classification_titanic_survival.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for better auto suggestion\n",
    "%config IPCompleter.greedy=True\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "#feature scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#for tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "# to evaluate the models\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn import metrics\n",
    "\n",
    "pd.pandas.set_option('display.max_columns', None)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.getcwd()\n",
    "os.chdir('C:\\\\Users\\Dell\\Downloads\\datascience')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
       "      <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>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                     Moran, Mr. James    male   NaN      0   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                      Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                                Rice, Master. Eugene    male   2.0      4   \n",
       "17                        Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                             Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "20                                Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                               Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                             Emir, Mr. Farred Chehab    male   NaN      0   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n",
       "29                                 Todoroff, Mr. Lalio    male   NaN      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.0      1   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0      0   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female   NaN      8   \n",
       "864                             Gill, Mr. John William    male  24.0      0   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.0      0   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.0      1   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.0      0   \n",
       "868                        van Melkebeke, Mr. Philemon    male   NaN      0   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.0      1   \n",
       "870                                  Balkic, Mr. Cerin    male  26.0      0   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.0      0   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.0      0   \n",
       "877                               Petroff, Mr. Nedelio    male  19.0      0   \n",
       "878                                 Laleff, Mr. Kristo    male   NaN      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "881                                 Markun, Mr. Johann    male  33.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "883                      Banfield, Mr. Frederick James    male  28.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket      Fare        Cabin Embarked  \n",
       "0        0         A/5 21171    7.2500          NaN        S  \n",
       "1        0          PC 17599   71.2833          C85        C  \n",
       "2        0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "3        0            113803   53.1000         C123        S  \n",
       "4        0            373450    8.0500          NaN        S  \n",
       "5        0            330877    8.4583          NaN        Q  \n",
       "6        0             17463   51.8625          E46        S  \n",
       "7        1            349909   21.0750          NaN        S  \n",
       "8        2            347742   11.1333          NaN        S  \n",
       "9        0            237736   30.0708          NaN        C  \n",
       "10       1           PP 9549   16.7000           G6        S  \n",
       "11       0            113783   26.5500         C103        S  \n",
       "12       0         A/5. 2151    8.0500          NaN        S  \n",
       "13       5            347082   31.2750          NaN        S  \n",
       "14       0            350406    7.8542          NaN        S  \n",
       "15       0            248706   16.0000          NaN        S  \n",
       "16       1            382652   29.1250          NaN        Q  \n",
       "17       0            244373   13.0000          NaN        S  \n",
       "18       0            345763   18.0000          NaN        S  \n",
       "19       0              2649    7.2250          NaN        C  \n",
       "20       0            239865   26.0000          NaN        S  \n",
       "21       0            248698   13.0000          D56        S  \n",
       "22       0            330923    8.0292          NaN        Q  \n",
       "23       0            113788   35.5000           A6        S  \n",
       "24       1            349909   21.0750          NaN        S  \n",
       "25       5            347077   31.3875          NaN        S  \n",
       "26       0              2631    7.2250          NaN        C  \n",
       "27       2             19950  263.0000  C23 C25 C27        S  \n",
       "28       0            330959    7.8792          NaN        Q  \n",
       "29       0            349216    7.8958          NaN        S  \n",
       "..     ...               ...       ...          ...      ...  \n",
       "861      0             28134   11.5000          NaN        S  \n",
       "862      0             17466   25.9292          D17        S  \n",
       "863      2          CA. 2343   69.5500          NaN        S  \n",
       "864      0            233866   13.0000          NaN        S  \n",
       "865      0            236852   13.0000          NaN        S  \n",
       "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "867      0          PC 17590   50.4958          A24        S  \n",
       "868      0            345777    9.5000          NaN        S  \n",
       "869      1            347742   11.1333          NaN        S  \n",
       "870      0            349248    7.8958          NaN        S  \n",
       "871      1             11751   52.5542          D35        S  \n",
       "872      0               695    5.0000  B51 B53 B55        S  \n",
       "873      0            345765    9.0000          NaN        S  \n",
       "874      0         P/PP 3381   24.0000          NaN        C  \n",
       "875      0              2667    7.2250          NaN        C  \n",
       "876      0              7534    9.8458          NaN        S  \n",
       "877      0            349212    7.8958          NaN        S  \n",
       "878      0            349217    7.8958          NaN        S  \n",
       "879      1             11767   83.1583          C50        C  \n",
       "880      1            230433   26.0000          NaN        S  \n",
       "881      0            349257    7.8958          NaN        S  \n",
       "882      0              7552   10.5167          NaN        S  \n",
       "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "885      5            382652   29.1250          NaN        Q  \n",
       "886      0            211536   13.0000          NaN        S  \n",
       "887      0            112053   30.0000          B42        S  \n",
       "888      2        W./C. 6607   23.4500          NaN        S  \n",
       "889      0            111369   30.0000         C148        C  \n",
       "890      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of variable in catagorical is 5\n"
     ]
    }
   ],
   "source": [
    "catagorical=[var for var in df.columns if df[var].dtype=='O']\n",
    "print('no. of variable in catagorical is',len(catagorical))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no. of variable in numerical is 7\n"
     ]
    }
   ],
   "source": [
    "numerical=[var for var in df.columns if df[var].dtype!='O']\n",
    "\n",
    "print('no. of variable in numerical is',len(numerical))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass [3 1 2]\n",
      "SibSp [1 0 3 4 2 5 8]\n",
      "Parch [0 1 2 5 3 4 6]\n"
     ]
    }
   ],
   "source": [
    "#discere values\n",
    "\n",
    "discrete=[]\n",
    "for var in numerical:\n",
    "    if len(df[var].unique())<20 and var not in ['Survived','PassengerId'] :\n",
    "        print(var,df[var].unique())\n",
    "        discrete.append(var)\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass', 'SibSp', 'Parch']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discrete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find out null\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "continous=[var for var in numerical if var not in ['PassengerId','Survived'] and var not in discrete]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age', 'Fare']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Fare')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outliers for continous varible\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "fig = df.boxplot(column='Age')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Age')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "fig = df.boxplot(column='Fare')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Fare')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Fare')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2WL6MdrN3X0QsLfbqLQV2TPfkzFwPrAcYGRnJ0dHRecZof/Hu5vn91O+sp6+7ct7PXbtynHM3d3pVj8HrZ96tp4129fw6fWahXnnrlBXqlbdOWeeqhBopSVLPdHKCljOBo4CHADLzTmDeUywz8wfAPRHx7GLVMcBtwEZgVbFuFXD5fN9DkqQ+6WmNlCSplzrZnfFIZv4iIgCIiEW0L5fQjTcCF0XEXsBdwBm0G89LI2I1cDdwSpfvIUlS2cqokZIk9UQnzd7XIuJtwOKIeBnwBuCL3bxpZt5M+9pEu3LaiySpTnpeIyVJ6pVOpnGuo332zM3A64GrgHeUGUqSpJqwRkqSKquTs3E+Bnwc+HhE7Acsz0ynqEiSFjxrpCSpyna7Zy8iWhGxT1HEbqZ9fbz3lx9NkqRqs0ZKkqqsk2mc+2bmQ8ArgX/IzBcCLy03liRJtWCNlCRVVifN3qLiunevAq4oOY8kSXVijZQkVVYnzd5/B74MbMnM6yPimcCd5caSJKkWrJGSpMrq5AQtnwE+M2X5LuCPywwlSVIdWCMlSVXWyQla/kdx8PmeEbEpIn4UEa/pRzhJkqrMGilJqrJOpnH+fnHw+SuAbcBvAv+11FSSJNWDNVKSVFmdNHt7Fj+PAy7OzB+XmEeSpDqxRkqSKmu3x+wBX4yI7wA/A94QEQcCPy83liRJtWCNlCRV1m737GXmOuBFwEhm/hJ4GDix7GCSJFWdNVKSVGWd7NkDWAa8LCKeNGXdhSXkkSSpbqyRkqRK2m2zFxHvAkaB5wFXAccCX8dCJkla4KyRkqQq6+QELScDxwA/yMwzgEOBJ5aaSpKkerBGSpIqq5Nm72eZ+RgwHhH7ADuAZ5YbS5KkWrBGSpIqq5Nm74aIWAJ8HLgRuAm4rtRUkiTVw7xqZEQ8KSKui4hvRcStEfGeYv0hEXFtRNwZEZ+OiL2K9U8slrcUjw+X9ytJkppit8fsZeYbirsfjYgvAftk5rfLjSVJUvV1USMfAY7OzLGI2BP4ekT8M/AW4AOZeUlEfBRYDZxf/Lw/M58VEacC7wP+pOe/kCSpUTrZs0dEvDIi3g+8EfiNciNJklQf86mR2TZWLO5Z3BI4GrisWL8BOKm4f2KxTPH4MRERPYgvSWqwTs7G+ffAs4CLi1Wvj4iXZuaZpSaTJKniuqmREbEH7amfzwI+AnwXeCAzx4tNttG+rAPFz3sAMnM8Ih4E9gd+tMtrrgHWAAwNDdFqteb/ywFDi2HtyvHdbziLbjNUxdjYWGN+l15wPCY5FjtzPCZVYSw6uc7e7wLPz8wEiIgNwOZSU0mSVA/zrpGZ+ShwWHHM3+eB5063WfFzur14+bgVmeuB9QAjIyM5OjraSZQZffiiyzl3c6eX5J3e1tO6y1AVrVaLbsezSRyPSY7FzhyPSVUYi06mcd4BPH3K8sGAx+xJktSDGpmZDwAt4EhgSURMdFfLgXuL+9uK16Z4fF/gx/NOLUlaEDpp9vYHbo+IVkS0gNuAAyNiY0RsLDWdJEnVNq8aGREHFnv0iIjFwEuB24FraF+7D2AVcHlxf2OxTPH4Vyf2JkqSNJNO5ma8s/QUkiTV03xr5FJgQ3Hc3hOASzPzioi4DbgkIv4K+CZwQbH9BcA/RsQW2nv0Tu0ytyRpAejk0gtf60cQSZLqZr41srg8wwumWX8XcMQ0638OnDKf95IkLVwdXXpBkiRJklQvNnuSJEmS1EAzNnsRsan4+b7+xZEkqfqskZKkOpjtmL2lEfG7wAkRcQm7XOMnM28qNZkkSdVljZQkVd5szd47gXW0r/Pz/l0eS+DoskJJklRx1khJUuXN2Oxl5mXAZRHx3zLzvX3MJElSpVkjJUl10MmlF94bEScALylWtTLzinJjSZJUfdZISVKV7fZsnBHxN8BZwG3F7axinSRJC5o1UpJUZbvdswccDxyWmY8BRMQG4JvA2WUGkySpBqyRkqTK6vQ6e0um3N+3jCCSJNWUNVKSVEmd7Nn7G+CbEXEN7VNLvwT/xVKSJLBGSpIqrJMTtFwcES3gP9AuZG/NzB+UHUySpKqzRkqSqqyTPXtk5nZgY8lZJEmqHWukJKmqOj1mT5IkSZJUIzZ7kiRJktRAszZ7EfGEiLilX2EkSaoLa6QkqepmbfaK6wZ9KyKe3qc8kiTVgjVSklR1nZygZSlwa0RcBzw8sTIzTygtldRQw+uu7Or5a1eOM9qbKJJ6wxopSaqsTpq995SeQpKkerJGSpIqq5Pr7H0tIp4BrMjMf4mIJwN7lB9NkqRqs0ZKkqpst2fjjIg/By4DPlasWgZ8ocxQkiTVgTVSklRlnVx64UzgKOAhgMy8EziozFCSJNWENVKSVFmdNHuPZOYvJhYiYhGQ5UWSJKk2rJGSpMrqpNn7WkS8DVgcES8DPgN8sdxYkiTVgjVSklRZnTR764AfApuB1wNXAe8oM5QkSTVhjZQkVVYnZ+N8LCI2ANfSnppyR2Y6RUWStOBZIyVJVbbbZi8ijgc+CnwXCOCQiHh9Zv5z2eEkSaoya6Qkqco6uaj6ucDvZeYWgIj4DeBKwEImSVrorJGSpMrq5Ji9HRNFrHAXsKOkPJIk1Yk1UpJUWTPu2YuIVxZ3b42Iq4BLaR+PcApwfR+ySZJUSdZISVIdzDaN8w+n3L8P+N3i/g+Bp3X7xhGxB3AD8P3MfEVEHAJcAuwH3AS8duq1iyRJqpBSa6QkSb0wY7OXmWeU/N5nAbcD+xTL7wM+kJmXRMRHgdXA+SVnkCRpzvpQIyVJ6lonZ+M8BHgjMDx1+8w8Yb5vGhHLgeOBvwbeEhEBHA38abHJBuDd2OxJkiqsjBopSVKvdHI2zi8AFwBfBB7r0ft+EPhL4KnF8v7AA5k5XixvA5ZN98SIWAOsARgaGqLVas07xNjYGB++6PJ5P3+qlcv27cnrzGRsbKyr33Wu1q4c3/1GMxha3N3z+61OeYcW09fPQbf6/bntRp2yQr3y1inrPJRRIyVJ6olOmr2fZ+aHevWGEfEK2mcvuzEiRidWT7PptBelzcz1wHqAkZGRHB0dnW6zjrRaLc79+sPzfv5UW0+bf45OtFotuvld5+r0dVfO+7lrV45z7uZOPlrVUKe8a1eO86o+fg661e/PbTfqlBXqlbdOWeehpzVSkqRe6uQb7nkR8S7gK8AjEysz86Z5vudRwAkRcRzwJNrH7H0QWBIRi4q9e8uBe+f5+pIk9Uuva6QkST3TSbO3Engt7WPqJqaoZLE8Z5l5NnA2QLFn779k5mkR8RngZNpn5FwF9GZ+pSRJ5elpjZQkqZc6afb+CHhmHy6D8Fbgkoj4K+CbtI+BkCSpyvpVIyVJmrNOmr1vAUuAHb1+88xsAa3i/l3AEb1+D0mSSlRajZQkqVudNHtDwHci4np2Ph7B00pLkhY6a6QkqbI6afbeVXoKSZLqyRopSaqs3TZ7mfm1fgSRJKlurJGSpCrbbbMXET9h8pp3ewF7Ag9n5j5lBpMkqeqskZKkKutkz95Tpy5HxEl4IhVJkqyRkqRKe8Jcn5CZX8DrB0mS9DjWSElSlXQyjfOVUxafAIwwOWVFkqQFyxopSaqyTs7G+YdT7o8DW4ETS0kjSVK9zLlGRsTBwIXArwGPAesz87yI2A/4NDBcvM6rMvP+iAjgPOA44KfA6Zl5U29/DUlSE3VyzN4Z/QgiSVLdzLNGjgNrM/OmiHgqcGNEXA2cDmzKzHMiYh2wDngrcCyworj9DnB+8VOSpFnN2OxFxDtneV5m5ntLyCNJUuV1UyMzczuwvbj/k4i4HVhGe4/gaLHZBqBFu9k7EbgwMxP4RkQsiYilxetIkjSj2fbsPTzNur2B1cD+gM2eNADD664cdIRf2XrO8YOOIA1KT2pkRAwDLwCuBYYmGrjM3B4RBxWbLQPumfK0bcW6xzV7EbEGWAMwNDREq9XqJMaMhhbD2pXjXb1GtxmqYmxsrDG/Sy84HpMci505HpOqMBYzNnuZee7E/WKayVnAGcAlwLkzPU+SpKbrRY2MiKcAnwXenJkPtQ/Nm37T6SLMkGs9sB5gZGQkR0dHO4kyow9fdDnnbu7k8P6ZbT2tuwxV0Wq16HY8m8TxmORY7MzxmFSFsZj10gsRsV9E/BXwbdqN4eGZ+dbM3NGXdJIkVVQ3NTIi9qTd6F2UmZ8rVt8XEUuLx5cCE6+zDTh4ytOXA/f26NeQJDXYjM1eRPwtcD3wE2BlZr47M+/vWzJJkiqqmxpZnF3zAuD2zHz/lIc2AquK+6uAy6esf120HQk86PF6kqROzDY3Yy3wCPAO4O1TppcE7YPP9yk5myRJVdVNjTwKeC2wOSJuLta9DTgHuDQiVgN3A6cUj11F+7ILW2hfesGzZEuSOjLbMXuzTvGUJGmh6qZGZubXmf44PIBjptk+gTPn+36SpIXLhk6SJEmSGshmT5IkSZIayGZPkiRJkhrIZk+SJEmSGshmT5IkSZIayGZPkiRJkhrIZk+SJEmSGmi2i6qrhobXXTnoCJIkSZIqwD17kiRJktRANnuSJEmS1EA2e5IkSZLUQB6zJ0mSStOrY8m3nnN8T15HkhYS9+xJkiRJUgO5Z0/SvO3uX+zXrhzn9N1s47/WS5IklcM9e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EA2e5IkSZLUQDZ7kiRJktRANnuSJEmS1EB9b/Yi4uCIuCYibo+IWyPirGL9fhFxdUTcWfx8Wr+zSZIkSVJTDGLP3jiwNjOfCxwJnBkRzwPWAZsycwWwqViWJEmSJM1D35u9zNyemTcV938C3A4sA04ENhSbbQBO6nc2SZIkSWqKRYN884gYBl4AXAsMZeZ2aDeEEXHQDM9ZA6wBGBoaotVqzfv9x8bGWLvy0Xk/f6pucnRibGyso/dYu3K81BydGFpcjRydqlPeOmWFzvKW/bfTqU7/xqqiTnnrlFWSpCYZWLMXEU8BPgu8OTMfioiOnpeZ64H1ACMjIzk6OjrvDK1Wi3O//vC8nz/V1tPmn6MTrVaLTn7X09ddWWqOTqxdOc65mwf67whzUqe8dcoKneUt+2+nU53+jVVFnfLWKaskSU0ykLNxRsSetBu9izLzc8Xq+yJiafH4UmDHILJJkiRJUhMM4mycAVwA3J6Z75/y0EZgVXF/FXB5v7NJkiRJUlMMYj7YUcBrgc0RcXOx7m3AOcClEbEauBs4ZQDZJEmSJKkR+t7sZebXgZkO0Dumn1kkSRqEiPgk8ApgR2Y+v1i3H/BpYBjYCrwqM+8vZsScBxwH/BQ4feKs1pIkzWYgx+xJkrTAfQp4+S7rZrre7LHAiuK2Bji/TxklSTVnsydJUp9l5r8CP95l9UzXmz0RuDDbvgEsmTihmSRJs7HZkySpGna63iwwcb3ZZcA9U7bbVqyTJGlW9blglyRJC9N0x7nntBtGrKE91ZOhoaGuL2Y/tLh9vcwq6PZ36dbY2NjAM1SJ4zHJsdiZ4zGpCmNhsydJUjXcFxFLM3P7Lteb3QYcPGW75cC9071AZq4H1gOMjIxktxez//BFl3Pu5mp8Vdh62uhA37/VatHteDaJ4zHJsdiZ4zGpCmPhNE5JkqphpuvNbgReF21HAg9OTPeUJGk21fjnOkmSFpCIuBgYBQ6IiG3Au5j5erNX0b7swhbal144o++BJUm1ZLMnaaCG113Z9WtsPef4HiSR+iczXz3DQ4+73mxmJnBmuYkkSU3kNE5JkiRJaiCbPUmSJElqIKdxVshs09nWrhzn9B5Md5MkSZK0MLhnT5IkSZIayGZPkiRJkhrIaZw90oszCkqSJElSr7hnT5IkSZIayGZPkiRJkhrIZk+SJEmSGshj9iTVXi+OmV27cpzR7qNIkiRVhnv2JEmSJKmBbPYkSZIkqYFs9iRJkiSpgTxmT5IkVV4vjs3demgZW+QAAAusSURBVM7xPUgiSfXhnj1JkiRJaiD37ElSwT0HkiSpSdyzJ0mSJEkNZLMnSZIkSQ1ksydJkiRJDWSzJ0mSJEkNZLMnSZIkSQ1ksydJkiRJDWSzJ0mSJEkNZLMnSZIkSQ1ksydJkiRJDWSzJ0mSJEkNZLMnSZIkSQ1ksydJkiRJDbRo0AEkSZL6YXjdlfN+7tqV45y+7kq2nnN8DxNJUrncsydJkiRJDWSzJ0mSJEkNZLMnSZIkSQ1ksydJkiRJDWSzJ0mSJEkN5Nk4JaliOjlj4MSZAWfjWQMlSVrY3LMnSZIkSQ1ksydJkiRJDeQ0TknqoW4u2ixJktRL7tmTJEmSpAay2ZMkSZKkBrLZkyRJkqQG8pg9SZKkPurVsb1eXkXS7tjsSZIkdciTMEmqE6dxSpIkSVID2exJkiRJUgM5jVOSJEnz5jGIUnVVqtmLiJcD5wF7AJ/IzHMGHEmSpEqwRkq714vG06ZTTVKZZi8i9gA+ArwM2AZcHxEbM/O2wSaTpHqqyokkPvXyvQcdofaskZKk+ahMswccAWzJzLsAIuIS4ETAQiZJWuiskZJUIZ38g+raleOcvpvtyt6TXKVmbxlwz5TlbcDvDCiLJElVYo3U4zRtymJVZiNITRKZOegMAETEKcAfZOafFcuvBY7IzDfust0aYE2x+Gzgji7e9gDgR108v5/MWp465a1TVqhX3jplhXrl7VXWZ2TmgT14ndoZUI2Een3OyuZY7MzxmORY7MzxmNSvsZixPlZpz9424OApy8uBe3fdKDPXA+t78YYRcUNmjvTitcpm1vLUKW+dskK98tYpK9Qrb52yVljfayT4324qx2Jnjsckx2JnjsekKoxFla6zdz2wIiIOiYi9gFOBjQPOJElSFVgjJUlzVpk9e5k5HhH/Gfgy7dNKfzIzbx1wLEmSBs4aKUmaj8o0ewCZeRVwVR/fsmdTXfrArOWpU946ZYV65a1TVqhX3jplrawB1Ejwv91UjsXOHI9JjsXOHI9JAx+LypygRZIkSZLUO1U6Zk+SJEmS1CMLstmLiJdHxB0RsSUi1g06z64i4pMRsSMibpmybr+IuDoi7ix+Pm2QGSdExMERcU1E3B4Rt0bEWcX6yuWNiCdFxHUR8a0i63uK9YdExLVF1k8XJz+ojIjYIyK+GRFXFMuVzBsRWyNic0TcHBE3FOsq9zmYEBFLIuKyiPhO8fl9URXzRsSzizGduD0UEW+uYtYJEfEXxd/YLRFxcfG3V8nPraZX9TpZhrnU3mj7UDE+346IwweXvPfmWtsXwHjM6ftDRDyxWN5SPD48yPxl6PS7yQIZi46//wzib2XBNXsRsQfwEeBY4HnAqyPieYNN9TifAl6+y7p1wKbMXAFsKparYBxYm5nPBY4EzizGs4p5HwGOzsxDgcOAl0fEkcD7gA8UWe8HVg8w43TOAm6fslzlvL+XmYdNOc1wFT8HE84DvpSZzwEOpT3GlcubmXcUY3oY8ELgp8DnqWBWgIhYBrwJGMnM59M+mcipVPtzqylqUifL8Ck6r73HAiuK2xrg/D5l7Je51vamj8dcvz+sBu7PzGcBHyi2a5pOv5sshLGAzr//9P9vJTMX1A14EfDlKctnA2cPOtc0OYeBW6Ys3wEsLe4vBe4YdMYZcl8OvKzqeYEnAzcBv0P7YpeLpvt8DPpG+1pam4CjgSuAqGpeYCtwwC7rKvk5APYBvkdx3HLV807J9/vA/6lyVmAZcA+wH+2TgF0B/EFVP7fepv1vWIs6WdLv3lHtBT4GvHq67Zp4211tX0jj0cn3B9pnzX1RcX9RsV0MIm9JY9Dxd5Omj0Xxe3X8/WcQfysLbs8ek19EJmwr1lXdUGZuByh+HjTgPI9T7Jp/AXAtFc1bTDu4GdgBXA18F3ggM8eLTar2efgg8JfAY8Xy/lQ3bwJfiYgbI2JNsa6SnwPgmcAPgX8opqF8IiL2prp5J5wKXFzcr2TWzPw+8HfA3cB24EHgRqr7udXj1bVOlmGmv7MFM0Yd1vbGj8ccvz/8ajyKxx+kXb+bYi7fTZo+FjC37z99/1tZiM1eTLPOU5J2KSKeAnwWeHNmPjToPDPJzEezPR1uOXAE8NzpNutvqulFxCuAHZl549TV02xaibzAUZl5OO0pCmdGxEsGHWgWi4DDgfMz8wXAw1RkGuRMiuMfTgA+M+gssymOSzgROAT4dWBv2p+JXVXlc6vHq/L/Z6piQYzRHGp748djjt8fGjse8/hu0tixmGIu33/6Ph4LsdnbBhw8ZXk5cO+AsszFfRGxFKD4uWPAeX4lIvakXQwuyszPFasrmxcgMx8AWrSPRVgSERPXnKzS5+Eo4ISI2ApcQnu6xAepaN7MvLf4uYP2MWVHUN3PwTZgW2ZeWyxfRrv5q2peaBeRmzLzvmK5qllfCnwvM3+Ymb8EPge8mIp+bjWtutbJMsz0d9b4MZpjbW/8eEzo8PvDr8ajeHxf4Mf9TVqauX43afJYAHP+/tP3v5WF2OxdD6wozhq0F+1pURsHnKkTG4FVxf1VtOfPD1xEBHABcHtmvn/KQ5XLGxEHRsSS4v5i2l9KbweuAU4uNqtEVoDMPDszl2fmMO3P6Vcz8zQqmDci9o6Ip07cp31s2S1U8HMAkJk/AO6JiGcXq44BbqOieQuvZnIKJ1Q3693AkRHx5OL/DxNjW7nPrWZU1zpZhpn+zjYCryvOrHck8ODElK0mmEdtb/p4zPX7w9RxOpl2/W7E3qx5fDdp7FjAvL7/9P9vZdAHNQ7iBhwH/Bvt+dZvH3SeafJdTPtYl1/S/heA1bTnN28C7ix+7jfonEXW/0h79/O3gZuL23FVzAv8NvDNIustwDuL9c8ErgO20J4i98RBZ50m+yhwRVXzFpm+Vdxunfi7quLnYErmw4Abis/DF4CnVTUv7RMC/D9g3ynrKpm1yPYe4DvF39k/Ak+s4ufW26z/DStdJ0v6nTuuvbSnYn2kGJ/NtM8+O/DfoYdjMafavgDGY07fH4AnFctbisefOejfoaRx2e13k6aPxVy//wzibyWKN5YkSZIkNchCnMYpSZIkSY1nsydJkiRJDWSzJ0mSJEkNZLMnSZIkSQ1ksydJkiRJDWSzJ1VQRPxRRGREPGfQWSRJGrSIeDQibp5yGx50JqkOvPSCVEERcSmwFNiUme8ecBxJkgYqIsYy8ynzeN4emfloGZmkOnDPnlQxEfEU4CjaF/Q9tVj3hIj4+4i4NSKuiIirIuLk4rEXRsTXIuLGiPhyRCwdYHxJkvoiIoYj4n9HxE3F7cXF+tGIuCYi/on2hauJiNdExHXFXsGPRcQeAw0v9YnNnlQ9JwFfysx/A34cEYcDrwSGgZXAnwEvAoiIPYEPAydn5guBTwJ/PYjQkiSVaPGUKZyfL9btAF6WmYcDfwJ8aMr2RwBvz8znRcRzi8ePyszDgEeB0/oZXhqURYMOIOlxXg18sLh/SbG8J/CZzHwM+EFEXFM8/mzg+cDVEQGwB7C9v3ElSSrdz4pGbao9gf8ZERMN3G9Oeey6zPxecf8Y4IXA9UWtXEy7UZQaz2ZPqpCI2B84Gnh+RCTt5i2Bz8/0FODWzHxRnyJKklQVfwHcBxxKe7baz6c89vCU+wFsyMyz+5hNqgSncUrVcjJwYWY+IzOHM/Ng4HvAj4A/Lo7dGwJGi+3vAA6MiF9N64yI3xpEcEmS+mxfYHsx6+W1tP+BdDqbgJMj4iCAiNgvIp7Rp4zSQNnsSdXyah6/F++zwK8D24BbgI8B1wIPZuYvaDeI74uIbwE3Ay/uX1xJkgbm74FVEfEN2lM4H55uo8y8DXgH8JWI+DZwNe0zXkuN56UXpJqIiKdk5lgx1fM62gea/2DQuSRJklRNHrMn1ccVEbEE2At4r42eJEmSZuOePUmSJElqII/ZkyRJkqQGstmTJEmSpAay2ZMkSZKkBrLZkyRJkqQGstmTJEmSpAay2ZMkSZKkBvr/CfwuzW6ukj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot to see skewness\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "fig = df['Age'].hist(bins=20)\n",
    "fig.set_ylabel('Number of passengers')\n",
    "fig.set_xlabel('Age')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "fig = df.Fare.hist(bins=20)\n",
    "fig.set_ylabel('Number of passengers')\n",
    "fig.set_xlabel('Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age outliers are values < -13.88037434994331 or > 73.27860964406095\n",
      "Fare outliers are values < -61.358399999999996 or > 100.2688\n"
     ]
    }
   ],
   "source": [
    "#Age is quite Gaussian and Fare is skewed, so I will use the Gaussian assumption for Age, and the interquantile range for Fare\n",
    "\n",
    "# find outliers\n",
    "\n",
    "# Age\n",
    "Upper_boundary = df.Age.mean() + 3* df.Age.std()\n",
    "Lower_boundary = df.Age.mean() - 3* df.Age.std()\n",
    "print('Age outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_boundary, upperboundary=Upper_boundary))\n",
    "\n",
    "# Fare\n",
    "IQR = df.Fare.quantile(0.75) - df.Fare.quantile(0.25)\n",
    "Lower_fence = df.Fare.quantile(0.25) - (IQR * 3)\n",
    "Upper_fence = df.Fare.quantile(0.75) + (IQR * 3)\n",
    "print('Fare outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age outliers are values < -13.88037434994331 or > 73.27860964406095\n",
    "Fare outliers are values < -61.358399999999996 or > 100.2688\n",
    "We should remove from the dataset Ages > 73 or Fares > 100.\n",
    "\n",
    "There are a few methods to handle outliers, one is top-coding, the other one is discretisation of variables. I will use top-coding for Age, and discretisation for Fare.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    0.551066\n",
      "1    0.242424\n",
      "2    0.206510\n",
      "Name: Pclass, dtype: float64\n",
      "0    0.682379\n",
      "1    0.234568\n",
      "2    0.031425\n",
      "4    0.020202\n",
      "3    0.017957\n",
      "8    0.007856\n",
      "5    0.005612\n",
      "Name: SibSp, dtype: float64\n",
      "0    0.760943\n",
      "1    0.132435\n",
      "2    0.089787\n",
      "5    0.005612\n",
      "3    0.005612\n",
      "4    0.004489\n",
      "6    0.001122\n",
      "Name: Parch, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#find outliers for discrete variable\n",
    "for var in discrete:\n",
    "    print(df[var].value_counts()/np.float(len(df)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pclass does not contain outliers, as all its numbers are present in at least 20% of the passengers.\n",
    "\n",
    "SibSp This variable indicates the number of of siblings / spouses aboard the Titanic. Values bigger than 4, are rare. So I will cap this variable at 4 (top coding).\n",
    "\n",
    "Parch This variable indicates the number of parents / children aboard the Titanic. We can see that values > 2 are rare (present in less than 1% of passengers). Thus I will cap this variable at 2 (top-coding)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name  contains  891  labels\n",
      "Sex  contains  2  labels\n",
      "Ticket  contains  681  labels\n",
      "Cabin  contains  148  labels\n",
      "Embarked  contains  4  labels\n"
     ]
    }
   ],
   "source": [
    "#Number of labels: cardinality¶\n",
    "for var in catagorical:\n",
    "    print(var, ' contains ', len(df[var].unique()), ' labels')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Cabin_numerical</th>\n",
       "      <th>Cabin_categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>123.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin  Cabin_numerical Cabin_categorical\n",
       "0   NaN              NaN               NaN\n",
       "1   C85             85.0                 C\n",
       "2   NaN              NaN               NaN\n",
       "3  C123            123.0                 C\n",
       "4   NaN              NaN               NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pre-procesing of mixed type variable\n",
    "df['Cabin_numerical'] = df.Cabin.str.extract('(\\d+)') # extracts number from string\n",
    "df['Cabin_numerical'] = df['Cabin_numerical'].astype('float') # parses the above variable to float type\n",
    "\n",
    "df['Cabin_categorical'] = df['Cabin'].str[0] # captures first letter of string (the letter of the cabin)\n",
    "df[['Cabin', 'Cabin_numerical', 'Cabin_categorical']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the original variable\n",
    "df.drop(labels='Cabin', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Ticket</th>\n",
       "      <th>Ticket_numerical</th>\n",
       "      <th>Ticket_categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>21171.0</td>\n",
       "      <td>A/5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC 17599</td>\n",
       "      <td>17599.0</td>\n",
       "      <td>PC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>3101282.0</td>\n",
       "      <td>STON/O2.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113803</td>\n",
       "      <td>113803.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373450</td>\n",
       "      <td>373450.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ticket  Ticket_numerical Ticket_categorical\n",
       "0         A/5 21171           21171.0                A/5\n",
       "1          PC 17599           17599.0                 PC\n",
       "2  STON/O2. 3101282         3101282.0           STON/O2.\n",
       "3            113803          113803.0                NaN\n",
       "4            373450          373450.0                NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Ticket\n",
    "# extract the last bit of ticket as number\n",
    "df['Ticket_numerical'] = df.Ticket.apply(lambda s: s.split()[-1])\n",
    "df['Ticket_numerical'] = np.where(df.Ticket_numerical.str.isdigit(), df.Ticket_numerical, np.nan)\n",
    "df['Ticket_numerical'] = df['Ticket_numerical'].astype('float')\n",
    "\n",
    "# extract the first part of ticket as category\n",
    "df['Ticket_categorical'] = df.Ticket.apply(lambda s: s.split()[0])\n",
    "df['Ticket_categorical'] = np.where(df.Ticket_categorical.str.isdigit(), np.nan, df.Ticket_categorical)\n",
    "\n",
    "\n",
    "# extract the first part of ticket as category\n",
    "df[['Ticket', 'Ticket_numerical', 'Ticket_categorical']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A/5', 'PC', 'STON/O2.', nan, 'PP', 'A/5.', 'C.A.', 'A./5.',\n",
       "       'SC/Paris', 'S.C./A.4.', 'A/4.', 'CA', 'S.P.', 'S.O.C.', 'SO/C',\n",
       "       'W./C.', 'SOTON/OQ', 'W.E.P.', 'STON/O', 'A4.', 'C', 'SOTON/O.Q.',\n",
       "       'SC/PARIS', 'S.O.P.', 'A.5.', 'Fa', 'CA.', 'LINE', 'F.C.C.', 'W/C',\n",
       "       'SW/PP', 'SCO/W', 'P/PP', 'SC', 'SC/AH', 'A/S', 'A/4', 'WE/P',\n",
       "       'S.W./PP', 'S.O./P.P.', 'F.C.', 'SOTON/O2', 'S.C./PARIS',\n",
       "       'C.A./SOTON'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's explore the ticket categorical part a bit further\n",
    "df.Ticket_categorical.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Ticket_categorical</th>\n",
       "      <th>Ticket_categorical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>A/5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC</td>\n",
       "      <td>PC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STONO</td>\n",
       "      <td>STON/O2.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PP</td>\n",
       "      <td>PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>A</td>\n",
       "      <td>A/5.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>CA</td>\n",
       "      <td>CA.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>SCPARIS</td>\n",
       "      <td>SC/PARIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>PC</td>\n",
       "      <td>PC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>PPP</td>\n",
       "      <td>P/PP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>CASOTON</td>\n",
       "      <td>C.A./SOTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>SOTONOQ</td>\n",
       "      <td>SOTON/OQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>WC</td>\n",
       "      <td>W./C.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ticket_categorical Ticket_categorical\n",
       "0                    A                A/5\n",
       "1                   PC                 PC\n",
       "2                STONO           STON/O2.\n",
       "3                  nan                NaN\n",
       "4                  nan                NaN\n",
       "5                  nan                NaN\n",
       "6                  nan                NaN\n",
       "7                  nan                NaN\n",
       "8                  nan                NaN\n",
       "9                  nan                NaN\n",
       "10                  PP                 PP\n",
       "11                 nan                NaN\n",
       "12                   A               A/5.\n",
       "13                 nan                NaN\n",
       "14                 nan                NaN\n",
       "15                 nan                NaN\n",
       "16                 nan                NaN\n",
       "17                 nan                NaN\n",
       "18                 nan                NaN\n",
       "19                 nan                NaN\n",
       "20                 nan                NaN\n",
       "21                 nan                NaN\n",
       "22                 nan                NaN\n",
       "23                 nan                NaN\n",
       "24                 nan                NaN\n",
       "25                 nan                NaN\n",
       "26                 nan                NaN\n",
       "27                 nan                NaN\n",
       "28                 nan                NaN\n",
       "29                 nan                NaN\n",
       "..                 ...                ...\n",
       "861                nan                NaN\n",
       "862                nan                NaN\n",
       "863                 CA                CA.\n",
       "864                nan                NaN\n",
       "865                nan                NaN\n",
       "866            SCPARIS           SC/PARIS\n",
       "867                 PC                 PC\n",
       "868                nan                NaN\n",
       "869                nan                NaN\n",
       "870                nan                NaN\n",
       "871                nan                NaN\n",
       "872                nan                NaN\n",
       "873                nan                NaN\n",
       "874                PPP               P/PP\n",
       "875                nan                NaN\n",
       "876                nan                NaN\n",
       "877                nan                NaN\n",
       "878                nan                NaN\n",
       "879                nan                NaN\n",
       "880                nan                NaN\n",
       "881                nan                NaN\n",
       "882                nan                NaN\n",
       "883            CASOTON         C.A./SOTON\n",
       "884            SOTONOQ           SOTON/OQ\n",
       "885                nan                NaN\n",
       "886                nan                NaN\n",
       "887                nan                NaN\n",
       "888                 WC              W./C.\n",
       "889                nan                NaN\n",
       "890                nan                NaN\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it contains several labels, some of them seem very similar apart from the punctuation\n",
    "# I will try to reduce this number of labels a bit further\n",
    "\n",
    "# remove non letter characters from string\n",
    "text = df.Ticket_categorical.apply(lambda x: re.sub(\"[^a-zA-Z]\", '', str(x)))\n",
    "\n",
    "# to visualise the output and compare with input\n",
    "pd.concat([text, df.Ticket_categorical], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'PC', 'STONO', 'NAN', 'PP', 'CA', 'SCPARIS', 'SCA', 'SP',\n",
       "       'SOC', 'WC', 'SOTONOQ', 'WEP', 'C', 'SOP', 'FA', 'LINE', 'FCC',\n",
       "       'SWPP', 'SCOW', 'PPP', 'SC', 'SCAH', 'AS', 'SOPP', 'FC', 'SOTONO',\n",
       "       'CASOTON'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set to upper case: we reduce the number of labels quite a bit\n",
    "text = text.str.upper()\n",
    "text.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ticket_categorical'] = text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'PC', 'STONO', 'NAN', 'PP', 'CA', 'SCPARIS', 'SCA', 'SP',\n",
       "       'SOC', 'WC', 'SOTONOQ', 'WEP', 'C', 'SOP', 'FA', 'LINE', 'FCC',\n",
       "       'SWPP', 'SCOW', 'PPP', 'SC', 'SCAH', 'AS', 'SOPP', 'FC', 'SOTONO',\n",
       "       'CASOTON'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket_categorical'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# drop the original variable\n",
    "df.drop(labels='Ticket', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Name</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name Title\n",
       "0                            Braund, Mr. Owen Harris    Mr\n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...   Mrs\n",
       "2                             Heikkinen, Miss. Laina  Miss\n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)   Mrs\n",
       "4                           Allen, Mr. William Henry    Mr"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def get_title(passenger):\n",
    "    # extracts the title from the name variable\n",
    "    line = passenger\n",
    "    if re.search('Mrs', line):\n",
    "        return 'Mrs'\n",
    "    elif re.search('Mr', line):\n",
    "        return 'Mr'\n",
    "    elif re.search('Miss', line):\n",
    "        return 'Miss'\n",
    "    elif re.search('Master', line):\n",
    "        return 'Master'\n",
    "    else:\n",
    "        return 'Other'\n",
    "    \n",
    "df['Title'] = df['Name'].apply(get_title)\n",
    "df[['Name', 'Title']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# drop the original variable\n",
    "df.drop(labels='Name', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1     0.602694\n",
      "2     0.180696\n",
      "3     0.114478\n",
      "4     0.032548\n",
      "6     0.024691\n",
      "5     0.016835\n",
      "7     0.013468\n",
      "11    0.007856\n",
      "8     0.006734\n",
      "Name: Family_size, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15308b12f98>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a variable indicating family size (including the passenger)\n",
    "# sums siblings and parents\n",
    "\n",
    "df['Family_size'] = df['SibSp']+df['Parch']+1\n",
    "print(df.Family_size.value_counts()/ np.float(len(df)))\n",
    "\n",
    "(df.Family_size.value_counts() / np.float(len(df))).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The new variable Family size is discrete, because it is the sum of 2 discrete variables. It takes a finite number of values, and large families were rare on the Titanic. In fact, families larger than 7 people were rare, so I will cap family size at 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>is_mother</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Parch   Age  is_mother\n",
       "0    male      0  22.0          0\n",
       "1  female      0  38.0          0\n",
       "2  female      0  26.0          0\n",
       "3  female      0  35.0          0\n",
       "4    male      0  35.0          0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# variable indicating if passenger was a mother\n",
    "df['is_mother'] = np.where((df.Sex =='female')&(df.Parch>=1)&(df.Age>18),1,0)\n",
    "df[['Sex', 'Parch', 'Age', 'is_mother']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Sex</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Age</th>\n",
       "      <th>is_mother</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>female</td>\n",
       "      <td>5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex  Parch   Age  is_mother\n",
       "8    female      2  27.0          1\n",
       "25   female      5  38.0          1\n",
       "88   female      2  23.0          1\n",
       "98   female      1  34.0          1\n",
       "136  female      2  19.0          1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.is_mother==1, ['Sex', 'Parch', 'Age', 'is_mother']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there were 64 mothers in the Titanic\n"
     ]
    }
   ],
   "source": [
    "print('there were {} mothers in the Titanic'.format(df.is_mother.sum()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin_numerical     0.775533\n",
       "Ticket_numerical    0.004489\n",
       "is_mother           0.000000\n",
       "Family_size         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's look for missing data, outliers, cardinality and rare labels in the newly created variables.\n",
    "\n",
    "df[['Cabin_numerical', 'Ticket_numerical', 'is_mother', 'Family_size']].isnull().mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Cabin_numerical, as expected contains the same amount of missing data than the original variable Cabin.\n",
    "\n",
    "Ticket, also contains a small percentage of missing values. The other newly created variables do not contain missing data, as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Ticket number')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#New numerical variables: Outliers¶\n",
    "# first we plot the distributions to find out if they are Gaussian or skewed.\n",
    "# Depending on the distribution, we will use the normal assumption or the interquantile\n",
    "# range to find outliers\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "fig = df.Cabin_numerical.hist(bins=50)\n",
    "fig.set_ylabel('Number of passengers')\n",
    "fig.set_xlabel('Cabin number')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "fig = df.Ticket_numerical.hist(bins=50)\n",
    "fig.set_ylabel('Number of passengers')\n",
    "fig.set_xlabel('Ticket number')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ticket number')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's visualise outliers with the boxplot and whiskers\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "fig = df.boxplot(column='Cabin_numerical')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Cabin number')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "fig = df.boxplot(column='Ticket_numerical')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Ticket number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ticket number outliers are values < -981730.0 or > 1343691.0\n",
      "Number of passengers with ticket values higher than 1343691.0: 0.04938271604938271\n"
     ]
    }
   ],
   "source": [
    "# Ticket numerical\n",
    "\n",
    "IQR = df.Ticket_numerical.quantile(0.75) - df.Ticket_numerical.quantile(\n",
    "    0.25)\n",
    "Lower_fence = df.Ticket_numerical.quantile(0.25) - (IQR * 3)\n",
    "Upper_fence = df.Ticket_numerical.quantile(0.75) + (IQR * 3)\n",
    "print(\n",
    "    'Ticket number outliers are values < {lowerboundary} or > {upperboundary}'.\n",
    "    format(lowerboundary=Lower_fence, upperboundary=Upper_fence))\n",
    "passengers = len(df[df.Ticket_numerical > Upper_fence]) / np.float(\n",
    "    len(df))\n",
    "print('Number of passengers with ticket values higher than {upperboundary}: {passengers}'.format(upperboundary=Upper_fence, \\\n",
    "                                                                                                 passengers=passengers))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "I will use equal width discretisation for this variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin_categorical     0.771044\n",
       "Ticket_categorical    0.000000\n",
       "Title                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#New categorical variables: Missing values¶\n",
    "df[['Cabin_categorical', 'Ticket_categorical', 'Title']].isnull().mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As expected, Cabin contains the same amount of missing data as the original Cabin variable.\n",
    "\n",
    "The other 2 variables do not show missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cabin_categorical  contains  9  labels\n",
      "Ticket_categorical  contains  28  labels\n",
      "Title  contains  5  labels\n"
     ]
    }
   ],
   "source": [
    "#New categorical variables: cardinality¶\n",
    "\n",
    "for var in ['Cabin_categorical', 'Ticket_categorical', 'Title']:\n",
    "    print(var, ' contains ', len(df[var].unique()), ' labels')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Title and Cabin are not highly cardinal, Ticket on the other hand has quite a few labels. Let's explore the percentage of passengers within each label to identify rare labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C    0.066218\n",
      "B    0.052750\n",
      "D    0.037037\n",
      "E    0.035915\n",
      "A    0.016835\n",
      "F    0.014590\n",
      "G    0.004489\n",
      "T    0.001122\n",
      "Name: Cabin_categorical, dtype: float64\n",
      "\n",
      "NAN        0.741863\n",
      "PC         0.067340\n",
      "CA         0.046016\n",
      "A          0.031425\n",
      "STONO      0.020202\n",
      "SOTONOQ    0.016835\n",
      "SCPARIS    0.012346\n",
      "WC         0.011223\n",
      "SOC        0.006734\n",
      "FCC        0.005612\n",
      "C          0.005612\n",
      "LINE       0.004489\n",
      "SOPP       0.003367\n",
      "SCAH       0.003367\n",
      "PP         0.003367\n",
      "WEP        0.003367\n",
      "SOTONO     0.002245\n",
      "SWPP       0.002245\n",
      "PPP        0.002245\n",
      "SCA        0.001122\n",
      "AS         0.001122\n",
      "CASOTON    0.001122\n",
      "SP         0.001122\n",
      "SOP        0.001122\n",
      "FC         0.001122\n",
      "SCOW       0.001122\n",
      "SC         0.001122\n",
      "FA         0.001122\n",
      "Name: Ticket_categorical, dtype: float64\n",
      "\n",
      "Mr        0.581369\n",
      "Miss      0.202020\n",
      "Mrs       0.144781\n",
      "Master    0.044893\n",
      "Other     0.026936\n",
      "Name: Title, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#New categorical variables: rare labels¶\n",
    "# rare / infrequent labels (less than 1% of passengers)\n",
    "for var in ['Cabin_categorical', 'Ticket_categorical', 'Title']:\n",
    "    print(df[var].value_counts() / np.float(len(df)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((712, 16), (179, 16))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's separate into train and test set\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df, df.Survived, test_size=0.2,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's group again the variables into categorical or numerical\n",
    "# now considering the newly created variables\n",
    "\n",
    "categorical = [col for col in df.columns if df[col].dtypes == 'O']\n",
    "numerical= [col for col in df.columns if df[col].dtypes != 'O']\n",
    "                 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex', 'Embarked', 'Cabin_categorical', 'Ticket_categorical', 'Title']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass',\n",
       " 'Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Fare',\n",
       " 'Cabin_numerical',\n",
       " 'Ticket_numerical',\n",
       " 'Family_size',\n",
       " 'is_mother']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = [var for var in numerical if var not in ['Survived','PassengerId']]\n",
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Engineering missing values in numerical variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age 0.19803370786516855\n",
      "Cabin_numerical 0.776685393258427\n",
      "Ticket_numerical 0.0056179775280898875\n"
     ]
    }
   ],
   "source": [
    "# print variables with missing data\n",
    "for col in numerical:\n",
    "    if X_train[col].isnull().mean()>0:\n",
    "        print(col, X_train[col].isnull().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age and ticket contains < 50% NA: create additional variable with NA + random sample imputation\n",
    "\n",
    "Cabin_numerical contains > 50% NA: impute NA by value far in the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_na(X_train, df, variable):\n",
    "    # make temporary df copy\n",
    "    temp = df.copy()\n",
    "    \n",
    "    # extract random from train set to fill the na\n",
    "    random_sample = X_train[variable].dropna().sample(temp[variable].isnull().sum(), random_state=0)\n",
    "    \n",
    "    # pandas needs to have the same index in order to merge datasets\n",
    "    random_sample.index = temp[temp[variable].isnull()].index\n",
    "    temp.loc[temp[variable].isnull(), variable] = random_sample\n",
    "    return temp[variable]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age and ticket\n",
    "# add variable indicating missingness\n",
    "for df in [X_train, X_test]:\n",
    "    for var in ['Age', 'Ticket_numerical']:\n",
    "        df[var+'_NA'] = np.where(df[var].isnull(), 1, 0)\n",
    "    \n",
    "# replace by random sampling\n",
    "for df in [X_train, X_test]:\n",
    "    for var in ['Age', 'Ticket_numerical']:\n",
    "        df[var] = impute_na(X_train, df, var)\n",
    "    \n",
    "\n",
    "# Cabin numerical\n",
    "extreme = X_train.Cabin_numerical.mean() + X_train.Cabin_numerical.std()*3\n",
    "for df in [X_train, X_test]:\n",
    "    df.Cabin_numerical.fillna(extreme, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked 0.0028089887640449437\n",
      "Cabin_categorical 0.7710674157303371\n"
     ]
    }
   ],
   "source": [
    "#Engineering Missing Data in categorical variables \n",
    "# print variables with missing data\n",
    "for col in categorical:\n",
    "    if X_train[col].isnull().mean()>0:\n",
    "        print(col, X_train[col].isnull().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embarked NA imputed by most frequent category, because NA is low\n",
    "\n",
    "Cabin_categorical imputed by 'Missing', because NA is high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add label indicating 'Missing' to Cabin categorical\n",
    "# or replace by most frequent label in Embarked\n",
    "\n",
    "for df in [X_train, X_test]:\n",
    "    df['Embarked'].fillna(X_train['Embarked'].mode()[0], inplace=True)\n",
    "    df['Cabin_categorical'].fillna('Missing', inplace=True)\n",
    "    df['Ticket_categorical'].fillna('missing',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId            0\n",
       "Survived               0\n",
       "Pclass                 0\n",
       "Sex                    0\n",
       "Age                    0\n",
       "SibSp                  0\n",
       "Parch                  0\n",
       "Fare                   0\n",
       "Embarked               0\n",
       "Cabin_numerical        0\n",
       "Cabin_categorical      0\n",
       "Ticket_numerical       0\n",
       "Ticket_categorical     0\n",
       "Title                  0\n",
       "Family_size            0\n",
       "is_mother              0\n",
       "Age_NA                 0\n",
       "Ticket_numerical_NA    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check absence of null values\n",
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId            0\n",
       "Survived               0\n",
       "Pclass                 0\n",
       "Sex                    0\n",
       "Age                    0\n",
       "SibSp                  0\n",
       "Parch                  0\n",
       "Fare                   0\n",
       "Embarked               0\n",
       "Cabin_numerical        0\n",
       "Cabin_categorical      0\n",
       "Ticket_numerical       0\n",
       "Ticket_categorical     0\n",
       "Title                  0\n",
       "Family_size            0\n",
       "is_mother              0\n",
       "Age_NA                 0\n",
       "Ticket_numerical_NA    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Outliers in Numerical variables\n",
    "______________________________________________________________\n",
    "\n",
    "Age: top-coding (73)\n",
    "\n",
    "Fare: equal frequency binning\n",
    "\n",
    "Sibsp: top-coding (4)\n",
    "\n",
    "Parch: top-coding (2)\n",
    "\n",
    "Family Size: top-coding (7)\n",
    "\n",
    "Ticket_number: equal frequency binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_coding (df,var,top):\n",
    "   return np.where(df[var]>top,top,df[var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [X_train,X_test]:\n",
    "    df['Age'] = top_coding(df, 'Age', 73)\n",
    "    df['SibSp'] = top_coding(df, 'SibSp', 4)\n",
    "    df['Parch'] = top_coding(df, 'Parch', 2)\n",
    "    df['Family_size'] = top_coding(df, 'Family_size', 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age  max value:  73.0\n",
      "SibSp  max value:  4\n",
      "Parch  max value:  2\n",
      "Family_size  max value:  7\n"
     ]
    }
   ],
   "source": [
    "# let's check that it worked\n",
    "for var in ['Age',  'SibSp', 'Parch', 'Family_size']:\n",
    "    print(var, ' max value: ', X_train[var].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find quantiles and discretise train set\n",
    "X_train['Ticket_numerical'], bins = pd.qcut(x=X_train['Ticket_numerical'], q=8, retbins=True, precision=3, duplicates='raise')\n",
    "X_test['Ticket_numerical'] = pd.cut(x = X_test['Ticket_numerical'], bins=bins, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.Ticket_numerical.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2.999, 2816.875], (14737.5, 24421.875], (364848.0, 3101317.0], (24421.875, 113053.0], (248701.0, 347077.25], (113053.0, 248701.0], (2816.875, 14737.5], (347077.25, 364848.0]]\n",
       "Categories (8, interval[float64]): [(2.999, 2816.875] < (2816.875, 14737.5] < (14737.5, 24421.875] < (24421.875, 113053.0] < (113053.0, 248701.0] < (248701.0, 347077.25] < (347077.25, 364848.0] < (364848.0, 3101317.0]]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the ticket bins in training set\n",
    "X_train.Ticket_numerical.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# find quantiles and discretise train set\n",
    "X_train['Fare'], bins = pd.qcut(x=X_train['Fare'], q=8, retbins=True, precision=3, duplicates='raise')\n",
    "X_test['Fare'] = pd.cut(x = X_test['Fare'], bins=bins, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.Ticket_categorical = X_train.Ticket_categorical.apply(lambda x: re.sub(\"[^a-zA-Z]\", '', str(x)))\n",
    "X_test.Ticket_categorical = X_test.Ticket_categorical.apply(lambda x: re.sub(\"[^a-zA-Z]\", '', str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex male      0.65309\n",
      "female    0.34691\n",
      "Name: Sex, dtype: float64\n",
      "\n",
      "Embarked S    0.728933\n",
      "C    0.181180\n",
      "Q    0.089888\n",
      "Name: Embarked, dtype: float64\n",
      "\n",
      "Cabin_categorical Missing    0.771067\n",
      "C          0.061798\n",
      "B          0.050562\n",
      "E          0.040730\n",
      "D          0.036517\n",
      "A          0.021067\n",
      "F          0.012640\n",
      "G          0.004213\n",
      "T          0.001404\n",
      "Name: Cabin_categorical, dtype: float64\n",
      "\n",
      "Ticket_categorical NAN        0.728933\n",
      "PC         0.066011\n",
      "CA         0.050562\n",
      "A          0.035112\n",
      "STONO      0.023876\n",
      "SOTONOQ    0.016854\n",
      "SCPARIS    0.014045\n",
      "WC         0.014045\n",
      "SOC        0.007022\n",
      "C          0.005618\n",
      "LINE       0.005618\n",
      "WEP        0.004213\n",
      "PP         0.004213\n",
      "PPP        0.002809\n",
      "SOTONO     0.002809\n",
      "SOPP       0.002809\n",
      "FCC        0.002809\n",
      "SCAH       0.002809\n",
      "SP         0.001404\n",
      "FC         0.001404\n",
      "AS         0.001404\n",
      "FA         0.001404\n",
      "SCA        0.001404\n",
      "SCOW       0.001404\n",
      "SWPP       0.001404\n",
      "Name: Ticket_categorical, dtype: float64\n",
      "\n",
      "Title Mr        0.589888\n",
      "Miss      0.200843\n",
      "Mrs       0.140449\n",
      "Master    0.049157\n",
      "Other     0.019663\n",
      "Name: Title, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Engineering rare labels in categorical variables \n",
    "for var in categorical:\n",
    "    print(var, X_train[var].value_counts()/np.float(len(X_train)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Cabin contains the rare labels G and T: replace by most frequent category\n",
    "\n",
    "Ticket contains a lot of infrequent labels: replace by rare\n",
    "Because the number of passengers in the rare cabins is so small, grouping them into a new category called rare, will be in itself rare, and may be prone to over-fitting.\n",
    "Thus, in cabin, I will replace rare labels by the most frequent category. In ticket_categorical, on the other hand, the number of infrequent labels is high, therefore grouping them into a new label makes sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rare_imputation(variable, which='rare'):    \n",
    "    # find frequent labels\n",
    "    temp = X_train.groupby([variable])[variable].count()/np.float(len(X_train))\n",
    "    frequent_cat = [x for x in temp.loc[temp>0.01].index.values]\n",
    "    \n",
    "    # create new variables, with Rare labels imputed\n",
    "    if which=='frequent':\n",
    "        # find the most frequent category\n",
    "        mode_label = X_train.groupby(variable)[variable].count().sort_values().tail(1).index.values[0]\n",
    "        X_train[variable] = np.where(X_train[variable].isin(frequent_cat), X_train[variable], mode_label)\n",
    "        X_test[variable] = np.where(X_test[variable].isin(frequent_cat), X_test[variable], mode_label)\n",
    "    \n",
    "    else:\n",
    "        X_train[variable] = np.where(X_train[variable].isin(frequent_cat), X_train[variable], 'Rare')\n",
    "        X_test[variable] = np.where(X_test[variable].isin(frequent_cat), X_test[variable], 'Rare')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "rare_imputation('Cabin_categorical', 'frequent')\n",
    "rare_imputation('Ticket_categorical', 'rare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex male      0.65309\n",
      "female    0.34691\n",
      "Name: Sex, dtype: float64\n",
      "\n",
      "Embarked S    0.728933\n",
      "C    0.181180\n",
      "Q    0.089888\n",
      "Name: Embarked, dtype: float64\n",
      "\n",
      "Cabin_categorical Missing    0.776685\n",
      "C          0.061798\n",
      "B          0.050562\n",
      "E          0.040730\n",
      "D          0.036517\n",
      "A          0.021067\n",
      "F          0.012640\n",
      "Name: Cabin_categorical, dtype: float64\n",
      "\n",
      "Ticket_categorical NAN        0.728933\n",
      "PC         0.066011\n",
      "Rare       0.050562\n",
      "CA         0.050562\n",
      "A          0.035112\n",
      "STONO      0.023876\n",
      "SOTONOQ    0.016854\n",
      "SCPARIS    0.014045\n",
      "WC         0.014045\n",
      "Name: Ticket_categorical, dtype: float64\n",
      "\n",
      "Title Mr        0.589888\n",
      "Miss      0.200843\n",
      "Mrs       0.140449\n",
      "Master    0.049157\n",
      "Other     0.019663\n",
      "Name: Title, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# let's check that it worked\n",
    "for var in categorical:\n",
    "    print(var, X_train[var].value_counts()/np.float(len(X_train)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex', 'Embarked', 'Cabin_categorical', 'Ticket_categorical', 'Title']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Encode categorical variables\n",
    "categorical\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sex: one hot encoding\n",
    "Remaining variables: replace by risk probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [X_train, X_test]:\n",
    "    df['Sex']  = pd.get_dummies(df.Sex, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=uint64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Sex.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex [1 0]\n",
      "Embarked ['C' 'S' 'Q']\n",
      "Cabin_categorical ['Missing' 'B' 'C' 'E' 'D' 'F']\n",
      "Ticket_categorical ['NAN' 'Rare' 'PC' 'CA' 'SCPARIS' 'SOTONOQ' 'STONO' 'A']\n",
      "Title ['Mr' 'Master' 'Mrs' 'Miss' 'Other']\n"
     ]
    }
   ],
   "source": [
    "for var in categorical:\n",
    "    print(var,df[var].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_categorical_variables(var, target):\n",
    "        # make label to risk dictionary\n",
    "        ordered_labels = X_train.groupby([var])[target].mean().to_dict()\n",
    "        \n",
    "        # encode variables\n",
    "        X_train[var] = X_train[var].map(ordered_labels)\n",
    "        X_test[var] = X_test[var].map(ordered_labels)\n",
    "\n",
    "# enccode labels in categorical vars\n",
    "for var in categorical:\n",
    "    encode_categorical_variables(var, 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex [0.19139785 0.74493927]\n",
      "Embarked [0.52713178 0.34296724 0.421875  ]\n",
      "Cabin_categorical [0.3074141  0.72222222 0.54545455 0.75862069 0.69230769 0.66666667]\n",
      "Ticket_categorical [0.39499037 0.30555556 0.61702128 0.5        0.16666667 0.41176471\n",
      " 0.08      ]\n",
      "Title [0.15714286 0.57142857 0.79       0.70629371 0.5       ]\n"
     ]
    }
   ],
   "source": [
    "for var in categorical:\n",
    "    print(var,df[var].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=[(-0.001, 7.75], (7.75, 7.918], (7.918, 10.353], (10.353, 14.456], (14.456, 24.15], (24.15, 31.275], (31.275, 66.6], (66.6, 512.329]],\n",
       "              ordered=True)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Fare.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse discretised variables(the numerical varibles which we convrted before) to object before encoding\n",
    "for df in [X_train, X_test]:\n",
    "    df.Fare = df.Fare.astype('O')\n",
    "    df.Ticket_numerical = df.Ticket_numerical.astype('O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Fare.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fare\n",
      "Ticket_numerical\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# encode numerical labels\n",
    "for var in ['Fare', 'Ticket_numerical']:\n",
    "    print(var)\n",
    "    encode_categorical_variables(var, 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Cabin_numerical</th>\n",
       "      <th>Cabin_categorical</th>\n",
       "      <th>Ticket_numerical</th>\n",
       "      <th>Ticket_categorical</th>\n",
       "      <th>Title</th>\n",
       "      <th>Family_size</th>\n",
       "      <th>is_mother</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Ticket_numerical_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.744939</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.397849</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.348315</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>440</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.404494</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.573034</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.494118</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.348315</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>379</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223214</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.348315</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>492</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223214</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.306818</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass       Sex   Age  SibSp  Parch      Fare  \\\n",
       "140          141         0       3  0.744939  27.0      0      2  0.397849   \n",
       "439          440         0       2  0.191398  31.0      0      0  0.404494   \n",
       "817          818         0       2  0.191398  31.0      1      1  0.494118   \n",
       "378          379         0       3  0.191398  20.0      0      0  0.223214   \n",
       "491          492         0       3  0.191398  21.0      0      0  0.223214   \n",
       "\n",
       "     Embarked  Cabin_numerical  Cabin_categorical  Ticket_numerical  \\\n",
       "140  0.527132       158.222059           0.307414          0.348315   \n",
       "439  0.342967       158.222059           0.307414          0.573034   \n",
       "817  0.527132       158.222059           0.307414          0.348315   \n",
       "378  0.527132       158.222059           0.307414          0.348315   \n",
       "491  0.342967       158.222059           0.307414          0.306818   \n",
       "\n",
       "     Ticket_categorical     Title  Family_size  is_mother  Age_NA  \\\n",
       "140            0.394990  0.790000            3          0       1   \n",
       "439            0.305556  0.157143            1          0       0   \n",
       "817            0.500000  0.157143            3          0       0   \n",
       "378            0.394990  0.157143            1          0       0   \n",
       "491            0.166667  0.157143            1          0       0   \n",
       "\n",
       "     Ticket_numerical_NA  \n",
       "140                    0  \n",
       "439                    0  \n",
       "817                    0  \n",
       "378                    0  \n",
       "491                    0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#let's inspect the dataset\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the labels how now been replaced by the survival probability.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <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",
       "      <th>Cabin_numerical</th>\n",
       "      <th>Cabin_categorical</th>\n",
       "      <th>Ticket_numerical</th>\n",
       "      <th>Ticket_categorical</th>\n",
       "      <th>Title</th>\n",
       "      <th>Family_size</th>\n",
       "      <th>is_mother</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Ticket_numerical_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>712.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>443.283708</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>2.317416</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>29.807584</td>\n",
       "      <td>0.484551</td>\n",
       "      <td>0.353933</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>133.956178</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>0.383427</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>0.067416</td>\n",
       "      <td>0.198034</td>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>260.003783</td>\n",
       "      <td>0.486563</td>\n",
       "      <td>0.833767</td>\n",
       "      <td>0.263664</td>\n",
       "      <td>14.496489</td>\n",
       "      <td>0.907718</td>\n",
       "      <td>0.673811</td>\n",
       "      <td>0.144616</td>\n",
       "      <td>0.071238</td>\n",
       "      <td>48.398112</td>\n",
       "      <td>0.149408</td>\n",
       "      <td>0.118238</td>\n",
       "      <td>0.098434</td>\n",
       "      <td>0.276532</td>\n",
       "      <td>1.460148</td>\n",
       "      <td>0.250917</td>\n",
       "      <td>0.398798</td>\n",
       "      <td>0.074795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.212121</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>211.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.232770</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.321087</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>441.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>28.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397849</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.370787</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>671.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.744939</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.421875</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>0.474719</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.706294</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>890.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.744939</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.659091</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.573034</td>\n",
       "      <td>0.617021</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Sex         Age  \\\n",
       "count   712.000000  712.000000  712.000000  712.000000  712.000000   \n",
       "mean    443.283708    0.383427    2.317416    0.383427   29.807584   \n",
       "std     260.003783    0.486563    0.833767    0.263664   14.496489   \n",
       "min       1.000000    0.000000    1.000000    0.191398    0.670000   \n",
       "25%     211.750000    0.000000    2.000000    0.191398   21.000000   \n",
       "50%     441.000000    0.000000    3.000000    0.191398   28.750000   \n",
       "75%     671.250000    1.000000    3.000000    0.744939   38.000000   \n",
       "max     890.000000    1.000000    3.000000    0.744939   73.000000   \n",
       "\n",
       "            SibSp       Parch        Fare    Embarked  Cabin_numerical  \\\n",
       "count  712.000000  712.000000  712.000000  712.000000       712.000000   \n",
       "mean     0.484551    0.353933    0.383427    0.383427       133.956178   \n",
       "std      0.907718    0.673811    0.144616    0.071238        48.398112   \n",
       "min      0.000000    0.000000    0.212121    0.342967         2.000000   \n",
       "25%      0.000000    0.000000    0.232770    0.342967       158.222059   \n",
       "50%      0.000000    0.000000    0.397849    0.342967       158.222059   \n",
       "75%      1.000000    0.000000    0.444444    0.421875       158.222059   \n",
       "max      4.000000    2.000000    0.659091    0.527132       158.222059   \n",
       "\n",
       "       Cabin_categorical  Ticket_numerical  Ticket_categorical       Title  \\\n",
       "count         712.000000        712.000000          712.000000  712.000000   \n",
       "mean            0.383427          0.383427            0.383427    0.383427   \n",
       "std             0.149408          0.118238            0.098434    0.276532   \n",
       "min             0.307414          0.166667            0.080000    0.157143   \n",
       "25%             0.307414          0.321087            0.394990    0.157143   \n",
       "50%             0.307414          0.370787            0.394990    0.157143   \n",
       "75%             0.307414          0.474719            0.394990    0.706294   \n",
       "max             0.758621          0.573034            0.617021    0.790000   \n",
       "\n",
       "       Family_size   is_mother      Age_NA  Ticket_numerical_NA  \n",
       "count   712.000000  712.000000  712.000000           712.000000  \n",
       "mean      1.875000    0.067416    0.198034             0.005618  \n",
       "std       1.460148    0.250917    0.398798             0.074795  \n",
       "min       1.000000    0.000000    0.000000             0.000000  \n",
       "25%       1.000000    0.000000    0.000000             0.000000  \n",
       "50%       1.000000    0.000000    0.000000             0.000000  \n",
       "75%       2.000000    0.000000    0.000000             0.000000  \n",
       "max       7.000000    1.000000    1.000000             1.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature scaling\n",
    "\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass',\n",
       " 'Sex',\n",
       " 'Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Fare',\n",
       " 'Embarked',\n",
       " 'Cabin_numerical',\n",
       " 'Cabin_categorical',\n",
       " 'Ticket_numerical',\n",
       " 'Ticket_categorical',\n",
       " 'Title',\n",
       " 'Family_size',\n",
       " 'is_mother',\n",
       " 'Age_NA',\n",
       " 'Ticket_numerical_NA']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_vars = [var for var in X_train.columns if var not in ['PassengerId', 'Survived']]\n",
    "training_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass                   int64\n",
       "Sex                    float64\n",
       "Age                    float64\n",
       "SibSp                    int64\n",
       "Parch                    int64\n",
       "Fare                   float64\n",
       "Embarked               float64\n",
       "Cabin_numerical        float64\n",
       "Cabin_categorical      float64\n",
       "Ticket_numerical       float64\n",
       "Ticket_categorical     float64\n",
       "Title                  float64\n",
       "Family_size              int64\n",
       "is_mother                int32\n",
       "Age_NA                   int32\n",
       "Ticket_numerical_NA      int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[training_vars].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit scaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler() # create an instance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train[training_vars]) #  fit  the scaler to the train set and then transform it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb train roc-auc: 0.9388929218086394\n",
      "xgb test roc-auc: 0.9077733860342556\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "xgb_model = xgb.XGBClassifier()\n",
    "\n",
    "eval_set = [(X_test[training_vars], y_test)]\n",
    "xgb_model.fit(X_train[training_vars], y_train, eval_metric=\"auc\", eval_set=eval_set, verbose=False)\n",
    "\n",
    "pred = xgb_model.predict_proba(X_train[training_vars])\n",
    "print('xgb train roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "pred = xgb_model.predict_proba(X_test[training_vars])\n",
    "print('xgb test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF train roc-auc: 0.9988318439343495\n",
      "RF test roc-auc: 0.8747694334650856\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "rf_model = RandomForestClassifier()\n",
    "rf_model.fit(X_train[training_vars], y_train)\n",
    "\n",
    "pred = rf_model.predict_proba(X_train[training_vars])\n",
    "print('RF train roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "pred = rf_model.predict_proba(X_test[training_vars])\n",
    "print('RF test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaboost train roc-auc: 0.897344113745025\n",
      "Adaboost test roc-auc: 0.8706192358366271\n"
     ]
    }
   ],
   "source": [
    "ada_model = AdaBoostClassifier()\n",
    "ada_model.fit(X_train[training_vars], y_train)\n",
    "\n",
    "pred = ada_model.predict_proba(X_train[training_vars])\n",
    "print('Adaboost train roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "pred = ada_model.predict_proba(X_test[training_vars])\n",
    "print('Adaboost test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logit train roc-auc: 0.8724915934483133\n",
      "Logit test roc-auc: 0.8268774703557312\n"
     ]
    }
   ],
   "source": [
    "logit_model = LogisticRegression()\n",
    "logit_model.fit(scaler.transform(X_train[training_vars]), y_train)\n",
    "\n",
    "pred = logit_model.predict_proba(scaler.transform(X_train[training_vars]))\n",
    "print('Logit train roc-auc: {}'.format(roc_auc_score(y_train, pred[:,1])))\n",
    "pred = ada_model.predict_proba(scaler.transform(X_test[training_vars]))\n",
    "print('Logit test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
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