PROJECT START.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to handle datasets\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    " \n",
    "# for text / string processing\n",
    "import re\n",
    " \n",
    "# for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    " \n",
    "# to divide train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "# feature scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    " \n",
    "# for tree binarisation\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    " \n",
    " \n",
    "# to build the models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import tree \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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('titanic.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's inspect the type of variables in pandas\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find categorical variables\n",
    "categorical = [var for var in data.columns if data[var].dtype =='O']\n",
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = [var for var in data.columns if data[var].dtype != 'O']\n",
    "numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.000000\n",
       "Survived       0.000000\n",
       "Pclass         0.000000\n",
       "Name           0.000000\n",
       "Sex            0.000000\n",
       "Age            0.198653\n",
       "SibSp          0.000000\n",
       "Parch          0.000000\n",
       "Ticket         0.000000\n",
       "Fare           0.000000\n",
       "Cabin          0.771044\n",
       "Embarked       0.002245\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Fare')"
      ]
     },
     "execution_count": 8,
     "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 make boxplots to visualise outliers in the continuous variables \n",
    "# Age and Fare\n",
    "# let's make boxplots to visualise outliers in the continuous variables \n",
    "# Age and Fare\n",
    " \n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "fig = data.boxplot(column='Age')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Age')\n",
    " \n",
    "plt.subplot(1, 2, 2)\n",
    "fig = data.boxplot(column='Fare')\n",
    "fig.set_title('')\n",
    "fig.set_ylabel('Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Fare')"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "fig = data.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 = data.Fare.hist(bins = 20)\n",
    "fig.set_ylabel('Number of passengers')\n",
    "fig.set_xlabel('Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age outliers are values < -13.8803743499 or > 73.2786096441\n",
      "Fare outliers are values < -61.3584 or > 100.2688\n"
     ]
    }
   ],
   "source": [
    "# find outliers\n",
    " \n",
    "# Age\n",
    "Upper_boundary = data.Age.mean() + 3* data.Age.std()\n",
    "Lower_boundary = data.Age.mean() - 3* data.Age.std()\n",
    "print('Age outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_boundary, upperboundary=Upper_boundary))\n",
    " \n",
    "# Fare\n",
    "IQR = data.Fare.quantile(0.75) - data.Fare.quantile(0.25)\n",
    "Lower_fence = data.Fare.quantile(0.25) - (IQR * 3)\n",
    "Upper_fence = data.Fare.quantile(0.75) + (IQR * 3)\n",
    "print('Fare outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "for var in ['Pclass', 'SibSp', 'Parch']:\n",
    "    print(data[var].value_counts()/np.float(len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name 891\n",
      "Sex 2\n",
      "Ticket 681\n",
      "Cabin 148\n",
      "Embarked 4\n"
     ]
    }
   ],
   "source": [
    "for var in categorical:\n",
    "    print var , len(data[var].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin\n",
    "data['Cabin_numerical'] = data.Cabin.str.extract('(\\d+)') # extracts number from string\n",
    "data['Cabin_numerical'] = data['Cabin_numerical'].astype('float') # parses the above variable to float type\n",
    " \n",
    "data['Cabin_categorical'] = data['Cabin'].str[0] # captures first letter of string (the letter of the cabin)\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "148\n"
     ]
    }
   ],
   "source": [
    "print len(data['Cabin_categorical'].unique()) \n",
    "print len(data['Cabin'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the original variable\n",
    "data.drop(labels='Cabin', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Ticket\n",
    "# extract the last bit of ticket as number\n",
    "data['Ticket_numerical'] = data.Ticket.apply(lambda s: s.split()[-1])\n",
    "data['Ticket_numerical'] = np.where(data.Ticket_numerical.str.isdigit(), data.Ticket_numerical, np.nan)\n",
    "data['Ticket_numerical'] = data['Ticket_numerical'].astype('float')\n",
    " \n",
    "# extract the first part of ticket as category\n",
    "data['Ticket_categorical'] = data.Ticket.apply(lambda s: s.split()[0])\n",
    "data['Ticket_categorical'] = np.where(data.Ticket_categorical.str.isdigit(), np.nan, data.Ticket_categorical)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n",
      "681\n"
     ]
    }
   ],
   "source": [
    "print len(data['Ticket_categorical'].unique()) \n",
    "print len(data['Ticket'].unique())"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_numerical</th>\n",
       "      <th>Cabin_categorical</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21171.0</td>\n",
       "      <td>A/5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>85.0</td>\n",
       "      <td>C</td>\n",
       "      <td>17599.0</td>\n",
       "      <td>PC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3101282.0</td>\n",
       "      <td>STON/O2.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>123.0</td>\n",
       "      <td>C</td>\n",
       "      <td>113803.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>373450.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  Cabin_numerical  \\\n",
       "0      0         A/5 21171   7.2500        S              NaN   \n",
       "1      0          PC 17599  71.2833        C             85.0   \n",
       "2      0  STON/O2. 3101282   7.9250        S              NaN   \n",
       "3      0            113803  53.1000        S            123.0   \n",
       "4      0            373450   8.0500        S              NaN   \n",
       "\n",
       "  Cabin_categorical  Ticket_numerical Ticket_categorical  \n",
       "0               NaN           21171.0                A/5  \n",
       "1                 C           17599.0                 PC  \n",
       "2               NaN         3101282.0           STON/O2.  \n",
       "3                 C          113803.0                NaN  \n",
       "4               NaN          373450.0                NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_numerical</th>\n",
       "      <th>Cabin_categorical</th>\n",
       "      <th>Ticket_numerical</th>\n",
       "      <th>Ticket_categorical</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21171.0</td>\n",
       "      <td>A/5</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>85.0</td>\n",
       "      <td>C</td>\n",
       "      <td>17599.0</td>\n",
       "      <td>PC</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3101282.0</td>\n",
       "      <td>STON/O2.</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>123.0</td>\n",
       "      <td>C</td>\n",
       "      <td>113803.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>373450.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  Cabin_numerical  \\\n",
       "0      0         A/5 21171   7.2500        S              NaN   \n",
       "1      0          PC 17599  71.2833        C             85.0   \n",
       "2      0  STON/O2. 3101282   7.9250        S              NaN   \n",
       "3      0            113803  53.1000        S            123.0   \n",
       "4      0            373450   8.0500        S              NaN   \n",
       "\n",
       "  Cabin_categorical  Ticket_numerical Ticket_categorical Title  \n",
       "0               NaN           21171.0                A/5    Mr  \n",
       "1                 C           17599.0                 PC   Mrs  \n",
       "2               NaN         3101282.0           STON/O2.  Miss  \n",
       "3                 C          113803.0                NaN   Mrs  \n",
       "4               NaN          373450.0                NaN    Mr  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_title(passenger):\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",
    "    \n",
    "    \n",
    "data['Title'] = data['Name'].apply(get_title)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(labels='Name', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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"
     ]
    }
   ],
   "source": [
    "data['Family_size'] = data['SibSp'] + data['Parch'] + 1 \n",
    "print( data['Family_size'].value_counts()/len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin_numerical     0.775533\n",
       "Ticket_numerical    0.004489\n",
       "Family_size         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Cabin_numerical', 'Ticket_numerical', 'Family_size']].isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Ticket_num')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cHIl+tDPKfQP7V3ej3L+S+2ZQkyRpDiLiscDHgVdl5vci4kLgr4Gs/p4PvByINrPnoxoyNwObAcbHx3NiYqLvGi/YspXzt7ffte9a2//jD1uj0WAQ66lEo9w3sH91N8r9K7lvnvooSdIsImJfmiFtS2Z+AiAz78/MhzLzZ8D7ePj0xt3AES2zrwLuW8x6JUn1Z1CTJKmLiAjgImBHZr6tpX1Fy2QvAm6v7l8FnBkR+0fEkcBRwI2LVa8kaTR46qMkSd2dALwE2B4R26q2PwfOiohjaJ7WuAv4A4DMvCMiLgfupHnFyFd4xUdJ0nwZ1KQWqzdd3XHcrvNOXcRKJJUiM79A+++dfarLPG8G3rxgRUmSRp6nPkqSJElSYQxqkiRJklQYg5okSZIkFcagJkmSJEmFMahJkiRJUmEMapIkSZJUGC/PL0mSBsqfOpGk/nlETZIkSZIKY1CTJEmSpMIY1CRJkiSpMAY1SZIkSSqMQU2SJEmSCmNQkyRJkqTCGNQkSZIkqTAGNUmSJEkqjEFNkiRJkgpjUJMkSZKkwhjUJEmSJKkwBjVJkiRJKoxBTZIkSZIKY1CTJEmSpMIY1CRJkiSpMH0FtYg4NCKuiIgvR8SOiHjmoAqTJEmSpKVqWZ/z/wPwz5l5RkTsBxw4gJokSZIkaUnrOahFxC8AvwOsB8jMnwA/GUxZkiRJkrR09XNE7UnAt4APRsTTgJuBczLzB60TRcQGYAPA2NgYjUZjXguZnJx8xDwb10z1VOwFW7Z2HLdm5SE9PeZstYwtn3+9nersVmOv66Tbtpi53udq+70Pdhy3cU1vtfSi1/q7rctB19hJr7UPS+s2H1v+8HO41/+rYarbum9V59olSdKj9RPUlgHHAn+SmTdExD8Am4D/1TpRZm4GNgOMj4/nxMTEvBbSaDRonWf9pqv7KLm9XWsnZp2mndlq2bhmivO393t2aVO3GntdJ90ec+Z6n6uFqKUXC1H/oGvspNfah6V1nbU+5xdrfQ1S3dZ9qzrXLkmSHq2fi4nsBnZn5g3V8BU0g5skSZIkqQ89B7XM/CZwT0Q8pWo6CbhzIFVJklSIiDgiIq6rrm58R0ScU7UfFhHXRMTd1d/HVe0REe+MiJ0RcVtE+CGmJGne+v0dtT8BtkTEbcAxwN/2X5IkSUWZAjZm5q8BxwOviIijaZ7uf21mHgVcWw0DPB84qrptAC5c/JIlSXXX1xeoMnMbMD6gWiRJKk5m7gH2VPe/HxE7gJXA6cBENdklQAN4XdX+ocxM4PrqN0dXVI8jSdKcDOZKF5IkLQERsRp4OnADMDYdvjJzT0Q8sZpsJXBPy2y7q7ZHBLV+r4rcTrerDS/mVUEX6gq6o3x101HuG9i/uhvl/pXcN4OaJElzEBGPBT4OvCozvxcRHSdt05aPaujzqsjtXLBla8erDS/mlVgX6gq6o3x101HuG9i/uhvl/pXct36/oyZJ0siLiH1phrQtmfmJqvn+iFhRjV8B7K3adwNHtMy+CrhvsWqVJI0Gg5okSV1E89DZRcCOzHxby6irgHXV/XXA1pb2l1ZXfzweeNDvp0mS5stTHyVJ6u4E4CXA9ojYVrX9OXAecHlEnA18A3hxNe5TwCnATuCHwMsWt1xJ0igwqEmS1EVmfoH23zuD5m+Izpw+gVcsaFGSpJHnqY+SJEmSVBiDmiRJkiQVxqAmSZIkSYUxqEmSJElSYQxqkiRJklQYg5okSZIkFcagJkmSJEmFMahJkiRJUmEMapIkSZJUGIOaJEmSJBXGoCZJkiRJhTGoSZIkSVJhDGqSJEmSVJhlwy5Ac7N609XDLkELoN123bhmivWbrmbXeacOoSJJkiSVwCNqkiRJklQYg5okSZIkFcagJkmSJEmFMahJkiRJUmEMapIkSZJUGIOaJEmSJBXGoCZJkiRJhTGoSZIkSVJhDGqSJEmSVBiDmiRJkiQVxqAmSZIkSYUxqEmSJElSYQxqkiRJklQYg5okSZIkFcagJknSLCLiAxGxNyJub2l7Y0TcGxHbqtspLePOjYidEXFXRDxvOFVLkurMoCZJ0uwuBk5u0/72zDymun0KICKOBs4Efr2a539HxD6LVqkkaSQY1CRJmkVmfh74zhwnPx24LDN/nJlfB3YCxy1YcZKkkbRs2AVIklRjr4yIlwI3ARsz8wFgJXB9yzS7q7ZHiIgNwAaAsbExGo1G38WMLYeNa6bajhvE489Vpxr6rWNycnJR+7GYRrlvYP/qbpT7V3LfDGqSJPXmQuCvgaz+ng+8HIg20+ajGjI3A5sBxsfHc2Jiou+CLtiylfO3t9+171rb/+PP1fpNV3cc108djUaDQaynEo1y38D+1d0o96/kvnnqoyRJPcjM+zPzocz8GfA+Hj69cTdwRMukq4D7Frs+SVK9GdQkSepBRKxoGXwRMH1FyKuAMyNi/4g4EjgKuHGx65Mk1ZunPkqSNIuIuBSYAA6PiN3AG4CJiDiG5mmNu4A/AMjMOyLicuBOYAp4RWY+NIy6JUn11XdQqy45fBNwb2ae1n9JkiSVJTPPatN8UZfp3wy8eeEqkiSNukGc+ngOsGMAjyNJkiRJos+gFhGrgFOB9w+mHEmSJElSv6c+vgN4LXBwpwn6/Z2Ymb9t0O23WXrV628nzFZLt9+zKcEFW7Z2HHfkIft0XC/b732w43wb1/RWy6B/v6LX38RYqN/+mc/ypp833bbPmpWH9LS8btuuV63bvPU5X+pvknQz83nT6/rqdfv0o+TfgZEkSfPXc1CLiNOAvZl5c0RMdJqu39+JmfnbBt1+m6VXvf6my2y1bFwz1fH3bEp38ckHdfxNiZK2QSe9/ibGQv32z3yWN5fnzUI9Z/vVWvti/mbToAzq9WYYfS/5d2AkSdL89XPq4wnACyJiF3AZcGJEfGQgVUmSJEnSEtZzUMvMczNzVWauBs4EPpeZvzewyiRJkiRpifIHryVJkiSpMAP5AlVmNoDGIB5LkiRJkpY6j6hJkiRJUmEMapIkSZJUGIOaJEmSJBXGoCZJkiRJhTGoSZIkSVJhDGqSJEmSVBiDmiRJkiQVxqAmSZIkSYUxqEmSJElSYQxqkiRJklQYg5okSZIkFcagJkmSJEmFMahJkiRJUmEMapIkSZJUGIOaJEmSJBXGoCZJkiRJhTGoSZIkSVJhDGqSJEmSVBiDmiRJkiQVxqAmSZIkSYVZNuwCSrd609XDLmEott/7IOsXse+DXs8b10x1rX/XeacOdHmL/TzptrxB961XdahRmquI+ABwGrA3M59atR0GfBRYDewCfjczH4iIAP4BOAX4IbA+M28ZRt2SpPryiJokSbO7GDh5Rtsm4NrMPAq4thoGeD5wVHXbAFy4SDVKkkaIQU2SpFlk5ueB78xoPh24pLp/CfDClvYPZdP1wKERsWJxKpUkjQpPfZQkqTdjmbkHIDP3RMQTq/aVwD0t0+2u2va0zhwRG2gecWNsbIxGo9F/Qcubp363M4jHn6tONfRbx+Tk5KL2YzGNct/A/tXdKPev5L4Z1CRJGqxo05aPasjcDGwGGB8fz4mJib4XfMGWrZy/vf2ufdfa/h9/rrp+R7iPOhqNBoNYTyUa5b6B/au7Ue5fyX3z1EdJknpz//QpjdXfvVX7buCIlulWAfctcm2SpJozqEmS1JurgHXV/XXA1pb2l0bT8cCD06dISpI0V576KEnSLCLiUmACODwidgNvAM4DLo+Is4FvAC+uJv8UzUvz76R5ef6XLXrBkqTaM6hJkjSLzDyrw6iT2kybwCsWtiJJ0qjz1EdJkiRJKoxBTZIkSZIKY1CTJEmSpMIY1CRJkiSpMAY1SZIkSSqMQU2SJEmSCmNQkyRJkqTCGNQkSZIkqTAGNUmSJEkqjEFNkiRJkgpjUJMkSZKkwhjUJEmSJKkwBjVJkiRJKsyyYRcgSZIW3upNV3cct+u8UxexEknSXPR8RC0ijoiI6yJiR0TcERHnDLIwSZIkSVqq+jmiNgVszMxbIuJg4OaIuCYz7xxQbZIkSZK0JPV8RC0z92TmLdX97wM7gJWDKkySJEmSlqqBfEctIlYDTwduaDNuA7ABYGxsjEajMa/HnpycfMQ8G9dM9VxnJxds2dpx3MY1vT/u2PKFqXcx1Ll2mL3+Ttu82/ZeqOfJTP2u+27/Ywu9Teda+3xfBxbS9nsf/Pn9seWP3M69btdh9G/ma6UkSaq3voNaRDwW+Djwqsz83szxmbkZ2AwwPj6eExMT83r8RqNB6zzru3wZujQb10xx/vZ6Xq+lzrVDvevvt/Zdayc6jlvo/5+51t6txsXWuk4G9bwZRv9mvlZKkqR66+vy/BGxL82QtiUzPzGYkiRJkiRpaevnqo8BXATsyMy3Da4kSZIkSVra+jmidgLwEuDEiNhW3U4ZUF2SJEmStGT1/GWMzPwCEAOsRZIkSZLEgK76KEmSRtPqDhch2nXeqYtciSQtLX1dTESSJEmSNHgGNUmSJEkqjEFNkiRJkgpjUJMkSZKkwhjUJEmSJKkwXvVRkqQ+RMQu4PvAQ8BUZo5HxGHAR4HVwC7gdzPzgWHVKEmqH4+oSZLUv2dl5jGZOV4NbwKuzcyjgGurYUmS5sygJknS4J0OXFLdvwR44RBrkSTVkKc+SpLUnwQ+GxEJvDczNwNjmbkHIDP3RMQTZ84UERuADQBjY2M0Go2+CxlbDhvXTM17vm7L7vR4vcwz23yzmZycHMh6KtEo9w3sX92Ncv9K7ptBTZKk/pyQmfdVYeyaiPjyXGaqAt1mgPHx8ZyYmOi7kAu2bOX87fPfte9a23nZ6zddPbB5ZptvNo1Gg0GspxKNct/A/tXdKPev5L556qMkSX3IzPuqv3uBK4HjgPsjYgVA9Xfv8CqUJNWRQU2SpB5FxEERcfD0feC5wO3AVcC6arJ1wNbhVChJqitPfZQkqXdjwJURAc196j9m5j9HxBeByyPibOAbwIuHWKMkaR5Wdzt9+7xTF60Og5okST3KzK8BT2vT/p/ASYtfkSRpVHjqoyRJkiQVxqAmSZIkSYUxqEmSJElSYQxqkiRJklQYg5okSZIkFcarPkojptslZUuxEDV2u1zuYq+TUi7r200dapQkaSnziJokSZIkFcagJkmSJEmFMahJkiRJUmH8jpokSVo0fj9SkubGI2qSJEmSVBiPqEmSpHmrwxVmJanOPKImSZIkSYUxqEmSJElSYQxqkiRJklQYg5okSZIkFcagJkmSJEmFMahJkiRJUmEMapIkSZJUGIOaJEmSJBXGoCZJkiRJhTGoSZIkSVJhDGqSJEmSVBiDmiRJkiQVxqAmSZIkSYUxqEmSJElSYQxqkiRJklQYg5okSZIkFaavoBYRJ0fEXRGxMyI2DaooSZLqzn2kJKkfPQe1iNgHeDfwfOBo4KyIOHpQhUmSVFfuIyVJ/erniNpxwM7M/Fpm/gS4DDh9MGVJklRr7iMlSX2JzOxtxogzgJMz8/er4ZcAz8jMV86YbgOwoRp8CnDXPBd1OPDtnoocPmsfnjrXb+3DU+f6S6v9lzPzCcMuYljmso8cwP6xndKeB4M2yv0b5b6B/au7Ue7fMPo2p33ksj4WEG3aHpX6MnMzsLnnhUTclJnjvc4/TNY+PHWu39qHp87117n2ETXrPrLf/WPbhY7482CU+zfKfQP7V3ej3L+S+9bPqY+7gSNahlcB9/VXjiRJI8F9pCSpL/0EtS8CR0XEkRGxH3AmcNVgypIkqdbcR0qS+tLzqY+ZORURrwQ+A+wDfCAz7xhYZQ8b6Gkhi8zah6fO9Vv78NS5/jrXPnIWcR8506g/D0a5f6PcN7B/dTfK/Su2bz1fTESSJEmStDD6+sFrSZIkSdLgGdQkSZIkqTDFBrWIODki7oqInRGxadj1dBMRR0TEdRGxIyLuiIhzqvbDIuKaiLi7+vu4YdfaSUTsExFfiohPVsNHRsQNVe0frb4MX6SIODQiroiIL1fb4Jl1WfcR8WfVc+b2iLg0Ig4oed1HxAciYm9E3N7S1nZdR9M7q//h2yLi2OFV3rH2v6+eN7dFxJURcWjLuHOr2u+KiOcNp+qHtau/ZdxrIiIj4vBquKh1r4VXh31mROyKiO0RsS1oczi8AAALTklEQVQibqra5v36ERHrqunvjoh1Le2/WT3+zmre6LaMPvsykNfCQfVl0P/zHfr3xoi4t9p+2yLilJZxbV8vOz0vo8N+LiL2r4Z3VuNXz7aMHvs3r/dtddqGXfo2Etsvmu+TboyIW6v+vWnQNQ2y333LzOJuNL94/VXgScB+wK3A0cOuq0u9K4Bjq/sHA18Bjgb+DthUtW8C3jLsWrv04dXAPwKfrIYvB86s7r8H+KNh19il9kuA36/u7wccWod1D6wEvg4sb1nn60te98DvAMcCt7e0tV3XwCnAp2n+ntTxwA0F1v5cYFl1/y0ttR9dve7sDxxZvR7tU1r9VfsRNC9Y8R/A4SWue28L/tyoxT4T2DX9HG1pm9frB3AY8LXq7+Oq+4+rxt0IPLOa59PA87sto8++9P1aOMi+DPp/vkP/3gi8ps20bV8vuz0v6bCfA/4YeE91/0zgo92W0Uf/5vW+rU7bsEvfRmL7VevnsdX9fYEbqvU1kJoG2e9B3Ib+wt1hIzwT+EzL8LnAucOuax71bwWeA9wFrKjaVgB3Dbu2DvWuAq4FTgQ+Wf0TfJuH38A+YnuUdAN+gWbYiRntxa97mkHtHpov8Muqdf+80tc9sJpH7rzbrmvgvcBZ7aYrpfYZ414EbKnuP+I1h2YQemZp675quwJ4Gi1vgktc994W9HlRi30m7YPavF4/gLOA97a0v7dqWwF8uaX959Mt1P6g39fCQfZlIf7n2/TvjbR/o9/29bLT85Iu7zFaX2tp7he/XU2/oK/JzPK+ra7bcEbfRm77AQcCtwDPGFRNg+z3IJ6bpZ76OP0Gdtruqq141eHOp9NM+GOZuQeg+vvE4VXW1TuA1wI/q4YfD3w3M6eq4ZLX/5OAbwEfjOapm++PiIOowbrPzHuBtwLfAPYADwI3U591P63Tuq7b//HLaX6aCTWpPSJeANybmbfOGFWL+jUwddneCXw2Im6OiA1V23xfP7q1727T3m0ZgzbMvizWc+CV1Wl5H4iHTyGdb/+6vcf4+TzV+Aer6Resf3N831bLbTijbzAi2y+aX9fZBuwFrqF5BGxQNQ2y330rNahFm7Zc9CrmKSIeC3wceFVmfm/Y9cxFRJwG7M3Mm1ub20xa6vpfRvP0jAsz8+nAD2ieSlC86kXydJqH3H8JOAh4fptJS133s6nN8ygiXg9MAVumm9pMVlTtEXEg8HrgL9uNbtNWVP0aqLps7xMy81iar3OviIjf6TJtpz7Nt70Ei9GXxej/hcCvAMfQ/HDx/FmW3Uv/FnX7zuN9W+22YZu+jcz2y8yHMvMYmmeEHQf82gBrGmS/+1ZqUNtN83sX01YB9w2pljmJiH1p/kNsycxPVM33R8SKavwKmsm/NCcAL4iIXcBlNE9/fAdwaERM/yB6yet/N7A7M6c/LbqCZnCrw7p/NvD1zPxWZv4U+ATwW9Rn3U/rtK5r8X9cffn7NGBtVuctUI/af4VmyL+1+v9dBdwSEb9IPerX4NRie2fmfdXfvcCVNN9gzff1o1v7qjbtdFnGoA2zLwv+HMjM+6s3yD8D3kdz+3Vbdqf2b9N5P/fzearxhwDf6fJYPZvn+7ZabcN2fRu17Vf16btAg+Z31AZV0yD73bdSg9oXgaOqq6vsR/OLeVcNuaaOIiKAi4Admfm2llFXAeuq++tonidclMw8NzNXZeZqmuv5c5m5FrgOOKOarMjaATLzm8A9EfGUqukk4E5qsO5pnvJ4fEQcWD2Hpmuvxbpv0WldXwW8NJqOBx6cPt2jFBFxMvA64AWZ+cOWUVcBZ1ZXcjoSOIrmF8OLkZnbM/OJmbm6+v/dTfML5N+kButeA1X8PjMiDoqIg6fv07yQz+3M//XjM8BzI+Jx1VkJz6X5PZE9wPcj4vjq9fSlMx5rMfYHw+zLgv/PT4eLyotobr/pZbd7vWz7vKw+EOu0n2vt3xk035Nkl2X02pf5vm+rzTbs1LdR2X4R8YSortAcEctpfui9Y4A1DbLf/RvEF90W4kbz6jdfoXne6euHXc8stf42zUOctwHbqtspNM9PvRa4u/p72LBrnaUfEzx81ccnVU/YncDHgP2HXV+Xuo8BbqrW/z/RvPJSLdY98CbgyzRfMD9M8+pDxa574FKap0z8lGYwOLvTuqZ5KsC7q//h7cB4gbXvpHle+fT/7Xtapn99VftdVFfrKq3+GeN38fDFRIpa994W5flR9D6zel27tbrdMV1jL68fNL9PurO6vaylfbx6Lf0q8C6qL/MvxP5gUK+Fg+rLoP/nO/Tvw9Vj30bzjemKlunbvl52el7SYT8HHFAN76zGP2m2ZfTYv3m9b6vTNuzSt5HYfsBvAF+q+nE78JeDrmmQ/e73Nv2kkSRJkiQVotRTHyVJkiRpyTKoSZIkSVJhDGqSJEmSVBiDmiRJkiQVxqAmSZIkSYUxqEmSJElSYQxqWrIi4hcj4rKI+GpE3BkRn4qIX+0w7eqIuL3DuPdHxNELW60kSQsrIh4fEduq2zcj4t6W4X+bZd5GRIzPY1mviogD+69aGl3Lhl2ANAwREcCVwCWZeWbVdgwwRvNHDucsM39/8BVKkrS4MvM/gWMAIuKNwGRmvnWBFvcq4CPADxfo8aXa84ialqpnAT/NzPdMN2TmNuBLEXFtRNwSEdsj4vSWeZZFxCURcVtEXDH9SWDrp4gRMRkRb46IWyPi+ogY61RARFwcEe+MiH+LiK9FxBlV+0REfLJlundFxPrq/q6I+NuI+PeIuCkijo2Iz1RHBf9wkCtIkqRpETHZcv+11T7y1og4b8Z0j6n2lX9TDT+32mfdEhEfi4jHRsSfAr8EXBcR13VbZrt9arX/PGNmbdX+8/9ExOUR8ZWIOC8i1kbEjVW9vzLYtSItLIOalqqnAje3af8R8KLMPJZmmDu/OvoG8BRgc2b+BvA94I/bzH8QcH1mPg34PPA/ZqljBfDbwGnAebNMO+2ezHwm8H+Bi4EzgOOBv5rj/JIk9SQing+8EHhGta/7u5bRy4AtwFcy8y8i4nDgL4BnV/vVm4BXZ+Y7gfuAZ2Xms7osbr77VICnAecAa4CXAL+amccB7wf+ZB5dlYbOoCY9UgB/GxG3Af8CrKR5OiQ0A9K/Vvc/QjNgzfQTYPpo2M3A6lmW90+Z+bPMvLNlObO5qvq7HbghM7+fmd8CfhQRh87xMSRJ6sWzgQ9m5g8BMvM7LePeC9yemW+uho8Hjgb+NSK2AeuAX57Hsua7TwX4YmbuycwfA18FPlu1b5/j/FIx/I6alqo7aB6Jmmkt8ATgNzPzpxGxCzigGpczpp05DM3TKafbH2L2/7Eft9yfPnI3xSM/RDmAR5qe52cz5v/ZHJYnSVI/gvb7P4B/A54VEedn5o+qaa/JzLN6XFanferP95PVWS/7tcwzc7/Yus90H6la8YialqrPAftHxM9Po4iI/0bzk769VUh7Fo/85O+/RMQzq/tnAV9YoNr+Azg6IvaPiEOAkxZoOZIkzddngZe3fE/7sJZxFwGfAj4WEcuA64ETIuLJ1bQHtlxd+fvAwT3WsAv4zer+6cC+PT6OVDSDmpak6hO6FwHPqS7EcQfwRpo7mPGIuInm0bUvt8y2A1hXnRZ5GHDhAtV2D3A5cBvNc/2/tBDLkSRpvjLzn2megn9TdTrja2aMfxtwC/Bh4D+B9cCl1b7zeuC/VpNuBj7d7WIiXbwP+O8RcSPwDOAHPTyGVLx4+IiyJEmSJKkEHlGTJEmSpML4pUppgUXE64EXz2j+WMtVsSRJWrIi4gZg/xnNL8nM7cOoRyqFpz5KkiRJUmE89VGSJEmSCmNQkyRJkqTCGNQkSZIkqTAGNUmSJEkqzP8HHhuXFNgOzw0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1 , 2 ,1) \n",
    "fig = data.Cabin_numerical.hist(bins = 50)\n",
    "fig.set_xlabel('Cabin_num')\n",
    "plt.subplot(1 , 2 , 2)\n",
    "fig = data.Ticket_numerical.hist(bins = 50)\n",
    "fig.set_xlabel('Ticket_num')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.figure(figsize = (15 , 6))\n",
    "plt.subplot(1 , 2 ,1)\n",
    "fig = data.boxplot(column = 'Cabin_numerical')\n",
    "\n",
    "plt.subplot(1 , 2 , 2)\n",
    "fig = data.boxplot(column = 'Ticket_numerical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ticket number outliers are values < -981730.0 or > 1343691.0\n"
     ]
    }
   ],
   "source": [
    "# Ticket numerical\n",
    "IQR = data.Ticket_numerical.quantile(0.75) - data.Ticket_numerical.quantile(0.25)\n",
    "Lower_fence = data.Ticket_numerical.quantile(0.25) - (IQR * 3)\n",
    "Upper_fence = data.Ticket_numerical.quantile(0.75) + (IQR * 3)\n",
    "print('Ticket number outliers are values < {lowerboundary} or > {upperboundary}'.format(lowerboundary=Lower_fence, upperboundary=Upper_fence))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0493827160494\n"
     ]
    }
   ],
   "source": [
    "b = np.where(data.Ticket_numerical > 1343691.0)\n",
    "print(len(b[0])/np.float(len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin_categorical     0.771044\n",
       "Ticket_categorical    0.741863\n",
       "Title                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Cabin_categorical', 'Ticket_categorical', 'Title']].isnull().mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": 28,
     "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 = data.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, data.Ticket_categorical], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = text.str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# process the variable in submission as well\n",
    "data['Ticket_categorical'] = text\n",
    "#data[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "for var in ['Cabin_categorical', 'Ticket_categorical', 'Title']:\n",
    "    print(var, ' contains ', len(data[var].unique()), ' labels')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
      "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",
      "PP         0.003367\n",
      "SCAH       0.003367\n",
      "WEP        0.003367\n",
      "SOTONO     0.002245\n",
      "SWPP       0.002245\n",
      "PPP        0.002245\n",
      "CASOTON    0.001122\n",
      "SCA        0.001122\n",
      "SCOW       0.001122\n",
      "AS         0.001122\n",
      "FC         0.001122\n",
      "FA         0.001122\n",
      "SP         0.001122\n",
      "SOP        0.001122\n",
      "SC         0.001122\n",
      "Name: Ticket_categorical, dtype: float64\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"
     ]
    }
   ],
   "source": [
    "for var in ['Cabin_categorical', 'Ticket_categorical', 'Title']:\n",
    "    print(data[var].value_counts()/len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((712, 16), (179, 16))"
      ]
     },
     "execution_count": 33,
     "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(data, data.Survived, test_size=0.2,\n",
    "                                                    random_state=0)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_categorical_and_numerical_variables(dataframe):\n",
    "    cat_vars = [col for col in data.columns if data[col].dtypes == 'O']\n",
    "    num_vars  = [col for col in data.columns if data[col].dtypes != 'O']\n",
    "    return cat_vars, num_vars\n",
    "                 \n",
    "categorical, numerical = find_categorical_and_numerical_variables(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex',\n",
       " 'Ticket',\n",
       " 'Embarked',\n",
       " 'Cabin_categorical',\n",
       " 'Ticket_categorical',\n",
       " 'Title']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass',\n",
       " 'Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Fare',\n",
       " 'Cabin_numerical',\n",
       " 'Ticket_numerical',\n",
       " 'Family_size']"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Age', 0.19803370786516855)\n",
      "('Cabin_numerical', 0.776685393258427)\n",
      "('Ticket_numerical', 0.0056179775280898875)\n"
     ]
    }
   ],
   "source": [
    "for col in numerical:\n",
    "    if(X_train[col].isnull().mean() > 0):\n",
    "        print(col , X_train[col].isnull().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</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>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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 18723</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>SCPARIS</td>\n",
       "      <td>Mr</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2648</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3101317.0</td>\n",
       "      <td>SOTONOQ</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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  \\\n",
       "140          141         0       3  female   NaN      0      2   \n",
       "439          440         0       2    male  31.0      0      0   \n",
       "817          818         0       2    male  31.0      1      1   \n",
       "378          379         0       3    male  20.0      0      0   \n",
       "491          492         0       3    male  21.0      0      0   \n",
       "\n",
       "               Ticket     Fare Embarked  Cabin_numerical Cabin_categorical  \\\n",
       "140              2678  15.2458        C              NaN               NaN   \n",
       "439        C.A. 18723  10.5000        S              NaN               NaN   \n",
       "817   S.C./PARIS 2079  37.0042        C              NaN               NaN   \n",
       "378              2648   4.0125        C              NaN               NaN   \n",
       "491  SOTON/OQ 3101317   7.2500        S              NaN               NaN   \n",
       "\n",
       "     Ticket_numerical Ticket_categorical Title  Family_size  Age_NA  \\\n",
       "140            2678.0                NAN   Mrs            3       1   \n",
       "439           18723.0                 CA    Mr            1       0   \n",
       "817            2079.0            SCPARIS    Mr            3       0   \n",
       "378            2648.0                NAN    Mr            1       0   \n",
       "491         3101317.0            SOTONOQ    Mr            1       0   \n",
       "\n",
       "     Ticket_numerical_NA  \n",
       "140                    0  \n",
       "439                    0  \n",
       "817                    0  \n",
       "378                    0  \n",
       "491                    0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.745183887915935"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meen = X_train.Age.mean()\n",
    "meen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [X_train , X_test]:\n",
    "    df['Age'].fillna(meen , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</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>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>female</td>\n",
       "      <td>29.745184</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 18723</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>SCPARIS</td>\n",
       "      <td>Mr</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2648</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3101317.0</td>\n",
       "      <td>SOTONOQ</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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  \\\n",
       "140          141         0       3  female  29.745184      0      2   \n",
       "439          440         0       2    male  31.000000      0      0   \n",
       "817          818         0       2    male  31.000000      1      1   \n",
       "378          379         0       3    male  20.000000      0      0   \n",
       "491          492         0       3    male  21.000000      0      0   \n",
       "\n",
       "               Ticket     Fare Embarked  Cabin_numerical Cabin_categorical  \\\n",
       "140              2678  15.2458        C              NaN               NaN   \n",
       "439        C.A. 18723  10.5000        S              NaN               NaN   \n",
       "817   S.C./PARIS 2079  37.0042        C              NaN               NaN   \n",
       "378              2648   4.0125        C              NaN               NaN   \n",
       "491  SOTON/OQ 3101317   7.2500        S              NaN               NaN   \n",
       "\n",
       "     Ticket_numerical Ticket_categorical Title  Family_size  Age_NA  \\\n",
       "140            2678.0                NAN   Mrs            3       1   \n",
       "439           18723.0                 CA    Mr            1       0   \n",
       "817            2079.0            SCPARIS    Mr            3       0   \n",
       "378            2648.0                NAN    Mr            1       0   \n",
       "491         3101317.0            SOTONOQ    Mr            1       0   \n",
       "\n",
       "     Ticket_numerical_NA  \n",
       "140                    0  \n",
       "439                    0  \n",
       "817                    0  \n",
       "378                    0  \n",
       "491                    0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x103a95b0>"
      ]
     },
     "execution_count": 43,
     "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": [
    "X_train.Ticket_numerical.hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113053.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = X_train.Ticket_numerical.median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [X_train , X_test]:\n",
    "    df['Ticket_numerical'].fillna(median , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</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>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>female</td>\n",
       "      <td>29.745184</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 18723</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>SCPARIS</td>\n",
       "      <td>Mr</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2648</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3101317.0</td>\n",
       "      <td>SOTONOQ</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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  \\\n",
       "140          141         0       3  female  29.745184      0      2   \n",
       "439          440         0       2    male  31.000000      0      0   \n",
       "817          818         0       2    male  31.000000      1      1   \n",
       "378          379         0       3    male  20.000000      0      0   \n",
       "491          492         0       3    male  21.000000      0      0   \n",
       "\n",
       "               Ticket     Fare Embarked  Cabin_numerical Cabin_categorical  \\\n",
       "140              2678  15.2458        C              NaN               NaN   \n",
       "439        C.A. 18723  10.5000        S              NaN               NaN   \n",
       "817   S.C./PARIS 2079  37.0042        C              NaN               NaN   \n",
       "378              2648   4.0125        C              NaN               NaN   \n",
       "491  SOTON/OQ 3101317   7.2500        S              NaN               NaN   \n",
       "\n",
       "     Ticket_numerical Ticket_categorical Title  Family_size  Age_NA  \\\n",
       "140            2678.0                NAN   Mrs            3       1   \n",
       "439           18723.0                 CA    Mr            1       0   \n",
       "817            2079.0            SCPARIS    Mr            3       0   \n",
       "378            2648.0                NAN    Mr            1       0   \n",
       "491         3101317.0            SOTONOQ    Mr            1       0   \n",
       "\n",
       "     Ticket_numerical_NA  \n",
       "140                    0  \n",
       "439                    0  \n",
       "817                    0  \n",
       "378                    0  \n",
       "491                    0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</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>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>female</td>\n",
       "      <td>29.745184</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>C</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 18723</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>CA</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>C</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>SCPARIS</td>\n",
       "      <td>Mr</td>\n",
       "      <td>3</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>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2648</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>C</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>NAN</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3101317.0</td>\n",
       "      <td>SOTONOQ</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</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  \\\n",
       "140          141         0       3  female  29.745184      0      2   \n",
       "439          440         0       2    male  31.000000      0      0   \n",
       "817          818         0       2    male  31.000000      1      1   \n",
       "378          379         0       3    male  20.000000      0      0   \n",
       "491          492         0       3    male  21.000000      0      0   \n",
       "\n",
       "               Ticket     Fare Embarked  Cabin_numerical Cabin_categorical  \\\n",
       "140              2678  15.2458        C       158.222059               NaN   \n",
       "439        C.A. 18723  10.5000        S       158.222059               NaN   \n",
       "817   S.C./PARIS 2079  37.0042        C       158.222059               NaN   \n",
       "378              2648   4.0125        C       158.222059               NaN   \n",
       "491  SOTON/OQ 3101317   7.2500        S       158.222059               NaN   \n",
       "\n",
       "     Ticket_numerical Ticket_categorical Title  Family_size  Age_NA  \\\n",
       "140            2678.0                NAN   Mrs            3       1   \n",
       "439           18723.0                 CA    Mr            1       0   \n",
       "817            2079.0            SCPARIS    Mr            3       0   \n",
       "378            2648.0                NAN    Mr            1       0   \n",
       "491         3101317.0            SOTONOQ    Mr            1       0   \n",
       "\n",
       "     Ticket_numerical_NA  \n",
       "140                    0  \n",
       "439                    0  \n",
       "817                    0  \n",
       "378                    0  \n",
       "491                    0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Embarked', 0.0028089887640449437)\n",
      "('Cabin_categorical', 0.7710674157303371)\n"
     ]
    }
   ],
   "source": [
    "for var in categorical:\n",
    "    if(X_train[var].isnull().mean() > 0):\n",
    "        print(var , X_train[var].isnull().mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S    0.726124\n",
      "C    0.181180\n",
      "Q    0.089888\n",
      "Name: Embarked, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((X_train['Embarked'].value_counts())/len(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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",
       "Ticket                 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",
       "Age_NA                 0\n",
       "Ticket_numerical_NA    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_code(df, variable, top):\n",
    "    return np.where(df[variable]>top, top, df[variable])\n",
    " \n",
    "for df in [X_train, X_test]:\n",
    "    df['Age'] = top_code(df, 'Age', 73)\n",
    "    df['SibSp'] = top_code(df, 'SibSp', 4)\n",
    "    df['Parch'] = top_code(df, 'Parch', 2)\n",
    "    df['Family_size'] = top_code(df, 'Family_size', 7)\n",
    "    df['Fare'] = top_code(df , 'Fare' , 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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",
      "('Fare', ' max value: ', 100.0)\n"
     ]
    }
   ],
   "source": [
    "for var in ['Age',  'SibSp', 'Parch', 'Family_size' , 'Fare']:\n",
    "    print(var, ' max value: ', X_train[var].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['Sex' , 'Embarked' , 'Cabin_categorical' , 'Ticket_categorical' , 'Title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Sex', male      0.521886\n",
      "female    0.277217\n",
      "Name: Sex, dtype: float64)\n",
      "('Embarked', S    0.582492\n",
      "C    0.144781\n",
      "Q    0.071829\n",
      "Name: Embarked, dtype: float64)\n",
      "('Cabin_categorical', Missing    0.616162\n",
      "C          0.049383\n",
      "B          0.040404\n",
      "E          0.032548\n",
      "D          0.029181\n",
      "A          0.016835\n",
      "F          0.010101\n",
      "G          0.003367\n",
      "T          0.001122\n",
      "Name: Cabin_categorical, dtype: float64)\n",
      "('Ticket_categorical', NAN        0.582492\n",
      "PC         0.052750\n",
      "CA         0.040404\n",
      "A          0.028058\n",
      "STONO      0.019080\n",
      "SOTONOQ    0.013468\n",
      "WC         0.011223\n",
      "SCPARIS    0.011223\n",
      "SOC        0.005612\n",
      "C          0.004489\n",
      "LINE       0.004489\n",
      "WEP        0.003367\n",
      "PP         0.003367\n",
      "FCC        0.002245\n",
      "PPP        0.002245\n",
      "SOTONO     0.002245\n",
      "SOPP       0.002245\n",
      "SCAH       0.002245\n",
      "SCA        0.001122\n",
      "SCOW       0.001122\n",
      "AS         0.001122\n",
      "FC         0.001122\n",
      "FA         0.001122\n",
      "SWPP       0.001122\n",
      "SP         0.001122\n",
      "Name: Ticket_categorical, dtype: float64)\n",
      "('Title', Mr        0.471380\n",
      "Miss      0.160494\n",
      "Mrs       0.112233\n",
      "Master    0.039282\n",
      "other     0.015713\n",
      "Name: Title, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "for var in categorical:\n",
    "    print(var , X_train[var].value_counts()/len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rare_imputation(variable, which='rare'):    \n",
    "    temp = X_train[variable].value_counts()/np.float(len(X_train))\n",
    "    f_cat = [x for x in temp.loc[temp > 0.01].index.values]\n",
    "    mode_label = X_train[variable].value_counts().sort_values().tail(1).index.values\n",
    "    if which == 'frequent':\n",
    "        mode_label = X_train[variable].value_counts().sort_values().tail(1).index.values\n",
    "        X_train[variable] = np.where(X_train[variable].isin(f_cat) , X_train[variable] , mode_label)\n",
    "        X_test[variable] = np.where(X_test[variable].isin(f_cat), X_test[variable], mode_label)\n",
    "    else:\n",
    "        X_train[variable] = np.where(X_train[variable].isin(f_cat) , X_train[variable] , 'Rare')\n",
    "        X_test[variable] = np.where(X_test[variable].isin(f_cat), X_test[variable], 'Rare')\n",
    "        \n",
    "        \n",
    "rare_imputation('Cabin_categorical', 'frequent')    \n",
    "rare_imputation('Ticket_categorical', 'rare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      "NAN        0.728933\n",
      "PC         0.066011\n",
      "CA         0.050562\n",
      "Rare       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"
     ]
    }
   ],
   "source": [
    "print X_train['Cabin_categorical'].value_counts()/np.float(len(X_train))\n",
    "print X_train['Ticket_categorical'].value_counts()/np.float(len(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex', 'Embarked', 'Cabin_categorical', 'Ticket_categorical', 'Title']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_categorical_variables(var, target):\n",
    "       \n",
    "        ordered_labels = X_train.groupby([var])[target].mean().to_dict()\n",
    "\n",
    "        X_train[var] = X_train[var].map(ordered_labels)\n",
    "        X_test[var] = X_test[var].map(ordered_labels)\n",
    "       \n",
    "        \n",
    "        \n",
    "\n",
    "for var in categorical:\n",
    "    encode_categorical_variables(var,'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</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>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>29.745184</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>3</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>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 18723</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S.C./PARIS 2079</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>3</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>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2648</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</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>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 3101317</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>3101317.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</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  \\\n",
       "140          141         0       3  0.744939  29.745184      0      2   \n",
       "439          440         0       2  0.191398  31.000000      0      0   \n",
       "817          818         0       2  0.191398  31.000000      1      1   \n",
       "378          379         0       3  0.191398  20.000000      0      0   \n",
       "491          492         0       3  0.191398  21.000000      0      0   \n",
       "\n",
       "               Ticket     Fare  Embarked  Cabin_numerical  Cabin_categorical  \\\n",
       "140              2678  15.2458  0.527132       158.222059           0.307414   \n",
       "439        C.A. 18723  10.5000  0.342967       158.222059           0.307414   \n",
       "817   S.C./PARIS 2079  37.0042  0.527132       158.222059           0.307414   \n",
       "378              2648   4.0125  0.527132       158.222059           0.307414   \n",
       "491  SOTON/OQ 3101317   7.2500  0.342967       158.222059           0.307414   \n",
       "\n",
       "     Ticket_numerical  Ticket_categorical     Title  Family_size  Age_NA  \\\n",
       "140            2678.0            0.394990  0.790000            3       1   \n",
       "439           18723.0            0.305556  0.157143            1       0   \n",
       "817            2079.0            0.500000  0.157143            3       0   \n",
       "378            2648.0            0.394990  0.157143            1       0   \n",
       "491         3101317.0            0.166667  0.157143            1       0   \n",
       "\n",
       "     Ticket_numerical_NA  \n",
       "140                    0  \n",
       "439                    0  \n",
       "817                    0  \n",
       "378                    0  \n",
       "491                    0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      21171.0\n",
       "1      17599.0\n",
       "2    3101282.0\n",
       "3     113803.0\n",
       "4     373450.0\n",
       "Name: Ticket_numerical, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Ticket_numerical'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Age_NA</th>\n",
       "      <th>Ticket_numerical_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>3</td>\n",
       "      <td>0.744939</td>\n",
       "      <td>29.745184</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>18723.0</td>\n",
       "      <td>0.305556</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>2</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0042</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>3</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0125</td>\n",
       "      <td>0.527132</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>2648.0</td>\n",
       "      <td>0.394990</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>3</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0.342967</td>\n",
       "      <td>158.222059</td>\n",
       "      <td>0.307414</td>\n",
       "      <td>3101317.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.157143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass       Sex        Age  SibSp  Parch     Fare  Embarked  \\\n",
       "140       3  0.744939  29.745184      0      2  15.2458  0.527132   \n",
       "439       2  0.191398  31.000000      0      0  10.5000  0.342967   \n",
       "817       2  0.191398  31.000000      1      1  37.0042  0.527132   \n",
       "378       3  0.191398  20.000000      0      0   4.0125  0.527132   \n",
       "491       3  0.191398  21.000000      0      0   7.2500  0.342967   \n",
       "\n",
       "     Cabin_numerical  Cabin_categorical  Ticket_numerical  Ticket_categorical  \\\n",
       "140       158.222059           0.307414            2678.0            0.394990   \n",
       "439       158.222059           0.307414           18723.0            0.305556   \n",
       "817       158.222059           0.307414            2079.0            0.500000   \n",
       "378       158.222059           0.307414            2648.0            0.394990   \n",
       "491       158.222059           0.307414         3101317.0            0.166667   \n",
       "\n",
       "        Title  Family_size  Age_NA  Ticket_numerical_NA  \n",
       "140  0.790000            3       1                    0  \n",
       "439  0.157143            1       0                    0  \n",
       "817  0.157143            3       0                    0  \n",
       "378  0.157143            1       0                    0  \n",
       "491  0.157143            1       0                    0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_vars = [var for var in X_train.columns if var not in ['PassengerId', 'Survived' , 'Ticket']]\n",
    "\n",
    "X_train[training_vars].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF train roc-auc: 0.999069647133\n",
      "RF test roc-auc: 0.917654808959\n"
     ]
    }
   ],
   "source": [
    "#Random Forest classifier\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])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adaboost train roc-auc: 0.919543250978\n",
      "Adaboost test roc-auc: 0.898880105402\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": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DT train roc-auc: 0.999995828014\n",
      "DT test roc-auc: 0.747035573123\n"
     ]
    }
   ],
   "source": [
    "#A single decision tree with min sample split as 2\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train[training_vars], y_train)\n",
    "pred = clf.predict_proba(X_train[training_vars])\n",
    "print('DT train roc-auc: {}'.format(roc_auc_score(+y_train, pred[:,1])))\n",
    "pred = clf.predict_proba(X_test[training_vars])\n",
    "print('DT test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DT train roc-auc: 0.923953040126\n",
      "DT test roc-auc: 0.874374176548\n"
     ]
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(min_samples_split = 50)\n",
    "clf = clf.fit(X_train[training_vars], y_train)\n",
    "pred = clf.predict_proba(X_train[training_vars])\n",
    "print('DT train roc-auc: {}'.format(roc_auc_score(+y_train, pred[:,1])))\n",
    "pred = clf.predict_proba(X_test[training_vars])\n",
    "print('DT test roc-auc: {}'.format(roc_auc_score(y_test, pred[:,1])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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