pre processing the data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv as csv\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Random Forest Regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "%matplotlib inline\n",
    "\n",
    "addpoly = True\n",
    "plot_lc = 0   # 1--display learning curve/ 0 -- don't display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train dataset: (891, 12), test dataset (418, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the data sets from the csv files\n",
    "\n",
    "df_train = pd.read_csv('./dataset/train.csv')\n",
    "df_test = pd.read_csv('./dataset/test.csv')\n",
    "\n",
    "print('train dataset: %s, test dataset %s' %(str(df_train.shape), str(df_test.shape)) )\n",
    "df_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "5          897       3                    Svensson, Mr. Johan Cervin    male   \n",
       "6          898       3                          Connolly, Miss. Kate  female   \n",
       "7          899       2                  Caldwell, Mr. Albert Francis    male   \n",
       "8          900       3     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female   \n",
       "9          901       3                       Davies, Mr. John Samuel    male   \n",
       "\n",
       "    Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0     330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0     363272   7.0000   NaN        S  \n",
       "2  62.0      0      0     240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0     315154   8.6625   NaN        S  \n",
       "4  22.0      1      1    3101298  12.2875   NaN        S  \n",
       "5  14.0      0      0       7538   9.2250   NaN        S  \n",
       "6  30.0      0      0     330972   7.6292   NaN        Q  \n",
       "7  26.0      1      1     248738  29.0000   NaN        S  \n",
       "8  18.0      0      0       2657   7.2292   NaN        C  \n",
       "9  21.0      2      0  A/4 48871  24.1500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dan/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:6201: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  sort=sort)\n"
     ]
    }
   ],
   "source": [
    "df = df_train.append(df_test , ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12) (418, 11) ['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "# some quick inspections\n",
    "print(df_train.shape, df_test.shape, df_train.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data Preprocessing\n",
    "#Let's look at individual features one by one. Missing values will have to be filled.\n",
    "#Categorical features need to be transformed to numerical values.\n",
    "\n",
    "\n",
    "#Pclass\n",
    "# check if there is any NAN\n",
    "df['Pclass'].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the correlation between Pclass and Survived\n",
    "df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()\n",
    "#We can see that a higher class (lower value) has a higher survival rate. This should be a very useful feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                              Braund, Mr. Owen Harris\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                               Heikkinen, Miss. Laina\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                             Allen, Mr. William Henry\n",
       "5                                     Moran, Mr. James\n",
       "6                              McCarthy, Mr. Timothy J\n",
       "7                       Palsson, Master. Gosta Leonard\n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Name\n",
    "\n",
    "df.Name.head(10)\n",
    "#Each name has a title, which is clearly what matters since it contains information of gender or status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr              757\n",
       "Miss            260\n",
       "Mrs             197\n",
       "Master           61\n",
       "Dr                8\n",
       "Rev               8\n",
       "Col               4\n",
       "Mlle              2\n",
       "Major             2\n",
       "Ms                2\n",
       "Jonkheer          1\n",
       "Mme               1\n",
       "Dona              1\n",
       "Capt              1\n",
       "the Countess      1\n",
       "Don               1\n",
       "Lady              1\n",
       "Sir               1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracting the titles from the names.\n",
    "df['Title'] = df.Name.map( lambda x: x.split(',')[1].split( '.' )[0].strip())\n",
    "\n",
    "df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dan/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "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>0.701087</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>0.796875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Others</td>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.701087\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.796875\n",
       "4  Others  0.318182"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'] = df['Title'].replace('Mlle', 'Miss')\n",
    "df['Title'] = df['Title'].replace(['Mme','Lady','Ms'], 'Mrs')\n",
    "df.Title.loc[ (df.Title !=  'Master') & (df.Title !=  'Mr') & (df.Title !=  'Miss') \n",
    "             & (df.Title !=  'Mrs')] = 'Others'\n",
    "\n",
    "# inspect the correlation between Title and Survived\n",
    "df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        757\n",
       "Miss      262\n",
       "Mrs       201\n",
       "Master     61\n",
       "Others     28\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the amount of people for each title\n",
    "df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "      <th>Others</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  Parch  PassengerId  Pclass     Sex  SibSp  \\\n",
       "0  22.0   NaN        S   7.2500      0            1       3    male      1   \n",
       "1  38.0   C85        C  71.2833      0            2       1  female      1   \n",
       "2  26.0   NaN        S   7.9250      0            3       3  female      0   \n",
       "3  35.0  C123        S  53.1000      0            4       1  female      1   \n",
       "4  35.0   NaN        S   8.0500      0            5       3    male      0   \n",
       "\n",
       "   Survived            Ticket Title  Master  Miss  Mr  Mrs  Others  \n",
       "0       0.0         A/5 21171    Mr       0     0   1    0       0  \n",
       "1       1.0          PC 17599   Mrs       0     0   0    1       0  \n",
       "2       1.0  STON/O2. 3101282  Miss       0     1   0    0       0  \n",
       "3       1.0            113803   Mrs       0     0   0    1       0  \n",
       "4       0.0            373450    Mr       0     0   1    0       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eliminate name field\n",
    "df = pd.concat([df, pd.get_dummies(df['Title'])], axis=1).drop(labels=['Name'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sex\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Sex.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the correlation between Sex and Survived\n",
    "df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "      <th>Others</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  Parch  PassengerId  Pclass  Sex  SibSp  \\\n",
       "0  22.0   NaN        S   7.2500      0            1       3    0      1   \n",
       "1  38.0   C85        C  71.2833      0            2       1    1      1   \n",
       "2  26.0   NaN        S   7.9250      0            3       3    1      0   \n",
       "3  35.0  C123        S  53.1000      0            4       1    1      1   \n",
       "4  35.0   NaN        S   8.0500      0            5       3    0      0   \n",
       "\n",
       "   Survived            Ticket Title  Master  Miss  Mr  Mrs  Others  \n",
       "0       0.0         A/5 21171    Mr       0     0   1    0       0  \n",
       "1       1.0          PC 17599   Mrs       0     0   0    1       0  \n",
       "2       1.0  STON/O2. 3101282  Miss       0     1   0    0       0  \n",
       "3       1.0            113803   Mrs       0     0   0    1       0  \n",
       "4       0.0            373450    Mr       0     0   1    0       0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map the two genders to 0 and 1\n",
    "df.Sex = df.Sex.map({'male':0, 'female':1})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Age\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Age.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SibSp and Parch\n",
    "\n",
    "# check if there is any NAN\n",
    "df.SibSp.isnull().sum(axis=0), df.Parch.isnull().sum(axis=0)"
   ]
  },
  {
   "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",
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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>Family</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Family  Survived\n",
       "0       1  0.303538\n",
       "1       2  0.552795\n",
       "2       3  0.578431\n",
       "3       4  0.724138\n",
       "4       5  0.200000\n",
       "5       6  0.136364\n",
       "6       7  0.333333\n",
       "7       8  0.000000\n",
       "8      11  0.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new feature \"Family\"\n",
    "df['Family'] = df['SibSp'] + df['Parch'] + 1\n",
    "\n",
    "# inspect the correlation between Family and Survived\n",
    "df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Family</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.161290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Family  Survived\n",
       "0       0  0.161290\n",
       "1       1  0.303538\n",
       "2       2  0.552795\n",
       "3       3  0.578431\n",
       "4       4  0.724138"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Family = df.Family.map(lambda x: 0 if x > 4 else x)\n",
    "df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    790\n",
       "2    235\n",
       "3    159\n",
       "0     82\n",
       "4     43\n",
       "Name: Family, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Family'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ticket\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Ticket.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            A/5 21171\n",
       "1             PC 17599\n",
       "2     STON/O2. 3101282\n",
       "3               113803\n",
       "4               373450\n",
       "5               330877\n",
       "6                17463\n",
       "7               349909\n",
       "8               347742\n",
       "9               237736\n",
       "10             PP 9549\n",
       "11              113783\n",
       "12           A/5. 2151\n",
       "13              347082\n",
       "14              350406\n",
       "15              248706\n",
       "16              382652\n",
       "17              244373\n",
       "18              345763\n",
       "19                2649\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like there are two types of tickets: (1) number (2) letters + number\n",
    "\n",
    "Ticket names with letters probably represent some special classes. For the numbers, the majority of tickets have their first digit = 1, 2, or 3, which probably also represent different classes. So I just keep the first element (a letter or a single-digit number) of these ticket names\n"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.630137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.239203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A</td>\n",
       "      <td>0.068966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>0.340426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P</td>\n",
       "      <td>0.646154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>S</td>\n",
       "      <td>0.323077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>W</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticket  Survived\n",
       "0       1  0.630137\n",
       "1       2  0.464481\n",
       "2       3  0.239203\n",
       "3       4  0.200000\n",
       "4       5  0.000000\n",
       "5       6  0.166667\n",
       "6       7  0.111111\n",
       "7       8  0.000000\n",
       "8       9  1.000000\n",
       "9       A  0.068966\n",
       "10      C  0.340426\n",
       "11      F  0.571429\n",
       "12      L  0.250000\n",
       "13      P  0.646154\n",
       "14      S  0.323077\n",
       "15      W  0.153846"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket = df.Ticket.map(lambda x: x[0])\n",
    "\n",
    "# inspect the correlation between Ticket and Survived\n",
    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    429\n",
       "2    278\n",
       "1    210\n",
       "S     98\n",
       "P     98\n",
       "C     77\n",
       "A     42\n",
       "W     19\n",
       "F     13\n",
       "7     13\n",
       "4     11\n",
       "6      9\n",
       "L      5\n",
       "5      3\n",
       "8      2\n",
       "9      2\n",
       "Name: Ticket, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the amount of people for each type of tickets\n",
    "df['Ticket'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the majority of tickets are indeed \"3\", \"2\", and \"1\", and their corresponding survival rates are \"1\" > \"2\" > \"3\". For the others, the survival rate is low except for \"9\",\"C\",\"F\",\"P\", and \"S\". However, \"9\" and \"F\" are very small sample. The correlation we see here probably comes from Pclass or Fare, so let's check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65.771211</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20.235194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15.380682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>16.764036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>19.262500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>19.153711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>9.197438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8.397900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A</td>\n",
       "      <td>10.189681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>28.152273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>24.677246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L</td>\n",
       "      <td>1.515000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P</td>\n",
       "      <td>119.698253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>S</td>\n",
       "      <td>17.542900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>W</td>\n",
       "      <td>31.056579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticket        Fare\n",
       "0       1   65.771211\n",
       "1       2   20.235194\n",
       "2       3   15.380682\n",
       "3       4   16.764036\n",
       "4       5   19.262500\n",
       "5       6   19.153711\n",
       "6       7    9.197438\n",
       "7       8    8.397900\n",
       "8       9    7.750000\n",
       "9       A   10.189681\n",
       "10      C   28.152273\n",
       "11      F   24.677246\n",
       "12      L    1.515000\n",
       "13      P  119.698253\n",
       "14      S   17.542900\n",
       "15      W   31.056579"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Ticket', 'Fare']].groupby(['Ticket'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.309353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.904429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>2.558442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>1.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P</td>\n",
       "      <td>1.102041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>S</td>\n",
       "      <td>2.561224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>W</td>\n",
       "      <td>2.315789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticket    Pclass\n",
       "0       1  1.147619\n",
       "1       2  2.309353\n",
       "2       3  2.904429\n",
       "3       4  3.000000\n",
       "4       5  2.333333\n",
       "5       6  2.111111\n",
       "6       7  3.000000\n",
       "7       8  3.000000\n",
       "8       9  3.000000\n",
       "9       A  3.000000\n",
       "10      C  2.558442\n",
       "11      F  1.846154\n",
       "12      L  3.000000\n",
       "13      P  1.102041\n",
       "14      S  2.561224\n",
       "15      W  2.315789"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Ticket', 'Pclass']].groupby(['Ticket'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, there is some relation between Ticket and Fare, or between Ticket and Pclass. Also, one interesting thing is that \"P\" corresponds to very high Fare and Pcalss (better than \"1\"). This is an additional information that cannot be seen in other features. So keeping Ticket as a feature might be useful.\n",
    "\n",
    "I'll come back to Ticket later. I don't further transform it now because its current form might be useful for guessing some missing values in other features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fare\n",
    "\n",
    "\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Fare.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only one missing value. Fare can probably be inferred from Ticket, Pclass, Cabin and Embarked. Let's see the corresponding values of for these features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1043    3\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket[df.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1043    3\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Pclass[df.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1043    NaN\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin[df.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1043    S\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Embarked[df.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no corresponding value for Cabin, so let's look at the relation between Fare and the three features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use boxplot to visualize the distribution of Fare for each Pclass\n",
    "sns.boxplot('Pclass','Fare',data=df)\n",
    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87.508992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.179196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.302889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fare\n",
       "Pclass           \n",
       "1       87.508992\n",
       "2       21.179196\n",
       "3       13.302889"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the correlation between Pclass and Fare\n",
    "df[['Pclass', 'Fare']].groupby(['Pclass']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.919302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.642476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.864050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fare\n",
       "Pclass          \n",
       "1       0.919302\n",
       "2       0.642476\n",
       "3       0.864050"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
    "# distribution of Fare in each Pclass\n",
    "df[['Pclass', 'Fare']].groupby(['Pclass']).std() / df[['Pclass', 'Fare']].groupby(['Pclass']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IVhaR0tET5SI0NTXx1u693PDoCz2u46XdexliTUWM6lALFy7ssktmy5YtAMyZM6fLesaPH8+ll16a8/0GDhzI/v37M8qFGjduXEZSGDduXMF1ikhxKEH0YQ0NDTy/eSMjjsxyQHvwbccrG7PWseP1/N9v8ODBGQliyJAh+Z+cxdy5c5k9++1J9F//+te7OFpEepMSRISamhr2+16u+ujEHtdxw6MvMLCmplvnLF68mOXLlzNz5kwuuOCC3CcAI46ET3+i5z2F//FIe97H7tq1K6O8c2fn50F134QJEzpaEePGjWP8+PEF1ykixaF7EAmyfPlyAJYuXVriSHrX3LlzGTx4cEW2HjQHRJJMCSIhFi9enFFesmRJliNL59hjj80ojxpVnKfCTpgwgQceeKAiWw+aAyJJpgSREKnWQ0oSWxHXXHNNRvkb3/hGiSIpD5oDIkmnBCF5mzBhQkcrYtSoURV5xV9MmgMiSaeb1NIt11xzDXPmzElc66HzkN/GxuCJtaNHj+7Ylu9w3t6iOSCSdGpBJMTnPve5jPLMmTNLFEnX4rhfsHLlSmpra1m9enXR6ty3bx/79u0rWn1x6DznQ3NAJGnUgkiIiy66KOM+RL7DXMvBggULALj99tuZNm1aj+ro3DJITQ685ZZbCgsuRpoDIkmnFkSCpFoRSW09xGHlypUET5wFdy9qKyLpUnNAAM0BkURSgkiQiy66iDVr1lRk6yHl9ttvL1EkpVHJc0Ak+dTFJCWVaj1kK5e71D0dkSRSgpCSMrOMpGBmJYwmfn1xtJVULnUxSUml36QFuPzyy0sUSWn0hdFWUrnUgpCSmj59OgsWLMDdMbMej2LqK/riaCupXGpBSMmlWhGV1noQSTolCCm56dOns2bNmrJvPfRlWnW2MilBiEhOWnW2MpXkHoSZvQi8CbQBre4+2cyOBpYDxwMvAp91913Z6pDg0ahv7O7eQ3862/E6HPB4H40qfVvUqrOa1FcZStmCmOLuJ7n75LD8deDX7j4R+HVYFpES06qzlStJo5jOAT4evl4C/Aa4slTB9AU1NTUMsB0FP3J0xKjuPRpVKotWna1cpUoQDqw1Mwf+1d0XAce4+yvh/leBY0oUm4ikST0zPL1czjSZ8W2l6mL6qLt/EDgLmG1mp6Xv9GBqbeSaC2Y2y8zWm9n67du390KoIpVt7ty5GeVKWzeqkiczlqQF4e6N4fdtZvYAcArwmpmNcvdXzGwUsC3LuYuARQCTJ0+urIV7REogters1q1bK2LVWU1mfFuvtyDMbIiZHZ56DZwJbAJWAKllTC8AftHbsZVaXV0dtbW1rFu3rtShiGTQqrOVqRRdTMcAj5rZn4AngAfd/SHgRuAMM3sB+GRYrii33norADfddFOJI8lu8eLF1NbWsmTJklKHIr0ojicJSvL1eoJw9wZ3/0D49T53vz7c3uzup7v7RHf/pLvv7O3YSqmuro7W1lYAWltbE9uKSD31bunSpSWORETippnUCZFqPaQksRWxePHijLJaESLlTQkiIVKth2zlJEh/ZjaoFSFS7pI0Ua6iVVVV0d7enlEuVxpnLtI3KEEkRP/+/dm/f39GuVL0ZIx55yTT2ZYtW4C3hyhmo0Qkkp0SREKkJ4eochJ86lOfYsWKFR3lc889t0f1FGOceUNDAxuf3wQjDstyxAEANu6oz17Jjsqc/CSSLyWIhBg6dCh79uzJKCdNS0tLRvnNN98sUSShEYfR75yeD7ts+0X8zzYoRktHrRwpFSWILF7avZcbHn0h6/5tbwVX+O8YMjDr+RNHR+6KNG/ePObNm9dRvvrqq/M/uZf86le/OqScqwun0gUtnc3Y8KMj9werysAz21+L3t9cUaO9JWGUICLkMxnoQHjlN3D0uyL3TxydXz0pkyZNYtCgQezdu5fBgwdz8skn532uJJsNP5rqs2t7dG7rqjVFjkYkf0oQEfJpzsexPsvhhx/O3r17E9m9JCKVpyISxIYNG7j66quZP39+Yq/M6+vr2bYtWJ9w27ZteT+1a8fr2Z8otzu8pTGsi3yz43UYMarb4YpIBaiIBDF//nza29u57rrruP/++0sdTqSop3YtWrSoy3NyJZDdYTfYiFHR3WDBvu51hYlI5Sj7BLFhw4aO0UF79uzhqaeeSkQrovPolqinds2ZM6fLESy5usKSskyx5iwUV+d/z6amJvbu3dvlOYMGDaKm5u0nB3b+t4yjzr4g1+8mFD7SLOo9+srk0LJPEPPnz88oJ7UVMXDgwIy5DwMHRo+O6m25/oBSfzRd/XI3NDSw6fmNDBoeXcf+8KkeW7ZvzPo+e5vzi7cSBCOjnseGjwTAW/ZC68Euz3nLoXl78I/ozYc+aCuo8y/0Gx70N7a3HMBb27qss8UPsGt7MNS5rfmVLo9NqoaGBv6yuZ7Rw8ZmPaa6bQAAbzUdiNzfuPulbr9vX3kAUdkniPS5BVHlUun8YVpfX8/s2bM7yt/73vcS1/Xzjne8o+M+CcAxx+T/VNhBw2Hi2dbj935hVd98NlRTUxP+xu4ej0by5p00HTz0g9qGj2Tg2ef1qM79q+6L3N5v+CgGT5/VozpbVnbdHZpko4eNZfbH5uU+MIsFv53f5f6oC6ektO5zKfsE0RcmoEGw3n6qFZGkp3Z1/uWurX17uObdd9/d2+GISC8q+wTRFyagpYwZM4aGhoZEP7Ur1Yq45JJLShpHU1MTvLGvsNnQO/bRdKCpeEFFqKmpobl/v4LmQdSMzL+l1lNNTU20NTfz5o+/Fb7xQfDo0XEdrAqqwzXDDu6n6WCWPsQEa2pqYs/rb+VsBXSl8fWtDGVIR7k37mv0lrJPEJMmTcooJ+EGdTaDBw/mxBNPTEzrIcqxxx7Lsccey3nn9ax7Q5LpiCOOyLgpvb+9lfb2rrsEq6qMgf37BYX+gzniiCPiDLHPaGhooP65Fxh7ePalFAa0Bh+9B/7WErn/pTcbY4mtu8o+QdTXZy7Wlu/8Akm2mpoadgxoKXgtppoRNbkPrAALFy4sdQglUVNTw1scKPgexJCaARnbxh4+mqv++5d7XOcNf7yjx+cWU9kniM5dSvPmzWPZsmUlikZEyl1TUxM7d+3knx+5EoCDbQdpz9VdB1RZFf37BV12+1sPcHS/6PW7elPZJ4hdu3Z1We4tfaVfUnMWsmtubuaGG25g3rx5HH106f94e1N9fT1z5szhtttuUws8h87ddexvhRzddQBUGVUDgy67QQMHJaLLruwTRFIE4603cuyw7L8oVW3BUM43mp6J3P/q7viHejY0NLB580aGHRW9vy28EGp6Nfuchd2dcnBTUxN73yhsqOreZmg6GO8N5VyWLl3Kpk2buOeee/jyl3vefdAX3XzzzbS0tOQ1w7/SlVN3nRJELzp2mHHJaT1/Utyd67qeDFUsw46C087s+fnr1hYvlqRobm5mzZo1uDtr1qzh85//fMW0Iurr6ztm+m/durVo9/EquUXWV5R9grjsssv4wQ9+0FH+yle+UsJoKlNNTQ0739iRdf/+3cH3gcNy15NhRxfDXHeHs16HDYjeH57PiK7fM2Xp0qUcPBgk6IMHD3arFeHNO7NOlPPdwUxkG3Z41nPphWGuXenJOmH5qOQWWV9R9gli+vTpGQli2rRp3a4jql8+qi++HPvdiyHX1eaWN4J/y3eNzL6oICMz68lZ5+6wzhFd1Dkis56FCxfy8MMPd5T3799Pe3vQp5b6nrJq1SpWr15NVVXVIcuinHHGGR2/B7l/9mAS57uyJYGRxxxSR1NTE97czL4lP4w+p7U1+F6d5c/74EGaDub/SNuodcIK1dzczNq1a3F31q5dW1Etsr6kzyeIzn/ULS0tHU/pipKaCWxmDB48uGN7+h91Pg47LNuzkKM1NTXR3Ox8e0X0ei4AqaVvqvtF7z/QBsOJtx8+iBNWLI/e3xbG2C9LjABtrUD723HGsahgbyxU2NbW1uXvUnt7e5f74dA48xmskOtC45A5C2mJLBUXQFUqUUBmIuvfv1s3QONYjWDp0qUZyTefVkTnv3XI/fcOuf/WG3e/1OVEuR17gqf9jRgancQbd7/Eu2smdBlDobr7OZdS6Odcn08QvaEYrYJcf9RAx1A4b6/q2Jb+h10d1hOnXHF66o+aqozzMj6ABsQfZxwuvfTSrCucbtx46E3597///QW3Grt7oZGKq3M5PekUe6XQ1rREE1XuiUceeaSjntbWVh555JGSdDPlcy/l1S3BRV3nuQ4p766ZULYjuyyfLJRUkydP9vXr13d5TPraQSlr1pT+MY69sQRwMa6i8/nwgdLHGXeds2bNyuhaGTduXMWM5rnjjjtYtWpVR/nss88u+MP8jjvu4KGHHqK1tZXq6mqmTp2amPsQnX/nU93J73rX292Vfb072cw2uPvkXMepBVEifeWXq6/EGbe5c+dmrLab5PWyim3mzJkZH+af//zni1Ln2rXBcLeqqqqi1BmXnrTyykXiEoSZTQW+D/QD7nT3Gwup73Of+xzLl7/doT5z5szCApREynbVV6xBBBMmTGDcuHFs3bo1Uavt9obhw4czdepUHnzwQaZOnVqUm8nDhw/nzDPP5MEHH+TMM89M1A1qXRS9rSr3Ib3HzPoBC4CzgBOA883shELqvOiiizLKF1xwQSHVSR9x2GGHFf3Kb+7cuQwePLiiWg8pM2fO5MQTTyzqlX4cdUpxJeoehJl9GLjW3WvD8lUA7n5D1PH53IMAWLx4McuXL2fmzJllnSDy6TuF0vefVkIfb6WLa8SRFEe+9yCSliA+A0x190vC8heAD7n7ZWnHzAJSj716D/DnPKsfAWSfrdUzfaHOvhCj6lSdqrN36xzn7iNzHZS4exC5uPsioNvDR8xsfT4Zs9zq7Asxqk7VqTqTWWei7kEAjcCYtPJx4TYREellSUsQfwQmmtk7zWwAMANYUeKYREQqUqK6mNy91cwuA9YQDHNd7O7PFqn6OGY19YU6+0KMqlN1qs4E1pmom9QiIpIcSetiEhGRhFCCEBGRSGWfIMzs02bmZvbeItbZZmZPm9kmM7vPzAbnPitnnf/XzJ41s41h3R8qsL7FZrbNzDYVGltanYeZ2RNm9qcw1m8Voc4xZlZnZs+FdRb8RCczO9bMlpnZFjPbYGarzezdBdb5nvD/JfX1hpl9tQf1fDf9PDNbY2Z3ppVvM7OvFRhrPzN7ysxW5T46r/raOv3sxxehzj25j+p2nV8J/yaf7cn/TZY6jzSzn5nZ82a2OZzMW2idL5rZM+G/Ze6ZvvnV+X/Cn3uTmf3UzIqzjIC7l/UXsBz4LfCtIta5J+31PcDXCqzvw8BjwMCwPAKoKbDO04APApuK+HMbMDR83R94HDi1wDpHAR8MXx8O/AU4ocAYHwP+OW3bB4CPFfHfoR/wKsFko+6e+xng3vB1FbABeCxt/2NF+Df9GrAUWFWkn3dPMeqJs07gRGATMJhg8M2vgAlFqHcJcEn4egBwZBHqfBEYUcSffTTwV2BQWL4X+FIx6i7rFoSZDQU+ClxMMGQ2Dr8FCn1ayChgh7vvB3D3He5e0JOB3H0dsLPAuDrX6e6euvLrH34VNMrB3V9x9yfD128Cmwl+4XtqCnDQ3f9/2nv8yd1/W0icnZwObHH3njxa7fcEFwQA7yP4UHvTzI4ys4HA3wFP9jQwMzsO+J/AnbmOLTN/Bzzu7i3u3gr8J/C/CqnQzIYRXGjdBeDuB9z99YIjjUc1MMjMqgmSZFGeLFbWCQI4B3jI3f8CNJvZpGJWHv5nnAU8U2BVa4ExZvYXM/uhmf2PwqOLR9h98TSwDXjY3R8vYt3HAycTtEx66kSCq/I4zQB+2pMTw8TfamZjgb8naDE8TpA0JgPPuHv2xw7m9j1gLtCe68BuGJTWvfRAEestpk3Ax8xseNjlO43MSbc98U5gO/CjsMvuTjMbUmigBBdVa8Puz1k5j85VmXsjcCvwEvAKsNvd1xZaL5R/gjgfWBa+XhaWi2FQ+CG5nuA/5a5CKguvyicRrDG1HVhuZl8qNMg4uHubu59EMMv9FDM7sRj1hq29+4GvuvsbxagzDuEEzk8B9xVQze8JkkMqQTyWVv5dAbEr6D2NAAAD8klEQVSdDWxz92InyL3uflL4dW6R6y4Kd98M3ERwsfUQ8DTQVmC11QTdtAvd/WTgLaAYS/l+1N0/SHBxOdvMTiukMjM7iuBi+J1ADTDEzP6x8DDLOEGY2dHAJ4A7zexFYA7wWTOzIlSf/gfz5QKv+ICOD97fuPs3gcuAfyg8zPiETe06YGqhdZlZf4LkcI+7/7zA6p4lSLZxOQt40t1fK6CO3xEkg/9GcOX7B4IWxN8TJI+e+gjwqfD3fRnwCTP79wLq61Pc/S53n+TupwG7CO5nFeJl4OW0VvLPCBJGQcIrftx9G/AAcEqBVX4S+Ku7b3f3g8DPCX6XCla2CYLgZuBP3H2cux/v7mMIbuR8rMRxHSIcITMxbdNJQE/6t2NlZiPN7Mjw9SDgDOD5Aus0ghbYZnf/TuFR8ggwML3pbmbvN7Ni/b+fTw+7l9L8Hjgb2BleGOwEjiRIEj1OEO5+lbsf5+7HE3SDPeLuRbmS7AvM7B3h97EE9x+WFlKfu78K/M3M3hNuOh14rsAYh5jZ4anXwJkEFwmFeAk41cwGh39PpxPcyytYOSeI8wmyc7r7KV43UzENBZaEQz03Ejws6dpCKjSznxJ0XbzHzF42s4sLD5NRQF0Y4x8J7kEUOpTyI8AXCK52U/3c03pamQfDOM4FPhkOc30WuIFg1FFBwj/oMwiu0ArxDMFItT902rbb3Yu9/HNSDQ5/L1NfBQ3tDd1vZs8BK4HZRbqh/GXgnvB3/iRgfoH1HQM8amZ/Ap4AHnT3hwqpMGzh/IxgcMMzBJ/rRVlyQ0ttiIhIpHJuQYiISAGUIEREJJIShIiIRFKCEBGRSEoQIiISSQlCJItw2YbU0NtXzawxrdzlfAUz+42Z5f3weDP7qhVhVWCRYkrUI0dFksTdmwnGvmNm1xKsQHprTG/3VeDfgZaY6hfpNrUgRHog/XkGZnZluL7/n8zsxk7HVZnZj83surB8ppk9ZmZPWvAskaFmdjnBGjp1ZlbXuz+JSHZqQYgUwMzOIlgo7UPu3hKuAZZSTfC8kE3ufr2ZjQCuBj7p7m+Z2ZUEzxL5f+FM4ikVNJNa+gAlCJHCfBL4kbu3AITrKqX8K8HDga4Py6cSLKPyu3DNyAEEy6GIJJIShEh8fg9MMbPb3H0fwdPuHnb3JK4HJnII3YMQKczDwIWpEUidupjuAlYD94YPl/oD8BEzmxAeO8Teflb2mwSPXBVJDCUIkQKEK3GuANaHD5G6otP+7wBPAT8BmoEvAT8NVwd9DHhveOgi4CHdpJYk0WquIiISSS0IERGJpAQhIiKRlCBERCSSEoSIiERSghARkUhKECIiEkkJQkREIv0XFxrXQFiZHGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use boxplot to visualize the distribution of Fare for each Ticket\n",
    "sns.boxplot('Ticket','Fare',data=df)\n",
    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.771211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.235194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.380682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.764036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.262500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.153711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.197438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.397900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>10.189681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>28.152273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>24.677246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>1.515000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P</th>\n",
       "      <td>119.698253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>17.542900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>31.056579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fare\n",
       "Ticket            \n",
       "1        65.771211\n",
       "2        20.235194\n",
       "3        15.380682\n",
       "4        16.764036\n",
       "5        19.262500\n",
       "6        19.153711\n",
       "7         9.197438\n",
       "8         8.397900\n",
       "9         7.750000\n",
       "A        10.189681\n",
       "C        28.152273\n",
       "F        24.677246\n",
       "L         1.515000\n",
       "P       119.698253\n",
       "S        17.542900\n",
       "W        31.056579"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the correlation between Ticket and Fare \n",
    "# (we saw this earlier)\n",
    "df[['Ticket', 'Fare']].groupby(['Ticket']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use boxplot to visualize the distribution of Fare for each Embarked\n",
    "sns.boxplot('Embarked','Fare',data=df)\n",
    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>62.336267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>12.409012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>27.418824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fare\n",
       "Embarked           \n",
       "C         62.336267\n",
       "Q         12.409012\n",
       "S         27.418824"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the correlation between Embarked and Fare\n",
    "df[['Embarked', 'Fare']].groupby(['Embarked']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.350514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>1.097278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>1.352954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Fare\n",
       "Embarked          \n",
       "C         1.350514\n",
       "Q         1.097278\n",
       "S         1.352954"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
    "# distribution of Fare in each Embarked\n",
    "df[['Embarked', 'Fare']].groupby(['Embarked']).std() /  df[['Embarked', 'Fare']].groupby(['Embarked']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like Fare indeed has correlation with these three features. I'll guess the missing value using the median value of (Pcalss = 3) & (Ticket = 3) & (Embarked = S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.117887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>48.395408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived       Fare\n",
       "0       0.0  22.117887\n",
       "1       1.0  48.395408"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_Fare = df.Fare.loc[ (df.Ticket == '3') & (df.Pclass == 3) & (df.Embarked == 'S')].median()\n",
    "df.Fare.fillna(guess_Fare , inplace=True)\n",
    "\n",
    "# inspect the mean Fare values for people who died and survived\n",
    "df[['Fare', 'Survived']].groupby(['Survived'],as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.875x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the distribution of Fare for people who survived and died\n",
    "grid = sns.FacetGrid(df, hue='Survived', size=4, aspect=1.5)\n",
    "grid.map(plt.hist, 'Fare', alpha=.5, bins=range(0,210,10))\n",
    "grid.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the correlation between Fare and Survived using a scatter plot\n",
    "df[['Fare', 'Survived']].groupby(['Fare'],as_index=False).mean().plot.scatter('Fare','Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that people with lower Fare are less likely to survive. But this is certainly not a smooth curve if we don't bin the data. It would be better to feed machine learning algorithms with intervals of Fare, because using the original Fare values would likely cause over-fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Fare-bin</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.217877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.201087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.426901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.443243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.645349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fare-bin  Survived\n",
       "0         1  0.217877\n",
       "1         2  0.201087\n",
       "2         3  0.426901\n",
       "3         4  0.443243\n",
       "4         5  0.645349"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bin Fare into five intervals with equal amount of people\n",
    "df['Fare-bin'] = pd.qcut(df.Fare,5,labels=[1,2,3,4,5]).astype(int)\n",
    "\n",
    "# inspect the correlation between Fare-bin and Survived\n",
    "df[['Fare-bin', 'Survived']].groupby(['Fare-bin'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the correlation is clear after binning the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1014"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cabin\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Cabin.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is highly incomplete. We have two choices: (1) map the missing ones to a new cabin category \"unknown\" (2) just drop this feature. I have tried both and I decided to choose (2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(labels=['Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Embarked\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Embarked.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Embarked</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1307</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>914</td>\n",
       "      <td>429</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Embarked Ticket Title\n",
       "count      1307   1309  1309\n",
       "unique        3     16     5\n",
       "top           S      3    Mr\n",
       "freq        914    429   757"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O']) # S is the most common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the NAN\n",
    "df.Embarked.fillna('S' , inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>1.851852</td>\n",
       "      <td>62.336267</td>\n",
       "      <td>32.332170</td>\n",
       "      <td>0.418519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>2.894309</td>\n",
       "      <td>12.409012</td>\n",
       "      <td>28.630000</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "      <td>2.344978</td>\n",
       "      <td>27.512485</td>\n",
       "      <td>29.298151</td>\n",
       "      <td>0.319869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived    Pclass       Fare        Age       Sex\n",
       "0        C  0.553571  1.851852  62.336267  32.332170  0.418519\n",
       "1        Q  0.389610  2.894309  12.409012  28.630000  0.487805\n",
       "2        S  0.339009  2.344978  27.512485  29.298151  0.319869"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the correlation between Embarked and Survived as well as some other features\n",
    "df[['Embarked', 'Survived','Pclass','Fare', 'Age', 'Sex']].groupby(['Embarked'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The survival rate does change between different Embarked values. However, it is due to the changes of other features. For example, people from Embarked = C are more likely to survive because they are generally richer (Pclass, Fare). People from Embarked = S has the lowest survival rate because it has the lowest fraction of female passengers, even though they are a bit richer than people from Embarked = Q.\n",
    "\n",
    "I therefore decided to drop this feature as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(labels='Embarked', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can go back to Age and try to fill the missing values. Let's see how it relates to other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Age\n",
    "\n",
    "# check if there is any NAN\n",
    "df.Age.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the correlation between Title and Age\n",
    "grid = sns.FacetGrid(df, col='Title', size=3, aspect=0.8, sharey=False)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>5.482642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>21.795236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>32.252151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>36.930636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>45.074074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age\n",
       "Title            \n",
       "Master   5.482642\n",
       "Miss    21.795236\n",
       "Mr      32.252151\n",
       "Mrs     36.930636\n",
       "Others  45.074074"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the mean Age for each Title\n",
    "df[['Title', 'Age']].groupby(['Title']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>4.161554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>12.192794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>12.422089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>12.872625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>11.303253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age\n",
       "Title            \n",
       "Master   4.161554\n",
       "Miss    12.192794\n",
       "Mr      12.422089\n",
       "Mrs     12.872625\n",
       "Others  11.303253"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the standard deviation of Age for each Title\n",
    "df[['Title', 'Age']].groupby(['Title']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the correlation between Fare-bin and Age\n",
    "grid = sns.FacetGrid(df, col='Fare-bin', size=3, aspect=0.8, sharey=False)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare-bin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.848315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.898241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.144009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.728604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.877913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age\n",
       "Fare-bin           \n",
       "1         27.848315\n",
       "2         27.898241\n",
       "3         26.144009\n",
       "4         30.728604\n",
       "5         35.877913"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the mean Age for each Fare-bin\n",
    "df[['Fare-bin', 'Age']].groupby(['Fare-bin']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare-bin</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.812580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.995650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.523208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.466022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15.150724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age\n",
       "Fare-bin           \n",
       "1         10.812580\n",
       "2         10.995650\n",
       "3         13.523208\n",
       "4         17.466022\n",
       "5         15.150724"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the standard deviation of Age for each Fare-bin\n",
    "df[['Fare-bin', 'Age']].groupby(['Fare-bin']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x432 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the correlation between SibSp and Age\n",
    "grid = sns.FacetGrid(df, col='SibSp', col_wrap=4, size=3.0, aspect=0.8, sharey=False)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.921766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.058071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.569444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.772727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "SibSp           \n",
       "0      30.921766\n",
       "1      31.058071\n",
       "2      23.569444\n",
       "3      16.312500\n",
       "4       8.772727\n",
       "5      10.166667\n",
       "8      14.500000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the mean Age for each SibSp\n",
    "df[['SibSp', 'Age']].groupby(['SibSp']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.059751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.974482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.910640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.824938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.005545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.192944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "SibSp           \n",
       "0      13.059751\n",
       "1      15.974482\n",
       "2      14.910640\n",
       "3      11.824938\n",
       "4       8.005545\n",
       "5       5.192944\n",
       "8            NaN"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the standard deviation of Age for each SibSp\n",
    "df[['SibSp', 'Age']].groupby(['SibSp']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 691.2x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the correlation between Parch and Age\n",
    "grid = sns.FacetGrid(df, col='Parch', col_wrap=4, size=3.0, aspect=0.8, sharey=False)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31.969401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.965625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.975979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>39.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "Parch           \n",
       "0      31.969401\n",
       "1      24.965625\n",
       "2      18.975979\n",
       "3      38.875000\n",
       "4      47.600000\n",
       "5      39.333333\n",
       "6      41.500000\n",
       "9            NaN"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the mean Age for each Parch\n",
    "df[['Parch', 'Age']].groupby(['Parch']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.422617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.410885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.564066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.295815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.432602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.032796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.121320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age\n",
       "Parch           \n",
       "0      12.422617\n",
       "1      18.410885\n",
       "2      14.564066\n",
       "3      16.295815\n",
       "4      14.432602\n",
       "5       1.032796\n",
       "6       2.121320\n",
       "9            NaN"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the standard deviation of Age for each Parch\n",
    "df[['Parch', 'Age']].groupby(['Parch']).std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The change of Age as a function of Title, Fare-bin, or SibSp is quite significant, so I'll use them to guess the missing values. I use a random forest regressor to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dan/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notice that instead of using Title, we should use its corresponding dummy variables \n",
    "df_sub = df[['Age','Master','Miss','Mr','Mrs','Others','Fare-bin','SibSp']]\n",
    "\n",
    "X_train  = df_sub.dropna().drop('Age', axis=1)\n",
    "y_train  = df['Age'].dropna()\n",
    "X_test = df_sub.loc[np.isnan(df.Age)].drop('Age', axis=1)\n",
    "\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators = 300)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = np.round(regressor.predict(X_test),1)\n",
    "df.Age.loc[df.Age.isnull()] = y_pred\n",
    "\n",
    "df.Age.isnull().sum(axis=0) # no more NAN now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we still need to bin the data into different Age intervals, for the same reason as Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Age-bin</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.452381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.402597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.427083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age-bin  Survived\n",
       "0        1  0.675000\n",
       "1        2  0.452381\n",
       "2        3  0.402597\n",
       "3        4  0.315789\n",
       "4        5  0.427083\n",
       "5        6  0.375000\n",
       "6        7  0.125000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [ 0, 4, 12, 18, 30, 50, 65, 100] # This is somewhat arbitrary\n",
    "age_index = (1,2,3,4,5,6,7) #('baby','child','teenager','young','mid-age','over-50','senior')\n",
    "df['Age-bin'] = pd.cut(df.Age, bins, labels=age_index).astype(int)\n",
    "\n",
    "df[['Age-bin', 'Survived']].groupby(['Age-bin'],as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at Ticket again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.630137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.239203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A</td>\n",
       "      <td>0.068966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>0.340426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>L</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>P</td>\n",
       "      <td>0.646154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>S</td>\n",
       "      <td>0.323077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>W</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticket  Survived\n",
       "0       1  0.630137\n",
       "1       2  0.464481\n",
       "2       3  0.239203\n",
       "3       4  0.200000\n",
       "4       5  0.000000\n",
       "5       6  0.166667\n",
       "6       7  0.111111\n",
       "7       8  0.000000\n",
       "8       9  1.000000\n",
       "9       A  0.068966\n",
       "10      C  0.340426\n",
       "11      F  0.571429\n",
       "12      L  0.250000\n",
       "13      P  0.646154\n",
       "14      S  0.323077\n",
       "15      W  0.153846"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    429\n",
       "2    278\n",
       "1    210\n",
       "S     98\n",
       "P     98\n",
       "C     77\n",
       "A     42\n",
       "W     19\n",
       "F     13\n",
       "7     13\n",
       "4     11\n",
       "6      9\n",
       "L      5\n",
       "5      3\n",
       "8      2\n",
       "9      2\n",
       "Name: Ticket, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main categories of Ticket are \"1\", \"2\", \"3\", \"P\", \"S\", and \"C\", so I will combine all the others into \"4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.630137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.239203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C</td>\n",
       "      <td>0.340426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P</td>\n",
       "      <td>0.646154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S</td>\n",
       "      <td>0.323077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticket  Survived\n",
       "0      1  0.630137\n",
       "1      2  0.464481\n",
       "2      3  0.239203\n",
       "3      4  0.166667\n",
       "4      C  0.340426\n",
       "5      P  0.646154\n",
       "6      S  0.323077"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket'] = df['Ticket'].replace(['A','W','F','L','5','6','7','8','9'], '4')\n",
    "\n",
    "# check the correlation again\n",
    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "      <th>Others</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fare-bin</th>\n",
       "      <th>Age-bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>P</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Parch  PassengerId  Pclass  Sex  SibSp  Survived Ticket  \\\n",
       "0  22.0   7.2500      0            1       3    0      1       0.0      4   \n",
       "1  38.0  71.2833      0            2       1    1      1       1.0      P   \n",
       "2  26.0   7.9250      0            3       3    1      0       1.0      S   \n",
       "3  35.0  53.1000      0            4       1    1      1       1.0      1   \n",
       "4  35.0   8.0500      0            5       3    0      0       0.0      3   \n",
       "\n",
       "  Title  Master  Miss  Mr  Mrs  Others  Family  Fare-bin  Age-bin  \n",
       "0    Mr       0     0   1    0       0       2         1        4  \n",
       "1   Mrs       0     0   0    1       0       2         5        5  \n",
       "2  Miss       0     1   0    0       0       1         2        4  \n",
       "3   Mrs       0     0   0    1       0       2         5        5  \n",
       "4    Mr       0     0   1    0       0       1         2        5  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age' 'Fare' 'Parch' 'PassengerId' 'Pclass' 'Sex' 'SibSp' 'Survived'\n",
      " 'Title' 'Master' 'Miss' 'Mr' 'Mrs' 'Others' 'Family' 'Fare-bin' 'Age-bin'\n",
      " 'Ticket_1' 'Ticket_2' 'Ticket_3' 'Ticket_4' 'Ticket_C' 'Ticket_P'\n",
      " 'Ticket_S']\n"
     ]
    }
   ],
   "source": [
    "# dummy encoding\n",
    "df = pd.get_dummies(df,columns=['Ticket'])\n",
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Ticket_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
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       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare  Parch  PassengerId  Pclass  Sex  SibSp  Survived Title  \\\n",
       "0  22.0   7.2500      0            1       3    0      1       0.0    Mr   \n",
       "1  38.0  71.2833      0            2       1    1      1       1.0   Mrs   \n",
       "2  26.0   7.9250      0            3       3    1      0       1.0  Miss   \n",
       "3  35.0  53.1000      0            4       1    1      1       1.0   Mrs   \n",
       "4  35.0   8.0500      0            5       3    0      0       0.0    Mr   \n",
       "\n",
       "   Master    ...     Family  Fare-bin  Age-bin  Ticket_1  Ticket_2  Ticket_3  \\\n",
       "0       0    ...          2         1        4         0         0         0   \n",
       "1       0    ...          2         5        5         0         0         0   \n",
       "2       0    ...          1         2        4         0         0         0   \n",
       "3       0    ...          2         5        5         1         0         0   \n",
       "4       0    ...          1         2        5         0         0         1   \n",
       "\n",
       "   Ticket_4  Ticket_C  Ticket_P  Ticket_S  \n",
       "0         1         0         0         0  \n",
       "1         0         0         1         0  \n",
       "2         0         0         0         1  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can drop the features we don't need and split the data into training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(labels=['SibSp','Parch','Age','Fare','Title'], axis=1)\n",
    "y_train = df[0:891]['Survived']\n",
    "X_train = df[0:891].drop(['Survived','PassengerId'], axis=1)\n",
    "X_test  = df[891:].drop(['Survived','PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Survived  Master  Miss  Mr  Mrs  Others  Family  \\\n",
       "0            1       3    0       0.0       0     0   1    0       0       2   \n",
       "1            2       1    1       1.0       0     0   0    1       0       2   \n",
       "2            3       3    1       1.0       0     1   0    0       0       1   \n",
       "3            4       1    1       1.0       0     0   0    1       0       2   \n",
       "4            5       3    0       0.0       0     0   1    0       0       1   \n",
       "\n",
       "   Fare-bin  Age-bin  Ticket_1  Ticket_2  Ticket_3  Ticket_4  Ticket_C  \\\n",
       "0         1        4         0         0         0         1         0   \n",
       "1         5        5         0         0         0         0         0   \n",
       "2         2        4         0         0         0         0         0   \n",
       "3         5        5         1         0         0         0         0   \n",
       "4         2        5         0         0         1         0         0   \n",
       "\n",
       "   Ticket_P  Ticket_S  \n",
       "0         0         0  \n",
       "1         1         0  \n",
       "2         0         1  \n",
       "3         0         0  \n",
       "4         0         0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    1.0\n",
       "3    1.0\n",
       "4    0.0\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Master  Miss  Mr  Mrs  Others  Family  Fare-bin  Age-bin  \\\n",
       "0       3    0       0     0   1    0       0       2         1        4   \n",
       "1       1    1       0     0   0    1       0       2         5        5   \n",
       "2       3    1       0     1   0    0       0       1         2        4   \n",
       "3       1    1       0     0   0    1       0       2         5        5   \n",
       "4       3    0       0     0   1    0       0       1         2        5   \n",
       "\n",
       "   Ticket_1  Ticket_2  Ticket_3  Ticket_4  Ticket_C  Ticket_P  Ticket_S  \n",
       "0         0         0         0         1         0         0         0  \n",
       "1         0         0         0         0         0         1         0  \n",
       "2         0         0         0         0         0         0         1  \n",
       "3         1         0         0         0         0         0         0  \n",
       "4         0         0         1         0         0         0         0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "      <th>Others</th>\n",
       "      <th>Family</th>\n",
       "      <th>Fare-bin</th>\n",
       "      <th>Age-bin</th>\n",
       "      <th>Ticket_1</th>\n",
       "      <th>Ticket_2</th>\n",
       "      <th>Ticket_3</th>\n",
       "      <th>Ticket_4</th>\n",
       "      <th>Ticket_C</th>\n",
       "      <th>Ticket_P</th>\n",
       "      <th>Ticket_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>3</td>\n",
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       "      <th>892</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
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       "      <th>894</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Master  Miss  Mr  Mrs  Others  Family  Fare-bin  Age-bin  \\\n",
       "891       3    0       0     0   1    0       0       1         1        5   \n",
       "892       3    1       0     0   0    1       0       2         1        5   \n",
       "893       2    0       0     0   1    0       0       1         2        6   \n",
       "894       3    0       0     0   1    0       0       1         2        4   \n",
       "895       3    1       0     0   0    1       0       3         3        4   \n",
       "\n",
       "     Ticket_1  Ticket_2  Ticket_3  Ticket_4  Ticket_C  Ticket_P  Ticket_S  \n",
       "891         0         0         1         0         0         0         0  \n",
       "892         0         0         1         0         0         0         0  \n",
       "893         0         1         0         0         0         0         0  \n",
       "894         0         0         1         0         0         0         0  \n",
       "895         0         0         1         0         0         0         0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save pre-procesed data in a csv\n",
    "\n",
    "X_train.to_csv(\"X_train.csv\", index=False, header=False)\n",
    "y_train.to_csv(\"y_train.csv\", index=False, header=False)\n",
    "X_test.to_csv(\"X_test.csv\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the data to numpy arrays\n",
    "\n",
    "y_train = df[0:891]['Survived'].values\n",
    "X_train = df[0:891].drop(['Survived','PassengerId'], axis=1).values\n",
    "X_test  = df[891:].drop(['Survived','PassengerId'], axis=1).values"
   ]
  }
 ],
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