{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\npclass: A proxy for socio-economic status (SES)\\n1st = Upper\\n2nd = Middle\\n3rd = Lower\\n\\nage: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\\n\\nsibsp: The dataset defines family relations in this way...\\nSibling = brother, sister, stepbrother, stepsister\\nSpouse = husband, wife (mistresses and fiancés were ignored)\\n\\nparch: The dataset defines family relations in this way...\\nParent = mother, father\\nChild = daughter, son, stepdaughter, stepson\\nSome children travelled only with a nanny, therefore parch=0 for them.\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inference: https://www.kaggle.com/startupsci/titanic-data-science-solutions/notebook\n",
    "'''\n",
    "pclass: A proxy for socio-economic status (SES)\n",
    "1st = Upper\n",
    "2nd = Middle\n",
    "3rd = Lower\n",
    "\n",
    "age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "sibsp: The dataset defines family relations in this way...\n",
    "Sibling = brother, sister, stepbrother, stepsister\n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "parch: The dataset defines family relations in this way...\n",
    "Parent = mother, father\n",
    "Child = daughter, son, stepdaughter, stepson\n",
    "Some children travelled only with a nanny, therefore parch=0 for them.\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# acquire data\n",
    "train_df = pd.read_csv('https://github.com/TA-aiacademy/course_3.0/releases/download/Python/titanic_train.csv')\n",
    "test_df = pd.read_csv('https://github.com/TA-aiacademy/course_3.0/releases/download/Python/titanic_test.csv')\n",
    "combine = [train_df, test_df]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "# Cabin > Age > Embarked features contain a number of null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.head()\n",
    "test_df.info()\n",
    "# Cabin > Age are incomplete in case of test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print column names\n",
    "print(train_df.columns) \n",
    "\n",
    "# preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
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       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observe the numerical data\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Corn, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>1601</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name   Sex Ticket Cabin Embarked\n",
       "count               891   891    891   204      889\n",
       "unique              891     2    681   147        3\n",
       "top     Corn, Mr. Harry  male   1601    G6        S\n",
       "freq                  1   577      7     4      644"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observe the categorical data\n",
    "train_df.describe(include=['O'])  # string stype data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To confirm some of our observations and assumptions, we can quickly analyze our feature correlations by pivoting features against each other. We can only do so at this stage for features which do not have any empty values. It also makes sense doing so only for features which are categorical (Sex), ordinal (Pclass) or discrete (SibSp, Parch) type.\n",
    "\n",
    "Pclass We observe significant correlation (>0.5) among Pclass=1 and Survived. We decide to include this feature in our model.\n",
    "\n",
    "Sex We confirm the observation during problem definition that Sex=female had very high survival rate at 74%.\n",
    "\n",
    "SibSp and Parch These features have zero correlation for certain values. It may be best to derive a feature or a set of features from these individual features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Pclass\", \"Survived\"]].groupby(['Pclass'], as_index=False).mean().sort_values(by=\"Survived\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by=\"Survived\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['SibSp', \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by=\"Survived\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <th>5</th>\n",
       "      <td>5</td>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by=\"Survived\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "      <td>118</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
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       "      <td>678</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived  Sex\n",
       "3      3  0.600000    5\n",
       "1      1  0.550847  118\n",
       "2      2  0.500000   80\n",
       "0      0  0.343658  678\n",
       "5      5  0.200000    5\n",
       "4      4  0.000000    4\n",
       "6      6  0.000000    1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby(['Parch'], as_index=False).agg({'Survived':'mean', \"Sex\": 'count'}).sort_values(by = \"Survived\", ascending = False)\n",
    "\n",
    "# train_df[\"Parch\"] == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A histogram chart is useful for analyzing continous numerical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlating numerical features\n",
    "Let us start by understanding correlations between numerical features and our solution goal (Survived).\n",
    "\n",
    "A histogram chart is useful for analyzing continous numerical variables like Age where banding or ranges will help identify useful patterns. The histogram can indicate distribution of samples using automatically defined bins or equally ranged bands. This helps us answer questions relating to specific bands (Did infants have better survival rate?)\n",
    "\n",
    "### Observations.\n",
    "\n",
    "Infants (Age <=4) had high survival rate.\n",
    "\n",
    "Oldest passengers (Age = 80) survived.\n",
    "\n",
    "Large number of 15-25 year olds did not survive.\n",
    "\n",
    "Most passengers are in 15-35 age range.\n",
    "\n",
    "### Decisions.\n",
    "\n",
    "This simple analysis confirms our assumptions as decisions for subsequent workflow stages.\n",
    "\n",
    "We should consider Age in our model training.\n",
    "\n",
    "Complete the Age feature for null values.\n",
    "\n",
    "We should band age groups ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([27.,  9.,  4., 15., 27., 36., 30., 35., 35., 17., 12., 12., 14.,\n",
       "         7.,  5.,  4.,  0.,  0.,  0.,  1.]),\n",
       " array([ 0.42 ,  4.399,  8.378, 12.357, 16.336, 20.315, 24.294, 28.273,\n",
       "        32.252, 36.231, 40.21 , 44.189, 48.168, 52.147, 56.126, 60.105,\n",
       "        64.084, 68.063, 72.042, 76.021, 80.   ]),\n",
       " <a list of 20 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "axes1 = plt.subplot(1, 2, 1)\n",
    "axes1.hist(train_df.Age[train_df.Survived == 0].dropna(), bins=20)\n",
    "axes1.set_title(\"survived = 0\")\n",
    "\n",
    "axes2 = plt.subplot(1, 2, 2)\n",
    "axes2.set_ylim(0, 50)\n",
    "axes2.set_title(\"survived = 1\")\n",
    "axes2.hist(train_df.Age[train_df.Survived == 1].dropna(), bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlating numerical and ordinal features\n",
    "\n",
    "We can combine multiple features for identifying correlations using a single plot. This can be done with numerical and categorical features which have numeric values.\n",
    "\n",
    "### Observations.\n",
    "\n",
    "Pclass=3 had most passengers, however most did not survive.\n",
    "\n",
    "Confirms our classifying assumption .\n",
    "\n",
    "Infant passengers in Pclass=2 and Pclass=3 mostly survived. Further qualifies our classifying assumption .\n",
    "\n",
    "Most passengers in Pclass=1 survived. Confirms our classifying assumption.\n",
    "\n",
    "Pclass varies in terms of Age distribution of passengers.\n",
    "\n",
    "### Decisions.\n",
    "\n",
    "Consider Pclass for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 1), (1, 1), (0, 2), (1, 2), (0, 3), (1, 3)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "survived = [0, 1]\n",
    "pclass = [1, 2, 3]\n",
    "tuples = [(y, x) for x in pclass for y in survived]\n",
    "print(tuples)\n",
    "\n",
    "for i, value in enumerate(tuples):\n",
    "    survived, pclass = value[0], value[1]\n",
    "    axes = plt.subplot(3, 2, i+1)\n",
    "    axes.hist(train_df.Age[np.logical_and(train_df.Survived == survived, train_df.Pclass == pclass)].dropna(), bins=20)\n",
    "    axes.set_title(f\"survived = {survived} |pclass = {pclass}\")\n",
    "    axes.set_ylim(0, 40)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlating categorical features\n",
    "Now we can correlate categorical features with our solution goal.\n",
    "\n",
    "### Observations.\n",
    "\n",
    "Female passengers had much better survival rate than males. Confirms classifying.\n",
    "    \n",
    "Exception in Embarked=C where males had higher survival rate. This could be a correlation between Pclass \n",
    "and Embarked and in turn Pclass and Survived, not necessarily direct correlation between Embarked and Survived.\n",
    "    \n",
    "Males had better survival rate in Pclass=3 when compared with Pclass=2 for C and Q ports. Completing.\n",
    "    \n",
    "Ports of embarkation have varying survival rates for Pclass=3 and among male passengers. Correlating.\n",
    "    \n",
    "### Decisions.\n",
    "\n",
    "Add Sex feature to model training.\n",
    "    \n",
    "Complete and add Embarked feature to model training."
   ]
  },
  {
   "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>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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 12))\n",
    "embarked = ['S', 'C', 'Q']\n",
    "\n",
    "for i, value in enumerate(embarked):\n",
    "    on = value[0]\n",
    "    axes = plt.subplot(3, 1, i+1)\n",
    "    train_df[np.logical_and(train_df[\"Embarked\"] == on, train_df[\"Sex\"] == \"male\")].groupby([\"Pclass\"]).mean()[\"Survived\"].plot()\n",
    "    train_df[np.logical_and(train_df[\"Embarked\"] == on, train_df[\"Sex\"] == \"female\")].groupby([\"Pclass\"]).mean()[\"Survived\"].plot()\n",
    "    axes.set_title(f\"Embarked = {on}\")\n",
    "    axes.set_xticks([1, 2, 3])\n",
    "    axes.legend([\"male\", \"female\"], fontsize=12, loc=\"upper right\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlating categorical and numerical features\n",
    "We may also want to correlate categorical features (with non-numeric values) and numeric features. We can consider correlating Embarked (Categorical non-numeric), Sex (Categorical non-numeric), Fare (Numeric continuous), with Survived (Categorical numeric).\n",
    "\n",
    "### Observations.\n",
    "\n",
    "Higher fare paying passengers had better survival. Confirms our assumption for creating fare ranges.\n",
    "\n",
    "Port of embarkation correlates with survival rates. Confirms correlating and completing.\n",
    "    \n",
    "### Decisions.\n",
    "\n",
    "Consider banding Fare feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'S'), (1, 'S'), (0, 'C'), (1, 'C'), (0, 'Q'), (1, 'Q')]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "survived = [0, 1]\n",
    "embarked = ['S', 'C', 'Q']\n",
    "tuples = [(y, x) for x in embarked for y in survived]\n",
    "print(tuples)\n",
    "\n",
    "for i, value in enumerate(tuples):\n",
    "    survived, embarked = value[0], value[1]\n",
    "    axes = plt.subplot(3, 2, i+1)\n",
    "    train_df[np.logical_and(train_df.Survived == survived, train_df.Embarked == embarked)].groupby([\"Sex\"])['Fare'].mean().plot(kind=\"bar\")\n",
    "    axes.set_title(f\"survived = {survived} |Embarked = {embarked}\")\n",
    "    axes.set_ylim(0, 80)\n",
    "    plt.xticks(rotation='horizontal')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we want to drop the Cabin and Ticket features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n",
      "After (891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "print(\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new feature extracting from existing\n",
    "We want to analyze if Name feature can be engineered to extract titles and test correlation between titles and survival, before dropping Name and PassengerId features.\n",
    "\n",
    "In the following code we extract Title feature using regular expressions. The RegEx pattern (\\w+\\.) matches the first word which ends with a dot character within Name feature. The expand=False flag returns a DataFrame.\n",
    "\n",
    "### Observations.\n",
    "\n",
    "When we plot Title, Age, and Survived, we note the following observations.\n",
    "\n",
    "Most titles band Age groups accurately. For example: Master title has Age mean of 5 years.\n",
    "Survival among Title Age bands varies slightly.\n",
    "Certain titles mostly survived (Mme, Lady, Sir) or did not (Don, Rev, Jonkheer).\n",
    "### Decision.\n",
    "\n",
    "We decide to retain the new Title feature for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new feature extracting from existing\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])  # from A to Z more than one + ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can replace many titles with a more common name or classify them as Rare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert the categorical titles to ordinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Fare</th>\n",
       "      <th>Embarked</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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also do not need the PassengerId feature in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting a categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0}).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2, 3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we iterate over Sex (0 or 1) and Pclass (1, 2, 3) to calculate guessed values of Age for the six combinations."
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "            age_guess = guess_df.median()\n",
    "            # convert random age float to nearest .5 age\n",
    "            guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[(dataset.Age.isnull() & (dataset.Sex == i)) & (dataset.Pclass == j+1), 'Age'] = guess_ages[i, j]\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create Age bands and determine correlations with Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)  # cut into same width\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us replace Age with ordinals based on these bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>AgeBand</th>\n",
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       "      <th>0</th>\n",
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       "      <td>(16.0, 32.0]</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[dataset['Age'] > 64, 'Age'] = 4\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove the AgeBand feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Title</th>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
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       "      <th>6</th>\n",
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       "      <td>0.333333</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create another feature called IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
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      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create an artificial feature combining Pclass and Age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completing a categorical feature\n",
    "Embarked feature takes S, Q, C values based on port of embarkation. Our training dataset has two missing values. We simply fill these with the most common occurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "\n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting categorical feature to numeric\n",
    "We can now convert the EmbarkedFill feature by creating a new numeric Port feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S': 0, 'C': 1, 'Q': 2}).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick completing and converting a numeric feature\n",
    "We can now complete the Fare feature for single missing value in test dataset using mode to get the value that occurs most frequently for this feature. We do this in a single line of code.\n",
    "\n",
    "Note that we are not creating an intermediate new feature or doing any further analysis for correlation to guess missing feature as we are replacing only a single value. The completion goal achieves desired requirement for model algorithm to operate on non-null values.\n",
    "\n",
    "We may also want round off the fare to two decimals as it represents currency.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
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       "      <td>892</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can not create FareBand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the Fare feature to ordinal values based on the FareBand.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Survived</th>\n",
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       "      <th>Sex</th>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <th>9</th>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <th>9</th>\n",
       "      <td>901</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3\n",
       "5          897       3    0    0     1         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     2         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     2         0      1        0          3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ....to be continue"
   ]
  }
 ],
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