       "861    S\n",
       "862    S\n",
       "863    S\n",
       "864    S\n",
       "865    S\n",
       "866    C\n",
       "867    S\n",
       "868    S\n",
       "869    S\n",
       "870    S\n",
       "871    S\n",
       "872    S\n",
       "873    S\n",
       "874    C\n",
       "875    C\n",
       "876    S\n",
       "877    S\n",
       "878    S\n",
       "879    C\n",
       "880    S\n",
       "881    S\n",
       "882    S\n",
       "883    S\n",
       "884    S\n",
       "885    Q\n",
       "886    S\n",
       "887    S\n",
       "888    S\n",
       "889    C\n",
       "890    Q\n",
       "Name: Embarked, Length: 891, dtype: object"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Embarked'].fillna('S')\n",
    "#Removing null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=train.drop(['Name','Ticket','Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</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>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</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>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</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>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8583</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>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.4958</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</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>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1333</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>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</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>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</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>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</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>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0000</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>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0000</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>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</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>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8458</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>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</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>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</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>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</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>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</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>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5167</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>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</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>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0500</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>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</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>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</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>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</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>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</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>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch      Fare  \\\n",
       "0              1         0       3    male  22.0      1      0    7.2500   \n",
       "1              2         1       1  female  38.0      1      0   71.2833   \n",
       "2              3         1       3  female  26.0      0      0    7.9250   \n",
       "3              4         1       1  female  35.0      1      0   53.1000   \n",
       "4              5         0       3    male  35.0      0      0    8.0500   \n",
       "5              6         0       3    male   NaN      0      0    8.4583   \n",
       "6              7         0       1    male  54.0      0      0   51.8625   \n",
       "7              8         0       3    male   2.0      3      1   21.0750   \n",
       "8              9         1       3  female  27.0      0      2   11.1333   \n",
       "9             10         1       2  female  14.0      1      0   30.0708   \n",
       "10            11         1       3  female   4.0      1      1   16.7000   \n",
       "11            12         1       1  female  58.0      0      0   26.5500   \n",
       "12            13         0       3    male  20.0      0      0    8.0500   \n",
       "13            14         0       3    male  39.0      1      5   31.2750   \n",
       "14            15         0       3  female  14.0      0      0    7.8542   \n",
       "15            16         1       2  female  55.0      0      0   16.0000   \n",
       "16            17         0       3    male   2.0      4      1   29.1250   \n",
       "17            18         1       2    male   NaN      0      0   13.0000   \n",
       "18            19         0       3  female  31.0      1      0   18.0000   \n",
       "19            20         1       3  female   NaN      0      0    7.2250   \n",
       "20            21         0       2    male  35.0      0      0   26.0000   \n",
       "21            22         1       2    male  34.0      0      0   13.0000   \n",
       "22            23         1       3  female  15.0      0      0    8.0292   \n",
       "23            24         1       1    male  28.0      0      0   35.5000   \n",
       "24            25         0       3  female   8.0      3      1   21.0750   \n",
       "25            26         1       3  female  38.0      1      5   31.3875   \n",
       "26            27         0       3    male   NaN      0      0    7.2250   \n",
       "27            28         0       1    male  19.0      3      2  263.0000   \n",
       "28            29         1       3  female   NaN      0      0    7.8792   \n",
       "29            30         0       3    male   NaN      0      0    7.8958   \n",
       "..           ...       ...     ...     ...   ...    ...    ...       ...   \n",
       "861          862         0       2    male  21.0      1      0   11.5000   \n",
       "862          863         1       1  female  48.0      0      0   25.9292   \n",
       "863          864         0       3  female   NaN      8      2   69.5500   \n",
       "864          865         0       2    male  24.0      0      0   13.0000   \n",
       "865          866         1       2  female  42.0      0      0   13.0000   \n",
       "866          867         1       2  female  27.0      1      0   13.8583   \n",
       "867          868         0       1    male  31.0      0      0   50.4958   \n",
       "868          869         0       3    male   NaN      0      0    9.5000   \n",
       "869          870         1       3    male   4.0      1      1   11.1333   \n",
       "870          871         0       3    male  26.0      0      0    7.8958   \n",
       "871          872         1       1  female  47.0      1      1   52.5542   \n",
       "872          873         0       1    male  33.0      0      0    5.0000   \n",
       "873          874         0       3    male  47.0      0      0    9.0000   \n",
       "874          875         1       2  female  28.0      1      0   24.0000   \n",
       "875          876         1       3  female  15.0      0      0    7.2250   \n",
       "876          877         0       3    male  20.0      0      0    9.8458   \n",
       "877          878         0       3    male  19.0      0      0    7.8958   \n",
       "878          879         0       3    male   NaN      0      0    7.8958   \n",
       "879          880         1       1  female  56.0      0      1   83.1583   \n",
       "880          881         1       2  female  25.0      0      1   26.0000   \n",
       "881          882         0       3    male  33.0      0      0    7.8958   \n",
       "882          883         0       3  female  22.0      0      0   10.5167   \n",
       "883          884         0       2    male  28.0      0      0   10.5000   \n",
       "884          885         0       3    male  25.0      0      0    7.0500   \n",
       "885          886         0       3  female  39.0      0      5   29.1250   \n",
       "886          887         0       2    male  27.0      0      0   13.0000   \n",
       "887          888         1       1  female  19.0      0      0   30.0000   \n",
       "888          889         0       3  female   NaN      1      2   23.4500   \n",
       "889          890         1       1    male  26.0      0      0   30.0000   \n",
       "890          891         0       3    male  32.0      0      0    7.7500   \n",
       "\n",
       "    Embarked  \n",
       "0          S  \n",
       "1          C  \n",
       "2          S  \n",
       "3          S  \n",
       "4          S  \n",
       "5          Q  \n",
       "6          S  \n",
       "7          S  \n",
       "8          S  \n",
       "9          C  \n",
       "10         S  \n",
       "11         S  \n",
       "12         S  \n",
       "13         S  \n",
       "14         S  \n",
       "15         S  \n",
       "16         Q  \n",
       "17         S  \n",
       "18         S  \n",
       "19         C  \n",
       "20         S  \n",
       "21         S  \n",
       "22         Q  \n",
       "23         S  \n",
       "24         S  \n",
       "25         S  \n",
       "26         C  \n",
       "27         S  \n",
       "28         Q  \n",
       "29         S  \n",
       "..       ...  \n",
       "861        S  \n",
       "862        S  \n",
       "863        S  \n",
       "864        S  \n",
       "865        S  \n",
       "866        C  \n",
       "867        S  \n",
       "868        S  \n",
       "869        S  \n",
       "870        S  \n",
       "871        S  \n",
       "872        S  \n",
       "873        S  \n",
       "874        C  \n",
       "875        C  \n",
       "876        S  \n",
       "877        S  \n",
       "878        S  \n",
       "879        C  \n",
       "880        S  \n",
       "881        S  \n",
       "882        S  \n",
       "883        S  \n",
       "884        S  \n",
       "885        Q  \n",
       "886        S  \n",
       "887        S  \n",
       "888        S  \n",
       "889        C  \n",
       "890        Q  \n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=pd.get_dummies(data)\n",
    "#One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_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>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>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>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Age  SibSp  Parch      Fare  Sex_female  \\\n",
       "0              1         0       3  22.0      1      0    7.2500           0   \n",
       "1              2         1       1  38.0      1      0   71.2833           1   \n",
       "2              3         1       3  26.0      0      0    7.9250           1   \n",
       "3              4         1       1  35.0      1      0   53.1000           1   \n",
       "4              5         0       3  35.0      0      0    8.0500           0   \n",
       "5              6         0       3   NaN      0      0    8.4583           0   \n",
       "6              7         0       1  54.0      0      0   51.8625           0   \n",
       "7              8         0       3   2.0      3      1   21.0750           0   \n",
       "8              9         1       3  27.0      0      2   11.1333           1   \n",
       "9             10         1       2  14.0      1      0   30.0708           1   \n",
       "10            11         1       3   4.0      1      1   16.7000           1   \n",
       "11            12         1       1  58.0      0      0   26.5500           1   \n",
       "12            13         0       3  20.0      0      0    8.0500           0   \n",
       "13            14         0       3  39.0      1      5   31.2750           0   \n",
       "14            15         0       3  14.0      0      0    7.8542           1   \n",
       "15            16         1       2  55.0      0      0   16.0000           1   \n",
       "16            17         0       3   2.0      4      1   29.1250           0   \n",
       "17            18         1       2   NaN      0      0   13.0000           0   \n",
       "18            19         0       3  31.0      1      0   18.0000           1   \n",
       "19            20         1       3   NaN      0      0    7.2250           1   \n",
       "20            21         0       2  35.0      0      0   26.0000           0   \n",
       "21            22         1       2  34.0      0      0   13.0000           0   \n",
       "22            23         1       3  15.0      0      0    8.0292           1   \n",
       "23            24         1       1  28.0      0      0   35.5000           0   \n",
       "24            25         0       3   8.0      3      1   21.0750           1   \n",
       "25            26         1       3  38.0      1      5   31.3875           1   \n",
       "26            27         0       3   NaN      0      0    7.2250           0   \n",
       "27            28         0       1  19.0      3      2  263.0000           0   \n",
       "28            29         1       3   NaN      0      0    7.8792           1   \n",
       "29            30         0       3   NaN      0      0    7.8958           0   \n",
       "..           ...       ...     ...   ...    ...    ...       ...         ...   \n",
       "861          862         0       2  21.0      1      0   11.5000           0   \n",
       "862          863         1       1  48.0      0      0   25.9292           1   \n",
       "863          864         0       3   NaN      8      2   69.5500           1   \n",
       "864          865         0       2  24.0      0      0   13.0000           0   \n",
       "865          866         1       2  42.0      0      0   13.0000           1   \n",
       "866          867         1       2  27.0      1      0   13.8583           1   \n",
       "867          868         0       1  31.0      0      0   50.4958           0   \n",
       "868          869         0       3   NaN      0      0    9.5000           0   \n",
       "869          870         1       3   4.0      1      1   11.1333           0   \n",
       "870          871         0       3  26.0      0      0    7.8958           0   \n",
       "871          872         1       1  47.0      1      1   52.5542           1   \n",
       "872          873         0       1  33.0      0      0    5.0000           0   \n",
       "873          874         0       3  47.0      0      0    9.0000           0   \n",
       "874          875         1       2  28.0      1      0   24.0000           1   \n",
       "875          876         1       3  15.0      0      0    7.2250           1   \n",
       "876          877         0       3  20.0      0      0    9.8458           0   \n",
       "877          878         0       3  19.0      0      0    7.8958           0   \n",
       "878          879         0       3   NaN      0      0    7.8958           0   \n",
       "879          880         1       1  56.0      0      1   83.1583           1   \n",
       "880          881         1       2  25.0      0      1   26.0000           1   \n",
       "881          882         0       3  33.0      0      0    7.8958           0   \n",
       "882          883         0       3  22.0      0      0   10.5167           1   \n",
       "883          884         0       2  28.0      0      0   10.5000           0   \n",
       "884          885         0       3  25.0      0      0    7.0500           0   \n",
       "885          886         0       3  39.0      0      5   29.1250           1   \n",
       "886          887         0       2  27.0      0      0   13.0000           0   \n",
       "887          888         1       1  19.0      0      0   30.0000           1   \n",
       "888          889         0       3   NaN      1      2   23.4500           1   \n",
       "889          890         1       1  26.0      0      0   30.0000           0   \n",
       "890          891         0       3  32.0      0      0    7.7500           0   \n",
       "\n",
       "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0           1           0           0           1  \n",
       "1           0           1           0           0  \n",
       "2           0           0           0           1  \n",
       "3           0           0           0           1  \n",
       "4           1           0           0           1  \n",
       "5           1           0           1           0  \n",
       "6           1           0           0           1  \n",
       "7           1           0           0           1  \n",
       "8           0           0           0           1  \n",
       "9           0           1           0           0  \n",
       "10          0           0           0           1  \n",
       "11          0           0           0           1  \n",
       "12          1           0           0           1  \n",
       "13          1           0           0           1  \n",
       "14          0           0           0           1  \n",
       "15          0           0           0           1  \n",
       "16          1           0           1           0  \n",
       "17          1           0           0           1  \n",
       "18          0           0           0           1  \n",
       "19          0           1           0           0  \n",
       "20          1           0           0           1  \n",
       "21          1           0           0           1  \n",
       "22          0           0           1           0  \n",
       "23          1           0           0           1  \n",
       "24          0           0           0           1  \n",
       "25          0           0           0           1  \n",
       "26          1           1           0           0  \n",
       "27          1           0           0           1  \n",
       "28          0           0           1           0  \n",
       "29          1           0           0           1  \n",
       "..        ...         ...         ...         ...  \n",
       "861         1           0           0           1  \n",
       "862         0           0           0           1  \n",
       "863         0           0           0           1  \n",
       "864         1           0           0           1  \n",
       "865         0           0           0           1  \n",
       "866         0           1           0           0  \n",
       "867         1           0           0           1  \n",
       "868         1           0           0           1  \n",
       "869         1           0           0           1  \n",
       "870         1           0           0           1  \n",
       "871         0           0           0           1  \n",
       "872         1           0           0           1  \n",
       "873         1           0           0           1  \n",
       "874         0           1           0           0  \n",
       "875         0           1           0           0  \n",
       "876         1           0           0           1  \n",
       "877         1           0           0           1  \n",
       "878         1           0           0           1  \n",
       "879         0           1           0           0  \n",
       "880         0           0           0           1  \n",
       "881         1           0           0           1  \n",
       "882         0           0           0           1  \n",
       "883         1           0           0           1  \n",
       "884         1           0           0           1  \n",
       "885         0           0           1           0  \n",
       "886         1           0           0           1  \n",
       "887         0           0           0           1  \n",
       "888         0           0           0           1  \n",
       "889         1           1           0           0  \n",
       "890         1           0           1           0  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Fare             0\n",
       "Sex_female       0\n",
       "Sex_male         0\n",
       "Embarked_C       0\n",
       "Embarked_Q       0\n",
       "Embarked_S       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "imr=Imputer(missing_values='NaN',strategy='median')\n",
    "#imputing the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['Age']=imr.fit_transform(data2[['Age']]).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MinMax Scaler\n",
    "minmax_scaler=preprocessing.MinMaxScaler()\n",
    "m= data2.iloc[:,3:4].values\n",
    "n= data2.iloc[:,6:7].values\n",
    "age_nml=minmax_scaler.fit_transform(m)\n",
    "fare_nml=minmax_scaler.fit_transform(n)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#standard Scaler to transform data\n",
    "scaler=preprocessing.StandardScaler()\n",
    "m= data2.iloc[:,3:4].values\n",
    "n= data2.iloc[:,6:7].values\n",
    "age_ss=minmax_scaler.fit_transform(m)\n",
    "fare_ss=minmax_scaler.fit_transform(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2['Age']=age_ss\n",
    "data2['Fare']=fare_ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016510</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0.673285</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.101229</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.019854</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041136</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.334004</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.170646</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058694</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</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>0.044986</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.032596</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
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       "      <td>0.723549</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.246042</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.484795</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.061045</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.170646</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015330</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.685851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.031230</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.019854</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.056848</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.384267</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035134</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.434531</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050749</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.421965</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.183212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015672</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.095250</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.472229</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.061264</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.233476</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.513342</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015379</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <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>0.258608</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.022447</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.597889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.050610</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.135753</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.296306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.522493</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.334004</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027050</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.384267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.098561</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.018543</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.044986</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.321438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.585323</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.102579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.409399</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009759</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.585323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.017567</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.046845</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.183212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.246042</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.019218</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.233476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.698417</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.162314</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.308872</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.409399</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015412</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.271174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.020527</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.020495</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.308872</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.484795</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.056848</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.334004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025374</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.233476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.346569</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.045771</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0.321438</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.396833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015127</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass       Age  SibSp  Parch      Fare  \\\n",
       "0              1         0       3  0.271174      1      0  0.014151   \n",
       "1              2         1       1  0.472229      1      0  0.139136   \n",
       "2              3         1       3  0.321438      0      0  0.015469   \n",
       "3              4         1       1  0.434531      1      0  0.103644   \n",
       "4              5         0       3  0.434531      0      0  0.015713   \n",
       "5              6         0       3  0.346569      0      0  0.016510   \n",
       "6              7         0       1  0.673285      0      0  0.101229   \n",
       "7              8         0       3  0.019854      3      1  0.041136   \n",
       "8              9         1       3  0.334004      0      2  0.021731   \n",
       "9             10         1       2  0.170646      1      0  0.058694   \n",
       "10            11         1       3  0.044986      1      1  0.032596   \n",
       "11            12         1       1  0.723549      0      0  0.051822   \n",
       "12            13         0       3  0.246042      0      0  0.015713   \n",
       "13            14         0       3  0.484795      1      5  0.061045   \n",
       "14            15         0       3  0.170646      0      0  0.015330   \n",
       "15            16         1       2  0.685851      0      0  0.031230   \n",
       "16            17         0       3  0.019854      4      1  0.056848   \n",
       "17            18         1       2  0.346569      0      0  0.025374   \n",
       "18            19         0       3  0.384267      1      0  0.035134   \n",
       "19            20         1       3  0.346569      0      0  0.014102   \n",
       "20            21         0       2  0.434531      0      0  0.050749   \n",
       "21            22         1       2  0.421965      0      0  0.025374   \n",
       "22            23         1       3  0.183212      0      0  0.015672   \n",
       "23            24         1       1  0.346569      0      0  0.069291   \n",
       "24            25         0       3  0.095250      3      1  0.041136   \n",
       "25            26         1       3  0.472229      1      5  0.061264   \n",
       "26            27         0       3  0.346569      0      0  0.014102   \n",
       "27            28         0       1  0.233476      3      2  0.513342   \n",
       "28            29         1       3  0.346569      0      0  0.015379   \n",
       "29            30         0       3  0.346569      0      0  0.015412   \n",
       "..           ...       ...     ...       ...    ...    ...       ...   \n",
       "861          862         0       2  0.258608      1      0  0.022447   \n",
       "862          863         1       1  0.597889      0      0  0.050610   \n",
       "863          864         0       3  0.346569      8      2  0.135753   \n",
       "864          865         0       2  0.296306      0      0  0.025374   \n",
       "865          866         1       2  0.522493      0      0  0.025374   \n",
       "866          867         1       2  0.334004      1      0  0.027050   \n",
       "867          868         0       1  0.384267      0      0  0.098561   \n",
       "868          869         0       3  0.346569      0      0  0.018543   \n",
       "869          870         1       3  0.044986      1      1  0.021731   \n",
       "870          871         0       3  0.321438      0      0  0.015412   \n",
       "871          872         1       1  0.585323      1      1  0.102579   \n",
       "872          873         0       1  0.409399      0      0  0.009759   \n",
       "873          874         0       3  0.585323      0      0  0.017567   \n",
       "874          875         1       2  0.346569      1      0  0.046845   \n",
       "875          876         1       3  0.183212      0      0  0.014102   \n",
       "876          877         0       3  0.246042      0      0  0.019218   \n",
       "877          878         0       3  0.233476      0      0  0.015412   \n",
       "878          879         0       3  0.346569      0      0  0.015412   \n",
       "879          880         1       1  0.698417      0      1  0.162314   \n",
       "880          881         1       2  0.308872      0      1  0.050749   \n",
       "881          882         0       3  0.409399      0      0  0.015412   \n",
       "882          883         0       3  0.271174      0      0  0.020527   \n",
       "883          884         0       2  0.346569      0      0  0.020495   \n",
       "884          885         0       3  0.308872      0      0  0.013761   \n",
       "885          886         0       3  0.484795      0      5  0.056848   \n",
       "886          887         0       2  0.334004      0      0  0.025374   \n",
       "887          888         1       1  0.233476      0      0  0.058556   \n",
       "888          889         0       3  0.346569      1      2  0.045771   \n",
       "889          890         1       1  0.321438      0      0  0.058556   \n",
       "890          891         0       3  0.396833      0      0  0.015127   \n",
       "\n",
       "     Sex_female  Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0             0         1           0           0           1  \n",
       "1             1         0           1           0           0  \n",
       "2             1         0           0           0           1  \n",
       "3             1         0           0           0           1  \n",
       "4             0         1           0           0           1  \n",
       "5             0         1           0           1           0  \n",
       "6             0         1           0           0           1  \n",
       "7             0         1           0           0           1  \n",
       "8             1         0           0           0           1  \n",
       "9             1         0           1           0           0  \n",
       "10            1         0           0           0           1  \n",
       "11            1         0           0           0           1  \n",
       "12            0         1           0           0           1  \n",
       "13            0         1           0           0           1  \n",
       "14            1         0           0           0           1  \n",
       "15            1         0           0           0           1  \n",
       "16            0         1           0           1           0  \n",
       "17            0         1           0           0           1  \n",
       "18            1         0           0           0           1  \n",
       "19            1         0           1           0           0  \n",
       "20            0         1           0           0           1  \n",
       "21            0         1           0           0           1  \n",
       "22            1         0           0           1           0  \n",
       "23            0         1           0           0           1  \n",
       "24            1         0           0           0           1  \n",
       "25            1         0           0           0           1  \n",
       "26            0         1           1           0           0  \n",
       "27            0         1           0           0           1  \n",
       "28            1         0           0           1           0  \n",
       "29            0         1           0           0           1  \n",
       "..          ...       ...         ...         ...         ...  \n",
       "861           0         1           0           0           1  \n",
       "862           1         0           0           0           1  \n",
       "863           1         0           0           0           1  \n",
       "864           0         1           0           0           1  \n",
       "865           1         0           0           0           1  \n",
       "866           1         0           1           0           0  \n",
       "867           0         1           0           0           1  \n",
       "868           0         1           0           0           1  \n",
       "869           0         1           0           0           1  \n",
       "870           0         1           0           0           1  \n",
       "871           1         0           0           0           1  \n",
       "872           0         1           0           0           1  \n",
       "873           0         1           0           0           1  \n",
       "874           1         0           1           0           0  \n",
       "875           1         0           1           0           0  \n",
       "876           0         1           0           0           1  \n",
       "877           0         1           0           0           1  \n",
       "878           0         1           0           0           1  \n",
       "879           1         0           1           0           0  \n",
       "880           1         0           0           0           1  \n",
       "881           0         1           0           0           1  \n",
       "882           1         0           0           0           1  \n",
       "883           0         1           0           0           1  \n",
       "884           0         1           0           0           1  \n",
       "885           1         0           0           1           0  \n",
       "886           0         1           0           0           1  \n",
       "887           1         0           0           0           1  \n",
       "888           1         0           0           0           1  \n",
       "889           0         1           1           0           0  \n",
       "890           0         1           0           1           0  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= data2.loc[:, data2.columns!='Survived']\n",
    "Y= data2.loc[:, data2.columns=='Survived']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\New folder\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "lr= LogisticRegression()\n",
    "model= lr.fit(x_train,y_train)\n",
    "predictions= lr.predict(x_test)\n",
    "score=lr.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8067796610169492"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_corr= data2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x161451c9c88>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(data_corr, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Penalty: l2\n",
      "Best C: 1.0\n"
     ]
    }
   ],
   "source": [
    "from warnings import simplefilter\n",
    "# ignore all future warnings\n",
    "simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "logistic = LogisticRegression()\n",
    "\n",
    "# Create regularization penalty space\n",
    "penalty = ['l1', 'l2']\n",
    "\n",
    "# Create regularization hyperparameter space\n",
    "C = np.logspace(0, 4, 10)\n",
    "\n",
    "# Create hyperparameter options\n",
    "hyperparameters = dict(C=C, penalty=penalty)\n",
    "\n",
    "clf = GridSearchCV(logistic, hyperparameters, cv=5, verbose=0)\n",
    "X = data2[['Sex_male', 'Age', 'SibSp', 'Parch','Fare', 'Pclass', 'Embarked_C', 'Embarked_Q']]\n",
    "y = data2['Survived']  #target column i.e price range\n",
    "\n",
    "best_model = clf.fit(X, y)\n",
    "\n",
    "print('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])\n",
    "print('Best C:', best_model.best_estimator_.get_params()['C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8024691358024691"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict = best_model.predict(X)\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(y,y_predict)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "select_feature=SelectKBest(chi2,k=3).fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features_data2=pd.DataFrame({'Feature':list(x_train.columns),\n",
    "                               'Score':select_feature.scores_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>Feature</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sex_female</td>\n",
       "      <td>114.830706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sex_male</td>\n",
       "      <td>62.014516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>21.733144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>16.634786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>15.191863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>6.512796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>4.732588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>3.694375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>2.254248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.108845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>0.005356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Feature       Score\n",
       "6    Sex_female  114.830706\n",
       "7      Sex_male   62.014516\n",
       "1        Pclass   21.733144\n",
       "0   PassengerId   16.634786\n",
       "8    Embarked_C   15.191863\n",
       "5          Fare    6.512796\n",
       "10   Embarked_S    4.732588\n",
       "4         Parch    3.694375\n",
       "3         SibSp    2.254248\n",
       "2           Age    0.108845\n",
       "9    Embarked_Q    0.005356"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_data2.sort_values(ascending=False, by='Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_chi=select_feature.transform(x_train)\n",
    "x_test_chi=select_feature.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\New folder\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "lr_chi_model=lr.fit(x_train_chi, y_train)\n",
    "prediction= lr.predict(x_test_chi)\n",
    "score=lr.score(x_test_chi, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7830508474576271"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\New folder\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "rfe=RFE(estimator=lr, step=1)\n",
    "rfe= rfe.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>Feature</th>\n",
       "      <th>Ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sex_female</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sex_male</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Feature  Ranking\n",
       "0   PassengerId        7\n",
       "4         Parch        6\n",
       "10   Embarked_S        5\n",
       "9    Embarked_Q        4\n",
       "3         SibSp        3\n",
       "5          Fare        2\n",
       "1        Pclass        1\n",
       "2           Age        1\n",
       "6    Sex_female        1\n",
       "7      Sex_male        1\n",
       "8    Embarked_C        1"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_rfe=pd.DataFrame({'Feature':list(x_train.columns),\n",
    "                                   'Ranking':rfe.ranking_})\n",
    "selected_features_rfe.sort_values(ascending=False,by='Ranking')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_rfe=rfe.transform(x_train)\n",
    "x_test_rfe=rfe.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\New folder\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "lr_rfe_model=lr.fit(x_train_rfe,y_train)\n",
    "prediction=lr.predict(x_test_rfe)\n",
    "score=lr.score(x_test_rfe,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xgb= data2.drop(['Sex_female','Embarked_S'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1= data_xgb.loc[:, data_xgb.columns!='Survived']\n",
    "Y1= data_xgb.loc[:, data_xgb.columns=='Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1_train,x1_test,y1_train,y1_test=train_test_split(X1, Y1, \n",
    "                                                    test_size=0.33, \n",
    "                                                    random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\New folder\\lib\\site-packages\\sklearn\\preprocessing\\label.py:95: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "F:\\New folder\\lib\\site-packages\\sklearn\\preprocessing\\label.py:128: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0, learning_rate=0.1,\n",
       "       max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
       "       n_estimators=100, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
       "       subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_xgb= XGBClassifier()\n",
    "model_xgb.fit(x1_train,y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=model_xgb.predict(x1_test)\n",
    "score=model_xgb.score(x1_test,y1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8271186440677966"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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