jgwerner/end_to_end_notebook.ipynb

## end_to_end_notebook.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Workflow Example\n",
    "#### TItanic Dataset \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### 1. Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import packages \n",
    "from __future__ import division\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "from patsy import dmatrices\n",
    "from statsmodels.nonparametric.kde import KDEUnivariate\n",
    "from statsmodels.nonparametric import smoothers_lowess\n",
    "# remove warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.options.display.max_columns = 100\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "pd.options.display.max_rows = 100\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import data \n",
    "#Read in data from source \n",
    "df_train = pd.read_csv(\"../data/titanictrain.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Define Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describe the data using basic statistics \n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    891\n",
       "Survived       891\n",
       "Pclass         891\n",
       "Name           891\n",
       "Sex            891\n",
       "Age            714\n",
       "SibSp          891\n",
       "Parch          891\n",
       "Ticket         891\n",
       "Fare           891\n",
       "Cabin          204\n",
       "Embarked       889\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</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>29.361582</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>13.019697</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>22.000000</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>35.000000</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  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.361582    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.019697    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= df_train\n",
    "data['Age'].fillna(data['Age'].median(), inplace=True)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.034212</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.064910</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.339898</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.034212</td>\n",
       "      <td>-0.064910</td>\n",
       "      <td>-0.339898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.233296</td>\n",
       "      <td>-0.172482</td>\n",
       "      <td>0.096688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.233296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.172482</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096688</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.034212 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.064910 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.339898  0.083081  0.018443   \n",
       "Age             0.034212 -0.064910 -0.339898  1.000000 -0.233296 -0.172482   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.233296  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.172482  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096688  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096688  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for colinearity \n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Vizualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11bb8de90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S2if6BKEdiK8Ajxpj2irclpWENowOMFosoxg2wTtOY/19HouxJqISv96RsdFOGYExZgnh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XWRMWYHsCNV3qS1XgGUHXKsEGKAqN0eJx61lFcdeWUPgD2tcR5+Zyc3nluBxzly4sO2tuDeczPq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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1094cf610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the clean data\n",
    "\n",
    "fig = plt.figure(figsize=(18,6), dpi=1600) \n",
    "alpha=alpha_scatterplot = 0.2 \n",
    "alpha_bar_chart = 0.55\n",
    "\n",
    "\n",
    "ax1 = plt.subplot2grid((2,3),(0,0))\n",
    "# plots a bar graph of those who surived vs those who did not.               \n",
    "data.Survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "ax1.set_xlim(-1, 2)\n",
    "plt.title(\"Distribution of Survival, (1 = Survived)\")    \n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "plt.scatter(data.Survived, data.Age, alpha=alpha_scatterplot)\n",
    "plt.ylabel(\"Age\")                         \n",
    "plt.grid(b=True, which='major', axis='y')  \n",
    "plt.title(\"Survival by Age,  (1 = Survived)\")\n",
    "\n",
    "ax3 = plt.subplot2grid((2,3),(0,2))\n",
    "data.Pclass.value_counts().plot(kind=\"barh\", alpha=alpha_bar_chart)\n",
    "ax3.set_ylim(-1, len(data.Pclass.value_counts()))\n",
    "plt.title(\"Class Distribution\")\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0), colspan=2)\n",
    "data.Age[data.Pclass == 1].plot(kind='kde')    \n",
    "data.Age[data.Pclass == 2].plot(kind='kde')\n",
    "data.Age[data.Pclass == 3].plot(kind='kde')\n",
    "\n",
    "plt.xlabel(\"Age\")    \n",
    "plt.title(\"Age Distribution within classes\")\n",
    "plt.legend(('1st Class', '2nd Class','3rd Class'),loc='best') \n",
    "\n",
    "ax5 = plt.subplot2grid((2,3),(1,2))\n",
    "data.Embarked.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "ax5.set_xlim(-1, len(data.Embarked.value_counts()))\n",
    "plt.title(\"Passengers per boarding location\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15ddff5d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J0lpbk6QlOTXJ2Une21obm59xAQAAlp45hVtr7cQk/zjJ+2etPjfJpsHtTUnOG9w+J8m1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\nvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b7ea490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_sex = data[data['Survived']==1]['Sex'].value_counts()\n",
    "dead_sex = data[data['Survived']==0]['Sex'].value_counts()\n",
    "df = pd.DataFrame([survived_sex,dead_sex])\n",
    "df.index = ['Survived','Dead']\n",
    "df.plot(kind='bar',stacked=True, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11bc26150>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JfpJk7yQnJbmk/bRLksxv3z4xyWdrrWtqrUuT3JzkmH7VCwAA0BQD+Y5hKWXfJEckuS7J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\ngiEAAEDDCYYAAAANJxgCAAA03P8FhnL9MudLNDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15de43190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([data[data['Survived']==1]['Age'],data[data['Survived']==0]['Age']], stacked=True, color = ['y','r'],\n",
    "         bins = 30,label = ['Survived','Dead'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11c289ed0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAGCI5iwoVtWTkmxsrX05ya0tIZ26ldcAAAAYsrnczOYRSU6oqicm2TfJRFV9KMmGqlra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\nN/I5nGCh4gQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3e4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(15,8))\n",
    "plt.hist([data[data['Survived']==1]['Fare'],data[data['Survived']==0]['Fare']], stacked=True, color = ['g','y'],\n",
    "         bins = 30,label = ['Survived','Dead'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bbdda50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DEXoAAACDEXoAAACDEXoAAACDEXoAAACDEXoAAACDEXoAAACDEXoAAACDEXoA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\nuqUL4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc79490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "ax.set_ylabel('Average fare')\n",
    "data.groupby('Pclass').mean()['Fare'].plot(kind='bar',figsize=(15,8), ax = ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Data Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_combined_data():\n",
    "    # reading train data\n",
    "    train = pd.read_csv('../data/titanictrain.csv')\n",
    "    \n",
    "    # reading test data\n",
    "    test = pd.read_csv('../data/titanictest.csv')\n",
    "\n",
    "    # extracting and then removing the targets from the training data \n",
    "    targets = train.Survived\n",
    "    train.drop('Survived',1,inplace=True)\n",
    "    \n",
    "\n",
    "    # merging train data and test data for future feature engineering\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace=True)\n",
    "    combined.drop('index',inplace=True,axis=1)\n",
    "    \n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "combined = get_combined_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 11)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Handle Categorical Data\n",
    "def get_titles():\n",
    "\n",
    "    global combined\n",
    "    \n",
    "    # Extract the title from each name\n",
    "    combined['Title'] = combined['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "    \n",
    "    # Map of more aggregated titles\n",
    "    Title_Dictionary = {\n",
    "                        \"Capt\":       \"Officer\",\n",
    "                        \"Col\":        \"Officer\",\n",
    "                        \"Major\":      \"Officer\",\n",
    "                        \"Jonkheer\":   \"Royalty\",\n",
    "                        \"Don\":        \"Royalty\",\n",
    "                        \"Sir\" :       \"Royalty\",\n",
    "                        \"Dr\":         \"Officer\",\n",
    "                        \"Rev\":        \"Officer\",\n",
    "                        \"the Countess\":\"Royalty\",\n",
    "                        \"Dona\":       \"Royalty\",\n",
    "                        \"Mme\":        \"Mrs\",\n",
    "                        \"Mlle\":       \"Miss\",\n",
    "                        \"Ms\":         \"Mrs\",\n",
    "                        \"Mr\" :        \"Mr\",\n",
    "                        \"Mrs\" :       \"Mrs\",\n",
    "                        \"Miss\" :      \"Miss\",\n",
    "                        \"Master\" :    \"Master\",\n",
    "                        \"Lady\" :      \"Royalty\"\n",
    "\n",
    "                        }\n",
    "    \n",
    "    # Map each title\n",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)\n",
    "# Run   \n",
    "get_titles()\n",
    "\n",
    "# View\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Miss</th>\n",
       "      <td>529.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.9625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>853.5</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>797.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.9292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>760.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>Miss</th>\n",
       "      <td>606.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>533.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Miss</th>\n",
       "      <td>603.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>668.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>Master</th>\n",
       "      <td>803.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>634.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>678.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Royalty</th>\n",
       "      <td>600.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>Master</th>\n",
       "      <td>550.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>723.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Officer</th>\n",
       "      <td>513.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>Master</th>\n",
       "      <td>789.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.3583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>640.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       PassengerId   Age  SibSp  Parch      Fare\n",
       "Sex    Pclass Title                                             \n",
       "female 1      Miss           529.5  30.0    0.0    0.0   99.9625\n",
       "              Mrs            853.5  45.0    1.0    0.0   78.1125\n",
       "              Officer        797.0  49.0    0.0    0.0   25.9292\n",
       "              Royalty        760.0  39.0    0.0    0.0   86.5000\n",
       "       2      Miss           606.5  20.0    0.0    0.0   20.2500\n",
       "              Mrs            533.0  30.0    1.0    0.0   26.0000\n",
       "       3      Miss           603.5  18.0    0.0    0.0    8.0500\n",
       "              Mrs            668.5  31.0    1.0    1.0   15.5000\n",
       "male   1      Master         803.0   6.0    1.0    2.0  134.5000\n",
       "              Mr             634.0  41.5    0.0    0.0   47.1000\n",
       "              Officer        678.0  52.0    0.0    0.0   37.5500\n",
       "              Royalty        600.0  40.0    0.0    0.0   27.7208\n",
       "       2      Master         550.0   2.0    1.0    1.0   26.0000\n",
       "              Mr             723.5  30.0    0.0    0.0   13.0000\n",
       "              Officer        513.0  41.5    0.0    0.0   13.0000\n",
       "       3      Master         789.0   6.0    3.0    1.0   22.3583\n",
       "              Mr             640.5  26.0    0.0    0.0    7.8958"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age grouping \n",
    "\n",
    "grouped = combined.groupby(['Sex','Pclass','Title'])\n",
    "grouped.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Title          1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(6)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "def process_age():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that fills the missing values of the Age variable\n",
    "    \n",
    "    def fillAges(row):\n",
    "        if row['Sex']=='female' and row['Pclass'] == 1:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return 30\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return 45\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return 49\n",
    "            elif row['Title'] == 'Royalty':\n",
    "                return 39\n",
    "\n",
    "        elif row['Sex']=='female' and row['Pclass'] == 2:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return 20\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return 30\n",
    "\n",
    "        elif row['Sex']=='female' and row['Pclass'] == 3:\n",
    "            if row['Title'] == 'Miss':\n",
    "                return 18\n",
    "            elif row['Title'] == 'Mrs':\n",
    "                return 31\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 1:\n",
    "            if row['Title'] == 'Master':\n",
    "                return 6\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return 41.5\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return 52\n",
    "            elif row['Title'] == 'Royalty':\n",
    "                return 40\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 2:\n",
    "            if row['Title'] == 'Master':\n",
    "                return 2\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return 30\n",
    "            elif row['Title'] == 'Officer':\n",
    "                return 41.5\n",
    "\n",
    "        elif row['Sex']=='male' and row['Pclass'] == 3:\n",
    "            if row['Title'] == 'Master':\n",
    "                return 6\n",
    "            elif row['Title'] == 'Mr':\n",
    "                return 26\n",
    "    \n",
    "    combined.Age = combined.apply(lambda r : fillAges(r) if np.isnan(r['Age']) else r['Age'], axis=1)\n",
    "\n",
    "process_age()\n",
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0            1       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1            2       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2            3       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3            4       1  female  35.0      1      0            113803  53.1000   \n",
       "4            5       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  \\\n",
       "0   NaN        S           0.0         0.0       1.0        0.0   \n",
       "1   C85        C           0.0         0.0       0.0        1.0   \n",
       "2   NaN        S           0.0         1.0       0.0        0.0   \n",
       "3  C123        S           0.0         0.0       0.0        1.0   \n",
       "4   NaN        S           0.0         0.0       1.0        0.0   \n",
       "\n",
       "   Title_Officer  Title_Royalty  \n",
       "0            0.0            0.0  \n",
       "1            0.0            0.0  \n",
       "2            0.0            0.0  \n",
       "3            0.0            0.0  \n",
       "4            0.0            0.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def process_names():\n",
    "    \n",
    "    global combined\n",
    "    # we clean the Name variable\n",
    "    combined.drop('Name',axis=1,inplace=True)\n",
    "    \n",
    "    # encoding in dummy variable\n",
    "    titles_dummies = pd.get_dummies(combined['Title'],prefix='Title')\n",
    "    combined = pd.concat([combined,titles_dummies],axis=1)\n",
    "    \n",
    "    # removing the title variable\n",
    "    combined.drop('Title',axis=1,inplace=True)\n",
    "    \n",
    "process_names()\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def process_fares():\n",
    "    \n",
    "    global combined\n",
    "    # there's one missing fare value - replacing it with the mean.\n",
    "    combined.Fare.fillna(combined.Fare.mean(),inplace=True)\n",
    "    \n",
    "process_fares()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_embarked():\n",
    "    \n",
    "    global combined\n",
    "    # two missing embarked values - filling them with the most frequent one (S)\n",
    "    combined.Embarked.fillna('S',inplace=True)\n",
    "    \n",
    "    # dummy encoding \n",
    "    embarked_dummies = pd.get_dummies(combined['Embarked'],prefix='Embarked')\n",
    "    combined = pd.concat([combined,embarked_dummies],axis=1)\n",
    "    combined.drop('Embarked',axis=1,inplace=True)\n",
    "    \n",
    "process_embarked()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_cabin():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # replacing missing cabins with U (for Uknown)\n",
    "    combined.Cabin.fillna('U',inplace=True)\n",
    "    \n",
    "    # mapping each Cabin value with the cabin letter\n",
    "    combined['Cabin'] = combined['Cabin'].map(lambda c : c[0])\n",
    "    \n",
    "    # dummy encoding ...\n",
    "    cabin_dummies = pd.get_dummies(combined['Cabin'],prefix='Cabin')\n",
    "    \n",
    "    combined = pd.concat([combined,cabin_dummies],axis=1)\n",
    "    \n",
    "    combined.drop('Cabin',axis=1,inplace=True)\n",
    "    \n",
    "process_cabin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 26 columns):\n",
      "PassengerId      1309 non-null int64\n",
      "Pclass           1309 non-null int64\n",
      "Sex              1309 non-null object\n",
      "Age              1309 non-null float64\n",
      "SibSp            1309 non-null int64\n",
      "Parch            1309 non-null int64\n",
      "Ticket           1309 non-null object\n",
      "Fare             1309 non-null float64\n",
      "Title_Master     1309 non-null float64\n",
      "Title_Miss       1309 non-null float64\n",
      "Title_Mr         1309 non-null float64\n",
      "Title_Mrs        1309 non-null float64\n",
      "Title_Officer    1309 non-null float64\n",
      "Title_Royalty    1309 non-null float64\n",
      "Embarked_C       1309 non-null float64\n",
      "Embarked_Q       1309 non-null float64\n",
      "Embarked_S       1309 non-null float64\n",
      "Cabin_A          1309 non-null float64\n",
      "Cabin_B          1309 non-null float64\n",
      "Cabin_C          1309 non-null float64\n",
      "Cabin_D          1309 non-null float64\n",
      "Cabin_E          1309 non-null float64\n",
      "Cabin_F          1309 non-null float64\n",
      "Cabin_G          1309 non-null float64\n",
      "Cabin_T          1309 non-null float64\n",
      "Cabin_U          1309 non-null float64\n",
      "dtypes: float64(20), int64(4), object(2)\n",
      "memory usage: 266.0+ KB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    \n",
    "    global combined\n",
    "    # mapping string values to numerical one \n",
    "    combined['Sex'] = combined['Sex'].map({'male':1,'female':0})\n",
    "    \n",
    "\n",
    "process_sex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_pclass():\n",
    "    \n",
    "    global combined\n",
    "    # encoding into 3 categories:\n",
    "    pclass_dummies = pd.get_dummies(combined['Pclass'],prefix=\"Pclass\")\n",
    "    \n",
    "    # adding dummy variables\n",
    "    combined = pd.concat([combined,pclass_dummies],axis=1)\n",
    "    \n",
    "    # removing \"Pclass\"\n",
    "    \n",
    "    combined.drop('Pclass',axis=1,inplace=True)\n",
    "    \n",
    "process_pclass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_ticket():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    # a function that extracts each prefix of the ticket, returns 'XXX' if no prefix (i.e the ticket is a digit)\n",
    "    def cleanTicket(ticket):\n",
    "        ticket = ticket.replace('.','')\n",
    "        ticket = ticket.replace('/','')\n",
    "        ticket = ticket.split()\n",
    "        ticket = map(lambda t : t.strip() , ticket)\n",
    "        ticket = filter(lambda t : not t.isdigit(), ticket)\n",
    "        if len(ticket) > 0:\n",
    "            return ticket[0]\n",
    "        else: \n",
    "            return 'XXX'\n",
    "    \n",
    "\n",
    "    # Extracting dummy variables from tickets:\n",
    "\n",
    "    combined['Ticket'] = combined['Ticket'].map(cleanTicket)\n",
    "    tickets_dummies = pd.get_dummies(combined['Ticket'],prefix='Ticket')\n",
    "    combined = pd.concat([combined, tickets_dummies],axis=1)\n",
    "    combined.drop('Ticket',inplace=True,axis=1)\n",
    "\n",
    "process_ticket()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_family():\n",
    "    \n",
    "    global combined\n",
    "    # introducing a new feature : the size of families (including the passenger)\n",
    "    combined['FamilySize'] = combined['Parch'] + combined['SibSp'] + 1\n",
    "    \n",
    "    # introducing other features based on the family size\n",
    "    combined['Singleton'] = combined['FamilySize'].map(lambda s : 1 if s == 1 else 0)\n",
    "    combined['SmallFamily'] = combined['FamilySize'].map(lambda s : 1 if 2<=s<=4 else 0)\n",
    "    combined['LargeFamily'] = combined['FamilySize'].map(lambda s : 1 if 5<=s else 0)\n",
    "    \n",
    "process_family()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Ticket_A</th>\n",
       "      <th>Ticket_A4</th>\n",
       "      <th>Ticket_A5</th>\n",
       "      <th>Ticket_AQ3</th>\n",
       "      <th>Ticket_AQ4</th>\n",
       "      <th>Ticket_AS</th>\n",
       "      <th>Ticket_C</th>\n",
       "      <th>Ticket_CA</th>\n",
       "      <th>Ticket_CASOTON</th>\n",
       "      <th>Ticket_FC</th>\n",
       "      <th>Ticket_FCC</th>\n",
       "      <th>Ticket_Fa</th>\n",
       "      <th>Ticket_LINE</th>\n",
       "      <th>Ticket_LP</th>\n",
       "      <th>Ticket_PC</th>\n",
       "      <th>Ticket_PP</th>\n",
       "      <th>Ticket_PPP</th>\n",
       "      <th>Ticket_SC</th>\n",
       "      <th>Ticket_SCA3</th>\n",
       "      <th>Ticket_SCA4</th>\n",
       "      <th>Ticket_SCAH</th>\n",
       "      <th>Ticket_SCOW</th>\n",
       "      <th>Ticket_SCPARIS</th>\n",
       "      <th>Ticket_SCParis</th>\n",
       "      <th>Ticket_SOC</th>\n",
       "      <th>Ticket_SOP</th>\n",
       "      <th>Ticket_SOPP</th>\n",
       "      <th>Ticket_SOTONO2</th>\n",
       "      <th>Ticket_SOTONOQ</th>\n",
       "      <th>Ticket_SP</th>\n",
       "      <th>Ticket_STONO</th>\n",
       "      <th>Ticket_STONO2</th>\n",
       "      <th>Ticket_STONOQ</th>\n",
       "      <th>Ticket_SWPP</th>\n",
       "      <th>Ticket_WC</th>\n",
       "      <th>Ticket_WEP</th>\n",
       "      <th>Ticket_XXX</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Singleton</th>\n",
       "      <th>SmallFamily</th>\n",
       "      <th>LargeFamily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Sex   Age  SibSp  Parch     Fare  Title_Master  Title_Miss  \\\n",
       "0            1    1  22.0      1      0   7.2500           0.0         0.0   \n",
       "1            2    0  38.0      1      0  71.2833           0.0         0.0   \n",
       "2            3    0  26.0      0      0   7.9250           0.0         1.0   \n",
       "3            4    0  35.0      1      0  53.1000           0.0         0.0   \n",
       "4            5    1  35.0      0      0   8.0500           0.0         0.0   \n",
       "\n",
       "   Title_Mr  Title_Mrs  Title_Officer  Title_Royalty  Embarked_C  Embarked_Q  \\\n",
       "0       1.0        0.0            0.0            0.0         0.0         0.0   \n",
       "1       0.0        1.0            0.0            0.0         1.0         0.0   \n",
       "2       0.0        0.0            0.0            0.0         0.0         0.0   \n",
       "3       0.0        1.0            0.0            0.0         0.0         0.0   \n",
       "4       1.0        0.0            0.0            0.0         0.0         0.0   \n",
       "\n",
       "   Embarked_S  Cabin_A  Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  \\\n",
       "0         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1         0.0      0.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "2         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "3         1.0      0.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "4         1.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "   Cabin_T  Cabin_U  Pclass_1  Pclass_2  Pclass_3  Ticket_A  Ticket_A4  \\\n",
       "0      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "1      0.0      0.0       1.0       0.0       0.0       0.0        0.0   \n",
       "2      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "3      0.0      0.0       1.0       0.0       0.0       0.0        0.0   \n",
       "4      0.0      1.0       0.0       0.0       1.0       0.0        0.0   \n",
       "\n",
       "   Ticket_A5  Ticket_AQ3  Ticket_AQ4  Ticket_AS  Ticket_C  Ticket_CA  \\\n",
       "0        1.0         0.0         0.0        0.0       0.0        0.0   \n",
       "1        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "2        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "3        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "4        0.0         0.0         0.0        0.0       0.0        0.0   \n",
       "\n",
       "   Ticket_CASOTON  Ticket_FC  Ticket_FCC  Ticket_Fa  Ticket_LINE  Ticket_LP  \\\n",
       "0             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "1             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "2             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "3             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "4             0.0        0.0         0.0        0.0          0.0        0.0   \n",
       "\n",
       "   Ticket_PC  Ticket_PP  Ticket_PPP  Ticket_SC  Ticket_SCA3  Ticket_SCA4  \\\n",
       "0        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "1        1.0        0.0         0.0        0.0          0.0          0.0   \n",
       "2        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "3        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "4        0.0        0.0         0.0        0.0          0.0          0.0   \n",
       "\n",
       "   Ticket_SCAH  Ticket_SCOW  Ticket_SCPARIS  Ticket_SCParis  Ticket_SOC  \\\n",
       "0          0.0          0.0             0.0             0.0         0.0   \n",
       "1          0.0          0.0             0.0             0.0         0.0   \n",
       "2          0.0          0.0             0.0             0.0         0.0   \n",
       "3          0.0          0.0             0.0             0.0         0.0   \n",
       "4          0.0          0.0             0.0             0.0         0.0   \n",
       "\n",
       "   Ticket_SOP  Ticket_SOPP  Ticket_SOTONO2  Ticket_SOTONOQ  Ticket_SP  \\\n",
       "0         0.0          0.0             0.0             0.0        0.0   \n",
       "1         0.0          0.0             0.0             0.0        0.0   \n",
       "2         0.0          0.0             0.0             0.0        0.0   \n",
       "3         0.0          0.0             0.0             0.0        0.0   \n",
       "4         0.0          0.0             0.0             0.0        0.0   \n",
       "\n",
       "   Ticket_STONO  Ticket_STONO2  Ticket_STONOQ  Ticket_SWPP  Ticket_WC  \\\n",
       "0           0.0            0.0            0.0          0.0        0.0   \n",
       "1           0.0            0.0            0.0          0.0        0.0   \n",
       "2           0.0            1.0            0.0          0.0        0.0   \n",
       "3           0.0            0.0            0.0          0.0        0.0   \n",
       "4           0.0            0.0            0.0          0.0        0.0   \n",
       "\n",
       "   Ticket_WEP  Ticket_XXX  FamilySize  Singleton  SmallFamily  LargeFamily  \n",
       "0         0.0         0.0           2          0            1            0  \n",
       "1         0.0         0.0           2          0            1            0  \n",
       "2         0.0         0.0           1          1            0            0  \n",
       "3         0.0         1.0           2          0            1            0  \n",
       "4         0.0         1.0           1          1            0            0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#scale to correct for interval\n",
    "\n",
    "def scale_all_features():\n",
    "    \n",
    "    global combined\n",
    "    \n",
    "    features = list(combined.columns)\n",
    "    features.remove('PassengerId')\n",
    "    combined[features] = combined[features].apply(lambda x: x/x.max(), axis=0)\n",
    "    \n",
    "    print 'Features scaled successfully!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features scaled successfully!\n"
     ]
    }
   ],
   "source": [
    "scale_all_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.cross_validation import StratifiedKFold\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_score(clf, X, y,scoring='accuracy'):\n",
    "    xval = cross_val_score(clf, X, y, cv = 5,scoring=scoring)\n",
    "    return np.mean(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined\n",
    "    \n",
    "    train0 = pd.read_csv('../data/titanictrain.csv')\n",
    "    \n",
    "    targets = train0.Survived\n",
    "    train = combined.ix[0:890]\n",
    "    test = combined.ix[891:]\n",
    "    \n",
    "    return train,test,targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train,test,targets = recover_train_test_target()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = ExtraTreesClassifier(n_estimators=200)\n",
    "clf = clf.fit(train, targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = pd.DataFrame()\n",
    "features['feature'] = train.columns\n",
    "features['importance'] = clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0.128551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.119907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Title_Mr</td>\n",
       "      <td>0.119459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.114279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>0.099397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Pclass_3</td>\n",
       "      <td>0.041191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Title_Miss</td>\n",
       "      <td>0.040646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Title_Mrs</td>\n",
       "      <td>0.032838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Cabin_U</td>\n",
       "      <td>0.026212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pclass_1</td>\n",
       "      <td>0.022409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>FamilySize</td>\n",
       "      <td>0.020086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>SmallFamily</td>\n",
       "      <td>0.019078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>LargeFamily</td>\n",
       "      <td>0.017246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>0.016695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>0.015137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Pclass_2</td>\n",
       "      <td>0.013053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Title_Master</td>\n",
       "      <td>0.012673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Embarked_S</td>\n",
       "      <td>0.012530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Ticket_XXX</td>\n",
       "      <td>0.012158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Singleton</td>\n",
       "      <td>0.011730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Embarked_C</td>\n",
       "      <td>0.010622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cabin_E</td>\n",
       "      <td>0.010226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Title_Officer</td>\n",
       "      <td>0.008389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Embarked_Q</td>\n",
       "      <td>0.007250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Cabin_B</td>\n",
       "      <td>0.006664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Ticket_PC</td>\n",
       "      <td>0.006643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cabin_D</td>\n",
       "      <td>0.006570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cabin_C</td>\n",
       "      <td>0.006377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Ticket_SWPP</td>\n",
       "      <td>0.006249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Ticket_STONO</td>\n",
       "      <td>0.005688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ticket_A5</td>\n",
       "      <td>0.003607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ticket_CA</td>\n",
       "      <td>0.003341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Ticket_WC</td>\n",
       "      <td>0.002735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cabin_A</td>\n",
       "      <td>0.002361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Ticket_SOTONOQ</td>\n",
       "      <td>0.001978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Ticket_C</td>\n",
       "      <td>0.001887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Ticket_STONO2</td>\n",
       "      <td>0.001552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Ticket_SOPP</td>\n",
       "      <td>0.001544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Cabin_F</td>\n",
       "      <td>0.001497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cabin_G</td>\n",
       "      <td>0.001330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Title_Royalty</td>\n",
       "      <td>0.001161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Ticket_WEP</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Ticket_PP</td>\n",
       "      <td>0.000826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ticket_FC</td>\n",
       "      <td>0.000662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Ticket_FCC</td>\n",
       "      <td>0.000586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Ticket_LINE</td>\n",
       "      <td>0.000533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Ticket_SOC</td>\n",
       "      <td>0.000527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ticket_A4</td>\n",
       "      <td>0.000519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Ticket_SCPARIS</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Ticket_SCParis</td>\n",
       "      <td>0.000447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Cabin_T</td>\n",
       "      <td>0.000329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Ticket_SOP</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Ticket_SCAH</td>\n",
       "      <td>0.000232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Ticket_SP</td>\n",
       "      <td>0.000155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Ticket_SC</td>\n",
       "      <td>0.000096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Ticket_SOTONO2</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Ticket_PPP</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Ticket_Fa</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Ticket_SCOW</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Ticket_SCA4</td>\n",
       "      <td>0.000054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ticket_CASOTON</td>\n",
       "      <td>0.000048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Ticket_AS</td>\n",
       "      <td>0.000040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ticket_STONOQ</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ticket_A</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Ticket_SCA3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ticket_AQ3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Ticket_AQ4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Ticket_LP</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           feature  importance\n",
       "0      PassengerId    0.128551\n",
       "2              Age    0.119907\n",
       "8         Title_Mr    0.119459\n",
       "5             Fare    0.114279\n",
       "1              Sex    0.099397\n",
       "26        Pclass_3    0.041191\n",
       "7       Title_Miss    0.040646\n",
       "9        Title_Mrs    0.032838\n",
       "23         Cabin_U    0.026212\n",
       "24        Pclass_1    0.022409\n",
       "64      FamilySize    0.020086\n",
       "66     SmallFamily    0.019078\n",
       "67     LargeFamily    0.017246\n",
       "3            SibSp    0.016695\n",
       "4            Parch    0.015137\n",
       "25        Pclass_2    0.013053\n",
       "6     Title_Master    0.012673\n",
       "14      Embarked_S    0.012530\n",
       "63      Ticket_XXX    0.012158\n",
       "65       Singleton    0.011730\n",
       "12      Embarked_C    0.010622\n",
       "19         Cabin_E    0.010226\n",
       "10   Title_Officer    0.008389\n",
       "13      Embarked_Q    0.007250\n",
       "16         Cabin_B    0.006664\n",
       "41       Ticket_PC    0.006643\n",
       "18         Cabin_D    0.006570\n",
       "17         Cabin_C    0.006377\n",
       "60     Ticket_SWPP    0.006249\n",
       "57    Ticket_STONO    0.005688\n",
       "29       Ticket_A5    0.003607\n",
       "34       Ticket_CA    0.003341\n",
       "61       Ticket_WC    0.002735\n",
       "15         Cabin_A    0.002361\n",
       "55  Ticket_SOTONOQ    0.001978\n",
       "33        Ticket_C    0.001887\n",
       "58   Ticket_STONO2    0.001552\n",
       "53     Ticket_SOPP    0.001544\n",
       "20         Cabin_F    0.001497\n",
       "21         Cabin_G    0.001330\n",
       "11   Title_Royalty    0.001161\n",
       "62      Ticket_WEP    0.000969\n",
       "42       Ticket_PP    0.000826\n",
       "36       Ticket_FC    0.000662\n",
       "37      Ticket_FCC    0.000586\n",
       "39     Ticket_LINE    0.000533\n",
       "51      Ticket_SOC    0.000527\n",
       "28       Ticket_A4    0.000519\n",
       "49  Ticket_SCPARIS    0.000500\n",
       "50  Ticket_SCParis    0.000447\n",
       "22         Cabin_T    0.000329\n",
       "52      Ticket_SOP    0.000256\n",
       "47     Ticket_SCAH    0.000232\n",
       "56       Ticket_SP    0.000155\n",
       "44       Ticket_SC    0.000096\n",
       "54  Ticket_SOTONO2    0.000080\n",
       "43      Ticket_PPP    0.000072\n",
       "38       Ticket_Fa    0.000062\n",
       "48     Ticket_SCOW    0.000060\n",
       "46     Ticket_SCA4    0.000054\n",
       "35  Ticket_CASOTON    0.000048\n",
       "32       Ticket_AS    0.000040\n",
       "59   Ticket_STONOQ    0.000000\n",
       "27        Ticket_A    0.000000\n",
       "45     Ticket_SCA3    0.000000\n",
       "30      Ticket_AQ3    0.000000\n",
       "31      Ticket_AQ4    0.000000\n",
       "40       Ticket_LP    0.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.sort(['importance'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#15 features\n",
    "model = SelectFromModel(clf, prefit=True)\n",
    "train_new = model.transform(train)\n",
    "train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 15)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Shape\n",
    "test_new = model.transform(test)\n",
    "test_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.83164983165\n",
      "Best parameters: {'n_estimators': 240, 'criterion': 'gini', 'max_depth': 5}\n"
     ]
    }
   ],
   "source": [
    "#Boosted Random Forest Params\n",
    "forest = RandomForestClassifier(max_features='sqrt')\n",
    "\n",
    "parameter_grid = {\n",
    "                 'max_depth' : [4,5,6,7,8],\n",
    "                 'n_estimators': [200,210,240,250],\n",
    "                 'criterion': ['gini','entropy']\n",
    "                 }\n",
    "\n",
    "cross_validation = StratifiedKFold(targets, n_folds=5)\n",
    "\n",
    "grid_search = GridSearchCV(forest,\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation)\n",
    "\n",
    "grid_search.fit(train_new, targets)\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output = grid_search.predict(test_new).astype(int)\n",
    "df_output = pd.DataFrame()\n",
    "df_output['PassengerId'] = test['PassengerId']\n",
    "df_output['Survived'] = output\n",
    "df_output[['PassengerId','Survived']].to_csv('../data/output.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### 6. Show Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results = pd.read_csv('../data/output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11cf24190>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hlABERAqlBCAiUiglABGRQikBiIgUam6zCma2BLgGWATsAL7i7n9hZiuATwBP5aoXuvvN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3d6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph results data\n",
    "results.Survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "ax1.set_xlim(-1, 2)\n",
    "plt.title(\"Distribution of Survival Predicted, (1 = Survived)\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d4ee450>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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01datY33sV0bS0dFBb29vq5sh8hLaNxurs7MTd1893ulr3hhOXT6bzeyYVHQG8ZGqdcQv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cf6cb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph original data\n",
    "data.Survived.value_counts().plot(kind='bar', alpha=alpha_bar_chart)\n",
    "ax1.set_xlim(-1, 2)\n",
    "plt.title(\"Distribution of Survival Observed, (1 = Survived)\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### XGboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SelectKBest(k=5, score_func=<function f_classif at 0x11cdebe60>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import cross_validation\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "\n",
    "titanic = pd.read_csv('../data/titanictrain.csv', header=0)\n",
    "titanic_test = pd.read_csv('../data/titanictest.csv', header=0)\n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n",
    "\n",
    "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())\n",
    "# Replace all the occurences of male with the number 0.\n",
    "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\n",
    "# Replace all the occurences of female with the number 1.\n",
    "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1\n",
    "\n",
    "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n",
    "titanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n",
    "titanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n",
    "titanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n",
    "\n",
    "# Repeat with test dataset\n",
    "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n",
    "titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n",
    "titanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0 \n",
    "titanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n",
    "titanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n",
    "\n",
    "titanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n",
    "titanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n",
    "titanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n",
    "\n",
    "\n",
    "# Generating a familysize column\n",
    "titanic[\"FamilySize\"] = titanic[\"SibSp\"] + titanic[\"Parch\"]\n",
    "titanic_test[\"FamilySize\"] = titanic_test[\"SibSp\"] + titanic_test[\"Parch\"]\n",
    "\n",
    "# The .apply method generates a new series\n",
    "titanic[\"NameLength\"] = titanic[\"Name\"].apply(lambda x: len(x))\n",
    "titanic_test[\"NameLength\"] = titanic_test[\"Name\"].apply(lambda x: len(x))\n",
    "\n",
    "# A function to get the title from a name.\n",
    "def get_title(name):\n",
    "    # Use a regular expression to search for a title.  Titles always consist of capital and lowercase letters, and end with a period.\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "# Get all the titles\n",
    "titles = titanic[\"Name\"].apply(get_title)\n",
    "# Map each title to an integer.  Some titles are very rare, and are compressed into the same codes as other titles.\n",
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Dr\": 5, \"Rev\": 6, \"Major\": 7, \"Col\": 7, \"Mlle\": 8, \"Mme\": 8, \"Don\": 9, \"Lady\": 10, \"Countess\": 10, \"Jonkheer\": 10, \"Sir\": 9, \"Capt\": 7, \"Ms\": 2}\n",
    "for k,v in title_mapping.items():\n",
    "    titles[titles == k] = v\n",
    "\n",
    "# Add in the title column.\n",
    "titanic[\"Title\"] = titles\n",
    "\n",
    "titles_test = titanic[\"Name\"].apply(get_title)\n",
    "for k,v in title_mapping.items():\n",
    "    titles_test[titles_test == k] = v\n",
    "titanic_test[\"Title\"] = titles\n",
    "    \n",
    "    \n",
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"FamilySize\", \"NameLength\", \"Title\"]\n",
    "\n",
    "# Perform feature selection\n",
    "selector = SelectKBest(f_classif, k=5)\n",
    "selector.fit(titanic[predictors], titanic[\"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 24.59567142  68.85199425   1.27768955   0.5342545    1.82976043\n",
      "  14.21323514   2.85130099   0.20768458  23.69319016  26.98338607]\n"
     ]
    }
   ],
   "source": [
    "# Get the raw p-values for each feature, and transform from p-values into scores\n",
    "scores = -np.log10(selector.pvalues_)\n",
    "print scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Gs5iZ2TR5xaaZWcFcxM3MCuYibmZWMBdxM7OCuYibmRXMRdzMrGAu4mZmBXMRNzMrmIu4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5d1650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the scores.  See how \"Pclass\", \"Sex\", \"Title\", and \"Fare\" are the best?\n",
    "plt.bar(range(len(predictors)), scores)\n",
    "plt.xticks(range(len(predictors)), predictors, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic['Sex'] = titanic['Sex'].astype(int)\n",
    "titanic['Embarked'] = titanic['Embarked'].astype(int)\n",
    "titanic['Title'] = titanic['Title'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.927048260382\n"
     ]
    }
   ],
   "source": [
    "predictors = [\"Pclass\", \"Sex\", \"Age\", \"Fare\", \"Embarked\", \"FamilySize\", \"NameLength\", \"Title\"]\n",
    "\n",
    "titanic_test['Sex'] = titanic_test['Sex'].astype(int)\n",
    "titanic_test['Embarked'] = titanic_test['Embarked'].astype(int)\n",
    "titanic_test[\"Title\"] = titanic_test[\"Title\"].astype(int)\n",
    "train_X = titanic[predictors]\n",
    "test_X = titanic_test[predictors]\n",
    "train_y = titanic[\"Survived\"]\n",
    "\n",
    "gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.1).fit(train_X, train_y)\n",
    "predictions = gbm.predict(train_X)\n",
    "\n",
    "\n",
    "accuracy = sum(predictions[predictions == titanic[\"Survived\"]]) / len(predictions)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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crc1sAVGRW+juZwBLgRPdfaOZHUh0IkuPePmvAl2JCtxC4Hfu/jUzGw5cBlxR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\nviaODXb6C5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d861710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(gbm.feature_importances_)\n",
    "xgb.plot_importance(gbm)\n",
    "\n",
    "predictions = gbm.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({ 'PassengerId': titanic_test['PassengerId'],\n",
    "                            'Survived': predictions })\n",
    "submission.to_csv(\"../data/submission.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}