Titanic Survival Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Titanic Passanger Survival Analysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"input/train.csv\")\n",
    "test = pd.read_csv(\"input/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Shape: (891, 12)\n",
      "Test Shape: (418, 11)\n"
     ]
    }
   ],
   "source": [
    "train.isnull().sum()\n",
    "print(\"Train Shape:\",train.shape)\n",
    "test.isnull().sum()\n",
    "print(\"Test Shape:\",test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "\n",
    "* Survived: 0 = No, 1 = Yes\n",
    "* pclass: Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd\n",
    "* sibsp: # of siblings / spouses aboard the Titanic\n",
    "* parch: # of parents / children aboard the Titanic\n",
    "* ticket: Ticket number\n",
    "* cabin: Cabin number\n",
    "* embarked: Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton\n",
    "\n",
    "**Total rows and columns**\n",
    "\n",
    "We can see that there are 891 rows and 12 columns in our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "      <td>C</td>\n",
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      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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",
       "      <th>Survived</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",
       "      <td></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",
       "      <td></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",
       "      <td></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",
       "      <td></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",
       "      <td></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",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked Survived  \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           "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()\n",
    "test[\"Survived\"] = \"\"\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization using Matplotlib and Seaborn packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # Plot the graphes\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set() # setting seaborn default for plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bar Chart for Categorical Features \n",
    "\n",
    "* Pclass\n",
    "* Sex\n",
    "* SibSp ( # of siblings and spouse)\n",
    "* Parch ( # of parents and children)\n",
    "* Embarked\n",
    "* Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bar_chart(feature):\n",
    "    survived = train[train['Survived']==1][feature].value_counts()\n",
    "    dead = train[train['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived','Dead']\n",
    "    df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived :\n",
      " female    233\n",
      "male      109\n",
      "Name: Sex, dtype: int64\n",
      "Dead:\n",
      " male      468\n",
      "female     81\n",
      "Name: Sex, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2085d226358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')\n",
    "print(\"Survived :\\n\",train[train['Survived']==1]['Sex'].value_counts())\n",
    "print(\"Dead:\\n\",train[train['Survived']==0]['Sex'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **Women more likely survivied than Men**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived :\n",
      " 1    136\n",
      "3    119\n",
      "2     87\n",
      "Name: Pclass, dtype: int64\n",
      "Dead:\n",
      " 3    372\n",
      "2     97\n",
      "1     80\n",
      "Name: Pclass, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2085f6b82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Pclass')\n",
    "print(\"Survived :\\n\",train[train['Survived']==1]['Pclass'].value_counts())\n",
    "print(\"Dead:\\n\",train[train['Survived']==0]['Pclass'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **1st class** more likely survivied than **other classes**.  \n",
    "The Chart confirms **3rd class** more likely dead than **other classes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived :\n",
      " 0    210\n",
      "1    112\n",
      "2     13\n",
      "3      4\n",
      "4      3\n",
      "Name: SibSp, dtype: int64\n",
      "Dead:\n",
      " 0    398\n",
      "1     97\n",
      "4     15\n",
      "2     15\n",
      "3     12\n",
      "8      7\n",
      "5      5\n",
      "Name: SibSp, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2085d25b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('SibSp')\n",
    "print(\"Survived :\\n\",train[train['Survived']==1]['SibSp'].value_counts())\n",
    "print(\"Dead:\\n\",train[train['Survived']==0]['SibSp'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms a **person aboarded with more than 2 siblings or spouse** more likely survived.  \n",
    "The Chart confirms a **person aboarded without siblings or spouse** more likely dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived :\n",
      " 0    233\n",
      "1     65\n",
      "2     40\n",
      "3      3\n",
      "5      1\n",
      "Name: Parch, dtype: int64\n",
      "Dead:\n",
      " 0    445\n",
      "1     53\n",
      "2     40\n",
      "5      4\n",
      "4      4\n",
      "3      2\n",
      "6      1\n",
      "Name: Parch, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2086191d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Parch')\n",
    "print(\"Survived :\\n\",train[train['Survived']==1]['Parch'].value_counts())\n",
    "print(\"Dead:\\n\",train[train['Survived']==0]['Parch'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms a **person aboarded with more than 2 parents or children more likely survived.**  \n",
    "The Chart confirms a **person aboarded alone more likely dead**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived :\n",
      " S    217\n",
      "C     93\n",
      "Q     30\n",
      "Name: Embarked, dtype: int64\n",
      "Dead:\n",
      " S    427\n",
      "C     75\n",
      "Q     47\n",
      "Name: Embarked, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2086191d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Embarked')\n",
    "print(\"Survived :\\n\",train[train['Survived']==1]['Embarked'].value_counts())\n",
    "print(\"Dead:\\n\",train[train['Survived']==0]['Embarked'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms a **person aboarded from C** slightly more likely survived.  \n",
    "The Chart confirms a **person aboarded from Q** more likely dead.  \n",
    "The Chart confirms a **person aboarded from S** more likely dead.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering is the process of using domain knowledge of the data\n",
    "to create features (**feature vectors**) that make machine learning algorithms work.  \n",
    "\n",
    "feature vector is an n-dimensional vector of numerical features that represent some object.\n",
    "Many algorithms in machine learning require a numerical representation of objects,\n",
    "since such representations facilitate processing and statistical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 how titanic sank?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test_data = [train,test] # combine dataset\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Mlle          2\n",
       "Col           2\n",
       "Major         2\n",
       "Lady          1\n",
       "Sir           1\n",
       "Mme           1\n",
       "Ms            1\n",
       "Don           1\n",
       "Countess      1\n",
       "Capt          1\n",
       "Jonkheer      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Rev         2\n",
       "Col         2\n",
       "Dr          1\n",
       "Ms          1\n",
       "Dona        1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Title Map\n",
    "\n",
    "Mr : 0   \n",
    "Miss : 1  \n",
    "Mrs: 2  \n",
    "Others: 3  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset[\"Title\"].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
       "      <th>Survived</th>\n",
       "      <th>Title</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",
       "      <td></td>\n",
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       "      <th>1</th>\n",
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       "      <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",
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       "      <td>2</td>\n",
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       "    <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",
       "      <td></td>\n",
       "      <td>0</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",
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       "      <td></td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>2</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",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked Survived  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q               0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S               2  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q               0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S               0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S               2  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "      <th>Survived</th>\n",
       "      <th>Title</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",
       "      <td></td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>2</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",
       "      <td></td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>2</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",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked Survived  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q               0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S               2  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q               0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S               0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S               2  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RI3r/Zezatectjg0kyY4du6seARiAPLe8tkOF5iE/5fezn/0sF110UT7/+c9n+vTpSZIT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\nKgCAQoIKAKCQoAIAKPT/AIfgDD6XLCwsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861a20da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# delete unnecessary feature from dataset\n",
    "train.drop('Name', axis=1, inplace=True)\n",
    "test.drop('Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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as2ZNPvWpT+Xxxx9PkqxYsSKLFi3Kd77znTQajaxduzZbtmzJxo0bc/3116e7uzuf/vSn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\nmFMNK9lGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861aca898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    0  34.5      0      0   330911   7.8292   NaN   \n",
       "1          893       3    1  47.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    0  62.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    0  27.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3    1  22.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "  Embarked Survived  Title  \n",
       "0        Q               0  \n",
       "1        S               2  \n",
       "2        Q               0  \n",
       "3        S               0  \n",
       "4        S               2  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace= True)\n",
    "test[\"Age\"].fillna(test.groupby('Title')['Age'].transform(\"median\"), inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0             1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  38.0      1      0          PC 17599   \n",
       "2             3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  35.0      1      0            113803   \n",
       "4             5         0       3    0  35.0      0      0            373450   \n",
       "5             6         0       3    0  30.0      0      0            330877   \n",
       "6             7         0       1    0  54.0      0      0             17463   \n",
       "7             8         0       3    0   2.0      3      1            349909   \n",
       "8             9         1       3    1  27.0      0      2            347742   \n",
       "9            10         1       2    1  14.0      1      0            237736   \n",
       "10           11         1       3    1   4.0      1      1           PP 9549   \n",
       "11           12         1       1    1  58.0      0      0            113783   \n",
       "12           13         0       3    0  20.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  39.0      1      5            347082   \n",
       "14           15         0       3    1  14.0      0      0            350406   \n",
       "15           16         1       2    1  55.0      0      0            248706   \n",
       "16           17         0       3    0   2.0      4      1            382652   \n",
       "17           18         1       2    0  30.0      0      0            244373   \n",
       "18           19         0       3    1  31.0      1      0            345763   \n",
       "19           20         1       3    1  35.0      0      0              2649   \n",
       "20           21         0       2    0  35.0      0      0            239865   \n",
       "21           22         1       2    0  34.0      0      0            248698   \n",
       "22           23         1       3    1  15.0      0      0            330923   \n",
       "23           24         1       1    0  28.0      0      0            113788   \n",
       "24           25         0       3    1   8.0      3      1            349909   \n",
       "25           26         1       3    1  38.0      1      5            347077   \n",
       "26           27         0       3    0  30.0      0      0              2631   \n",
       "27           28         0       1    0  19.0      3      2             19950   \n",
       "28           29         1       3    1  21.0      0      0            330959   \n",
       "29           30         0       3    0  30.0      0      0            349216   \n",
       "\n",
       "        Fare        Cabin Embarked  Title  \n",
       "0     7.2500          NaN        S      0  \n",
       "1    71.2833          C85        C      2  \n",
       "2     7.9250          NaN        S      1  \n",
       "3    53.1000         C123        S      2  \n",
       "4     8.0500          NaN        S      0  \n",
       "5     8.4583          NaN        Q      0  \n",
       "6    51.8625          E46        S      0  \n",
       "7    21.0750          NaN        S      3  \n",
       "8    11.1333          NaN        S      2  \n",
       "9    30.0708          NaN        C      2  \n",
       "10   16.7000           G6        S      1  \n",
       "11   26.5500         C103        S      1  \n",
       "12    8.0500          NaN        S      0  \n",
       "13   31.2750          NaN        S      0  \n",
       "14    7.8542          NaN        S      1  \n",
       "15   16.0000          NaN        S      2  \n",
       "16   29.1250          NaN        Q      3  \n",
       "17   13.0000          NaN        S      0  \n",
       "18   18.0000          NaN        S      2  \n",
       "19    7.2250          NaN        C      2  \n",
       "20   26.0000          NaN        S      0  \n",
       "21   13.0000          D56        S      0  \n",
       "22    8.0292          NaN        Q      1  \n",
       "23   35.5000           A6        S      0  \n",
       "24   21.0750          NaN        S      1  \n",
       "25   31.3875          NaN        S      2  \n",
       "26    7.2250          NaN        C      0  \n",
       "27  263.0000  C23 C25 C27        S      0  \n",
       "28    7.8792          NaN        Q      1  \n",
       "29    7.8958          NaN        S      0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(30)\n",
    "#train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IRlkyM5f5k7OlKLwJ0moohBBCiL4ghaEQYkC1dIR55qWD/ORPBwhH43z89nxmlvgGO6wh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\nqKIzcTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861b6f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(10, 50)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FpHSdnYd7+NneDmKJFMU5bh7aUEKOzznfTROz4PJwuG1yWKmEQyGEELNFgqEQQtwC2nqC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861b1a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend() \n",
    "plt.show()\n",
    "\n",
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend() \n",
    "plt.xlim(10,50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those who were **20 to 30 years old** were **more dead and more survived.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Sex            891 non-null int64\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "Title          891 non-null int64\n",
      "dtypes: float64(2), int64(7), object(3)\n",
      "memory usage: 83.6+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Sex            418 non-null int64\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "Survived       418 non-null object\n",
      "Title          418 non-null int64\n",
      "dtypes: float64(2), int64(6), object(4)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Binning**\n",
    "\n",
    "Binning/Converting Numerical Age to Categorical Variable\n",
    "\n",
    "feature vector map:\n",
    "* child: 0\n",
    "* young: 1\n",
    "* adult: 2\n",
    "* mid-age: 3\n",
    "* senior: 4"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  38.0      1      0          PC 17599   \n",
       "2            3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  35.0      1      0            113803   \n",
       "4            5         0       3    0  35.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\n",
    "    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\n",
    "    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\n",
    "    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4\n",
    "# for dataset in train_test_data:\n",
    "#     dataset.loc[]\n",
    "#train[train['Age'].isin([23])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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VaVC9//77kqRly5Zp3bp1oaCaOnWqhg4dqpKSEq1evVoZGRlav369li9frr1792rKlCla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\nA4UChVtaWmAmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861abee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.head()\n",
    "bar_chart('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NHDlSTLwjkqSlpWnWrFmaP3++lixZIkl64YUXOJvVZShUAMJWly5dlJCQIEl6\n7rnndN9996lnz54tbiQLhLsFCxZo06ZNior6r51KPXr0+NFZf3AWx1ABCFuPP/64EhMTNXXqVMXE\nxKiqqkoTJ07U6dOnVVZW5nQ8AJ0Ix1ABCFuLFi3SFVdc0Twj1bNnT73++uu65ZZbHE4GoLNhhgoA\nAMAQM1QAAACGKFQAAACGKFQAAACGKFQAAACGKFQAAACG/j/egDA1jSzZNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861723160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass1:\n",
      " S    127\n",
      "C     85\n",
      "Q      2\n",
      "Name: Embarked, dtype: int64\n",
      "Pclass2:\n",
      " S    164\n",
      "C     17\n",
      "Q      3\n",
      "Name: Embarked, dtype: int64\n",
      "Pclass3:\n",
      " S    353\n",
      "Q     72\n",
      "C     66\n",
      "Name: Embarked, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass'] == 1]['Embarked'].value_counts()\n",
    "Pclass2 = train[train['Pclass'] == 2]['Embarked'].value_counts()\n",
    "Pclass3 = train[train['Pclass'] == 3]['Embarked'].value_counts()\n",
    "df = pd.DataFrame([Pclass1,Pclass2,Pclass3])\n",
    "df.index = ['1st Class','2nd Class','3rd Class']\n",
    "df.plot(kind = 'bar', stacked =  True, figsize=(10,5))\n",
    "plt.show()\n",
    "print(\"Pclass1:\\n\",Pclass1)\n",
    "print(\"Pclass2:\\n\",Pclass2)\n",
    "print(\"Pclass3:\\n\",Pclass3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "more than 50 % of 1st class are from S embark.  \n",
    "more than 50 % of 2st class are from S embark.   \n",
    "more than 50 % of 3st class are from S embark.  \n",
    "\n",
    "**fill out missing embark with S embark**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] =  dataset['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>7.2500</td>\n",
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       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embarked_mapping = {'S':0,'C':1,'Q':2}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17601</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17604</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A./5. 2152</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11668</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.C./A.4. 23567</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0             1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  3.0      1      0          PC 17599   \n",
       "2             3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  2.0      1      0            113803   \n",
       "4             5         0       3    0  2.0      0      0            373450   \n",
       "5             6         0       3    0  2.0      0      0            330877   \n",
       "6             7         0       1    0  3.0      0      0             17463   \n",
       "7             8         0       3    0  0.0      3      1            349909   \n",
       "8             9         1       3    1  2.0      0      2            347742   \n",
       "9            10         1       2    1  0.0      1      0            237736   \n",
       "10           11         1       3    1  0.0      1      1           PP 9549   \n",
       "11           12         1       1    1  3.0      0      0            113783   \n",
       "12           13         0       3    0  1.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  3.0      1      5            347082   \n",
       "14           15         0       3    1  0.0      0      0            350406   \n",
       "15           16         1       2    1  3.0      0      0            248706   \n",
       "16           17         0       3    0  0.0      4      1            382652   \n",
       "17           18         1       2    0  2.0      0      0            244373   \n",
       "18           19         0       3    1  2.0      1      0            345763   \n",
       "19           20         1       3    1  2.0      0      0              2649   \n",
       "20           21         0       2    0  2.0      0      0            239865   \n",
       "21           22         1       2    0  2.0      0      0            248698   \n",
       "22           23         1       3    1  0.0      0      0            330923   \n",
       "23           24         1       1    0  2.0      0      0            113788   \n",
       "24           25         0       3    1  0.0      3      1            349909   \n",
       "25           26         1       3    1  3.0      1      5            347077   \n",
       "26           27         0       3    0  2.0      0      0              2631   \n",
       "27           28         0       1    0  1.0      3      2             19950   \n",
       "28           29         1       3    1  1.0      0      0            330959   \n",
       "29           30         0       3    0  2.0      0      0            349216   \n",
       "30           31         0       1    0  3.0      0      0          PC 17601   \n",
       "31           32         1       1    1  2.0      1      0          PC 17569   \n",
       "32           33         1       3    1  1.0      0      0            335677   \n",
       "33           34         0       2    0  4.0      0      0        C.A. 24579   \n",
       "34           35         0       1    0  2.0      1      0          PC 17604   \n",
       "35           36         0       1    0  3.0      1      0            113789   \n",
       "36           37         1       3    0  2.0      0      0              2677   \n",
       "37           38         0       3    0  1.0      0      0        A./5. 2152   \n",
       "38           39         0       3    1  1.0      2      0            345764   \n",
       "39           40         1       3    1  0.0      1      0              2651   \n",
       "40           41         0       3    1  3.0      1      0              7546   \n",
       "41           42         0       2    1  2.0      1      0             11668   \n",
       "42           43         0       3    0  2.0      0      0            349253   \n",
       "43           44         1       2    1  0.0      1      2     SC/Paris 2123   \n",
       "44           45         1       3    1  1.0      0      0            330958   \n",
       "45           46         0       3    0  2.0      0      0   S.C./A.4. 23567   \n",
       "46           47         0       3    0  2.0      1      0            370371   \n",
       "47           48         1       3    1  1.0      0      0             14311   \n",
       "48           49         0       3    0  2.0      2      0              2662   \n",
       "49           50         0       3    1  1.0      1      0            349237   \n",
       "\n",
       "        Fare        Cabin  Embarked  Title  \n",
       "0     7.2500          NaN         0      0  \n",
       "1    71.2833          C85         1      2  \n",
       "2     7.9250          NaN         0      1  \n",
       "3    53.1000         C123         0      2  \n",
       "4     8.0500          NaN         0      0  \n",
       "5     8.4583          NaN         2      0  \n",
       "6    51.8625          E46         0      0  \n",
       "7    21.0750          NaN         0      3  \n",
       "8    11.1333          NaN         0      2  \n",
       "9    30.0708          NaN         1      2  \n",
       "10   16.7000           G6         0      1  \n",
       "11   26.5500         C103         0      1  \n",
       "12    8.0500          NaN         0      0  \n",
       "13   31.2750          NaN         0      0  \n",
       "14    7.8542          NaN         0      1  \n",
       "15   16.0000          NaN         0      2  \n",
       "16   29.1250          NaN         2      3  \n",
       "17   13.0000          NaN         0      0  \n",
       "18   18.0000          NaN         0      2  \n",
       "19    7.2250          NaN         1      2  \n",
       "20   26.0000          NaN         0      0  \n",
       "21   13.0000          D56         0      0  \n",
       "22    8.0292          NaN         2      1  \n",
       "23   35.5000           A6         0      0  \n",
       "24   21.0750          NaN         0      1  \n",
       "25   31.3875          NaN         0      2  \n",
       "26    7.2250          NaN         1      0  \n",
       "27  263.0000  C23 C25 C27         0      0  \n",
       "28    7.8792          NaN         2      1  \n",
       "29    7.8958          NaN         0      0  \n",
       "30   27.7208          NaN         1      3  \n",
       "31  146.5208          B78         1      2  \n",
       "32    7.7500          NaN         2      1  \n",
       "33   10.5000          NaN         0      0  \n",
       "34   82.1708          NaN         1      0  \n",
       "35   52.0000          NaN         0      0  \n",
       "36    7.2292          NaN         1      0  \n",
       "37    8.0500          NaN         0      0  \n",
       "38   18.0000          NaN         0      1  \n",
       "39   11.2417          NaN         1      1  \n",
       "40    9.4750          NaN         0      2  \n",
       "41   21.0000          NaN         0      2  \n",
       "42    7.8958          NaN         1      0  \n",
       "43   41.5792          NaN         1      1  \n",
       "44    7.8792          NaN         2      1  \n",
       "45    8.0500          NaN         0      0  \n",
       "46   15.5000          NaN         2      0  \n",
       "47    7.7500          NaN         2      1  \n",
       "48   21.6792          NaN         1      0  \n",
       "49   17.8000          NaN         0      2  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"])\n",
    "# train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace = True)\n",
    "# test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace = True)\n",
    "# train.head(50)\n",
    "\n",
    "\n",
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "train.head(50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WLbiuy9ChQ1m0aBE+X9fmPqmrq+Opp57iy1/+crfqcc011/CHP/wh53E5g6Fpmtx3330d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\nIv2cgqGIiIiIiEg/9/8BftzdH+A6yfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20861a7b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4 )\n",
    "facet.map(sns.kdeplot, 'Fare', shade = True)\n",
    "facet.set(xlim = (0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 20)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sfdgm0YntcusGh67Nato74AUAWIxabFufgu0FNzeraTR+58c6tkl0upEew0Ag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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x208618e5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[dataset['Fare'] <= 17, 'Fare'] = 0,\n",
    "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n",
    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n",
    "    dataset.loc[dataset['Fare'] >= 100, 'Fare'] = 3"
   ]
  },
  {
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare Cabin  Embarked  Title  \n",
       "0   0.0   NaN         0      0  \n",
       "1   2.0   C85         1      2  \n",
       "2   0.0   NaN         0      1  \n",
       "3   2.0  C123         0      2  \n",
       "4   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B96 B98            4\n",
       "G6                 4\n",
       "C23 C25 C27        4\n",
       "E101               3\n",
       "C22 C26            3\n",
       "D                  3\n",
       "F2                 3\n",
       "F33                3\n",
       "B57 B59 B63 B66    2\n",
       "E24                2\n",
       "B20                2\n",
       "B22                2\n",
       "D17                2\n",
       "C92                2\n",
       "E33                2\n",
       "E67                2\n",
       "C52                2\n",
       "F4                 2\n",
       "B5                 2\n",
       "B49                2\n",
       "C65                2\n",
       "D36                2\n",
       "C93                2\n",
       "C78                2\n",
       "E25                2\n",
       "B28                2\n",
       "D33                2\n",
       "D20                2\n",
       "D35                2\n",
       "B18                2\n",
       "                  ..\n",
       "C62 C64            1\n",
       "B102               1\n",
       "E46                1\n",
       "B69                1\n",
       "E68                1\n",
       "C50                1\n",
       "C106               1\n",
       "D28                1\n",
       "E50                1\n",
       "D46                1\n",
       "B19                1\n",
       "C47                1\n",
       "A24                1\n",
       "C70                1\n",
       "E36                1\n",
       "C86                1\n",
       "A34                1\n",
       "C111               1\n",
       "A32                1\n",
       "D15                1\n",
       "B101               1\n",
       "A6                 1\n",
       "B41                1\n",
       "B94                1\n",
       "B50                1\n",
       "E17                1\n",
       "C104               1\n",
       "D56                1\n",
       "B78                1\n",
       "C95                1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] =  dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2085f6b8748>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zdkREqAIDA7zNbps9dgdwoKiocLsjwMewz8AJ2M8rjpt+9EylSv9/UisvL0/VqlVTWFiY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\nAACGKFQAAACG/g/vmvZTIXGbhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2086190acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n",
    "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**family Size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 11.0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C/LrnF/TnelkcWM6iwNJKF+2i1NcW5yRq6Tg5u6rDcHDd3KvYuOIPqfXV8PaJ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\nrObyBVGqw5X/3ZqoVCbPYDzD8MgfGk6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GYWFhZodFAW7dunXYsGEDtm7diunTpyM5Odns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\n1IoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x208618b3080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'FamilySize',shade= True)\n",
    "facet.set(xlim=(0, train['FamilySize'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n",
    "for dataset in train_test_data:\n",
    "    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</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>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare  Cabin  Embarked  Title  FamilySize  \n",
       "0   0.0    2.0         0      0         0.4  \n",
       "1   2.0    0.8         1      2         0.4  \n",
       "2   0.0    2.0         0      1         0.0  \n",
       "3   2.0    0.8         0      2         0.4  \n",
       "4   0.0    2.0         0      0         0.0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_drop = ['Ticket','SibSp','Parch']\n",
    "train = train.drop(features_drop, axis = 1)\n",
    "test = test.drop(features_drop,axis=1)\n",
    "train = train.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = train.drop('Survived', axis = 1)\n",
    "target = train['Survived']\n",
    "train_data.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
       "0       3    0  1.0   0.0    2.0         0      0         0.4\n",
       "1       1    1  3.0   2.0    0.8         1      2         0.4\n",
       "2       3    1  1.0   0.0    2.0         0      1         0.0\n",
       "3       1    1  2.0   2.0    0.8         0      2         0.4\n",
       "4       3    0  2.0   0.0    2.0         0      0         0.0\n",
       "5       3    0  2.0   0.0    2.0         2      0         0.0\n",
       "6       1    0  3.0   2.0    1.6         0      0         0.0\n",
       "7       3    0  0.0   1.0    2.0         0      3         1.6\n",
       "8       3    1  2.0   0.0    2.0         0      2         0.8\n",
       "9       2    1  0.0   2.0    1.8         1      2         0.4"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing Classifier Modules\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier,BaggingClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "Survived      891 non-null int64\n",
      "Pclass        891 non-null int64\n",
      "Sex           891 non-null int64\n",
      "Age           891 non-null float64\n",
      "Fare          891 non-null float64\n",
      "Cabin         891 non-null float64\n",
      "Embarked      891 non-null int64\n",
      "Title         891 non-null int64\n",
      "FamilySize    891 non-null float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 62.7 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.Cross Validation(k-fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82222222 0.76404494 0.80898876 0.83146067 0.87640449 0.82022472\n",
      " 0.85393258 0.79775281 0.84269663 0.84269663]\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors = 13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of Model 0 : 82.6\n",
      "Score of Model 1 : 79.8\n",
      "Score of Model 2 : 80.92\n",
      "Score of Model 3 : 78.78\n",
      "Score of Model 4 : 83.5\n",
      "Score of Model 5 : 80.02\n",
      "Score of Model 6 : 81.25\n",
      "Score of Model 7 : 81.03\n",
      "Score of Model 8 : 80.7\n"
     ]
    }
   ],
   "source": [
    "#learning_rates = [0.05, 0.1, 0.25, 0.5, 0.75, 1]\n",
    "clf = [KNeighborsClassifier(n_neighbors = 13),DecisionTreeClassifier(),\n",
    "       RandomForestClassifier(n_estimators=13),GaussianNB(),SVC(),ExtraTreeClassifier(),\n",
    "      GradientBoostingClassifier(n_estimators=10, learning_rate=1,max_features=3, max_depth =3, random_state = 10),AdaBoostClassifier(),ExtraTreesClassifier()]\n",
    "def model_fit():\n",
    "    scoring = 'accuracy'\n",
    "    for i in range(len(clf)):\n",
    "        score = cross_val_score(clf[i], train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "        print(\"Score of Model\",i,\":\",round(np.mean(score)*100,2))\n",
    "#     round(np.mean(score)*100,2)\n",
    "#     print(\"Score of :\\n\",score)\n",
    "model_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf1 = SVC()\n",
    "clf1.fit(train_data, target)\n",
    "test\n",
    "test_data = test.drop(['Survived','PassengerId'], axis=1)\n",
    "prediction = clf1.predict(test_data)\n",
    "# test_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Survived'] = prediction\n",
    "submission = pd.DataFrame(test['PassengerId'],test_data['Survived'])\n",
    "submission.to_csv(\"Submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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