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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Kaggle Machine Learning Competition: Predicting Titanic Survivors"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Competition Site\n",
      "* Description\n",
      "* Evaluation\n",
      "* Data Set\n",
      "* Setup Imports and Variables\n",
      "* Explore the Data\n",
      "* Feature: Passenger Classes\n",
      "* Feature: Sex\n",
      "* Feature: Embarked\n",
      "* Feature: Age\n",
      "* Feature: Family Size\n",
      "* Final Data Preparation for Machine Learning\n",
      "* Data Munging Summary\n",
      "* Random Forest: Training\n",
      "* Random Forest: Predicting\n",
      "* Random Forest: Prepare for Kaggle Submission\n",
      "* Support Vector Machine: Training\n",
      "* Support Vector Machine: Predicting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Competition Site"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Description, Evaluation, and Data Set taken from the [competition site](https://www.kaggle.com/c/titanic-gettingStarted)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Description"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![alt text](http://upload.wikimedia.org/wikipedia/commons/6/6e/St%C3%B6wer_Titanic.jpg)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
      "\n",
      "One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
      "\n",
      "In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Evaluation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The historical data has been split into two groups, a 'training set' and a 'test set'.  For the training set, we provide the outcome ( 'ground truth' ) for each passenger.  You will use this set to build your model to generate predictions for the test set.\n",
      "\n",
      "For each passenger in the test set, you must predict whether or not they survived the sinking ( 0 for deceased, 1 for survived ).  Your score is the percentage of passengers you correctly predict.\n",
      "\n",
      " The Kaggle leaderboard has a public and private component.  50% of your predictions for the test set have been randomly assigned to the public leaderboard ( the same 50% for all users ).  Your score on this public portion is what will appear on the leaderboard.  At the end of the contest, we will reveal your score on the private 50% of the data, which will determine the final winner.  This method prevents users from 'overfitting' to the leaderboard."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Data Set"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "| File Name        | Available Formats |\n",
      "|------------------|-------------------|\n",
      "| train            | .csv (59.76 kb)   |\n",
      "| gendermodel      | .csv (3.18 kb)    |\n",
      "| genderclassmodel | .csv (3.18 kb)    |\n",
      "| test             | .csv (27.96 kb)   |\n",
      "| gendermodel      | .py (3.58 kb)     |\n",
      "| genderclassmodel | .py (5.63 kb)     |\n",
      "| myfirstforest    | .py (3.99 kb)     |"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<pre>\n",
      "VARIABLE DESCRIPTIONS:\n",
      "survival        Survival\n",
      "                (0 = No; 1 = Yes)\n",
      "pclass          Passenger Class\n",
      "                (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
      "name            Name\n",
      "sex             Sex\n",
      "age             Age\n",
      "sibsp           Number of Siblings/Spouses Aboard\n",
      "parch           Number of Parents/Children Aboard\n",
      "ticket          Ticket Number\n",
      "fare            Passenger Fare\n",
      "cabin           Cabin\n",
      "embarked        Port of Embarkation\n",
      "                (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
      "\n",
      "SPECIAL NOTES:\n",
      "Pclass is a proxy for socio-economic status (SES)\n",
      " 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n",
      "\n",
      "Age is in Years; Fractional if Age less than One (1)\n",
      " If the Age is Estimated, it is in the form xx.5\n",
      "\n",
      "With respect to the family relation variables (i.e. sibsp and parch)\n",
      "some relations were ignored.  The following are the definitions used\n",
      "for sibsp and parch.\n",
      "\n",
      "Sibling:  Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n",
      "Spouse:   Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n",
      "Parent:   Mother or Father of Passenger Aboard Titanic\n",
      "Child:    Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\n",
      "\n",
      "Other family relatives excluded from this study include cousins,\n",
      "nephews/nieces, aunts/uncles, and in-laws.  Some children travelled\n",
      "only with a nanny, therefore parch=0 for them.  As well, some\n",
      "travelled with very close friends or neighbors in a village, however,\n",
      "the definitions do not support such relations.\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Setup Imports and Variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pylab as plt\n",
      "\n",
      "# Set the global default size of matplotlib figures\n",
      "plt.rc('figure', figsize=(10, 5))\n",
      "\n",
      "# Size of matplotlib figures that contain subplots\n",
      "fizsize_with_subplots = (10, 10)\n",
      "\n",
      "# Size of matplotlib histogram bins\n",
      "bin_size = 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Explore the Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train = pd.read_csv('../data/titanic/train.csv')\n",
      "df_train.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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</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</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</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</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</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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "   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      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      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  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>886</th>\n",
        "      <td> 887</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>                    Montvila, Rev. Juozas</td>\n",
        "      <td>   male</td>\n",
        "      <td> 27</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>     211536</td>\n",
        "      <td> 13.00</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>887</th>\n",
        "      <td> 888</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>             Graham, Miss. Margaret Edith</td>\n",
        "      <td> female</td>\n",
        "      <td> 19</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>     112053</td>\n",
        "      <td> 30.00</td>\n",
        "      <td>  B42</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>888</th>\n",
        "      <td> 889</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
        "      <td> female</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> W./C. 6607</td>\n",
        "      <td> 23.45</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>889</th>\n",
        "      <td> 890</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                    Behr, Mr. Karl Howell</td>\n",
        "      <td>   male</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>     111369</td>\n",
        "      <td> 30.00</td>\n",
        "      <td> C148</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>890</th>\n",
        "      <td> 891</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                      Dooley, Mr. Patrick</td>\n",
        "      <td>   male</td>\n",
        "      <td> 32</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>     370376</td>\n",
        "      <td>  7.75</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "     PassengerId  Survived  Pclass                                      Name  \\\n",
        "886          887         0       2                     Montvila, Rev. Juozas   \n",
        "887          888         1       1              Graham, Miss. Margaret Edith   \n",
        "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
        "889          890         1       1                     Behr, Mr. Karl Howell   \n",
        "890          891         0       3                       Dooley, Mr. Patrick   \n",
        "\n",
        "        Sex  Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
        "886    male   27      0      0      211536  13.00   NaN        S  \n",
        "887  female   19      0      0      112053  30.00   B42        S  \n",
        "888  female  NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
        "889    male   26      0      0      111369  30.00  C148        C  \n",
        "890    male   32      0      0      370376   7.75   NaN        Q  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "View the data types of each column:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "PassengerId      int64\n",
        "Survived         int64\n",
        "Pclass           int64\n",
        "Name            object\n",
        "Sex             object\n",
        "Age            float64\n",
        "SibSp            int64\n",
        "Parch            int64\n",
        "Ticket          object\n",
        "Fare           float64\n",
        "Cabin           object\n",
        "Embarked        object\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Type 'object' is a string for pandas, which poses problems with machine learning algorithms.  If we want to use these as features, we'll need to convert these to number representations."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get some basic information on the DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 891 entries, 0 to 890\n",
        "Data columns (total 12 columns):\n",
        "PassengerId    891 non-null int64\n",
        "Survived       891 non-null int64\n",
        "Pclass         891 non-null int64\n",
        "Name           891 non-null object\n",
        "Sex            891 non-null object\n",
        "Age            714 non-null float64\n",
        "SibSp          891 non-null int64\n",
        "Parch          891 non-null int64\n",
        "Ticket         891 non-null object\n",
        "Fare           891 non-null float64\n",
        "Cabin          204 non-null object\n",
        "Embarked       889 non-null object\n",
        "dtypes: float64(2), int64(5), object(5)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Age, Cabin, and Embarked are missing values.  Cabin has too many missing values, whereas we might be able to infer values for Age and Embarked."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate various descriptive statistics on the DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "       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  "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have a general idea of the data set contents, we can dive deeper into each column.  We'll be doing exploratory data analysis and cleaning data to setup 'features' we'll be using in our machine learning algorithms.\n",
      "\n",
      "Plot a few features to get a better idea of each:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a grid of plots\n",
      "fig = plt.figure(figsize=fizsize_with_subplots) \n",
      "fig_dims = (3, 2)\n",
      "\n",
      "# Plot death and survival counts\n",
      "plt.subplot2grid(fig_dims, (0, 0))\n",
      "df_train['Survived'].value_counts().plot(kind='bar', \n",
      "                                         title='Death and Survival Counts')\n",
      "\n",
      "# Plot Pclass counts\n",
      "plt.subplot2grid(fig_dims, (0, 1))\n",
      "df_train['Pclass'].value_counts().plot(kind='bar', \n",
      "                                       title='Passenger Class Counts')\n",
      "\n",
      "# Plot Sex counts\n",
      "plt.subplot2grid(fig_dims, (1, 0))\n",
      "df_train['Sex'].value_counts().plot(kind='bar', \n",
      "                                    title='Gender Counts')\n",
      "plt.xticks(rotation=0)\n",
      "\n",
      "# Plot Embarked counts\n",
      "plt.subplot2grid(fig_dims, (1, 1))\n",
      "df_train['Embarked'].value_counts().plot(kind='bar', \n",
      "                                         title='Ports of Embarkation Counts')\n",
      "\n",
      "# Plot the Age histogram\n",
      "plt.subplot2grid(fig_dims, (2, 0))\n",
      "df_train['Age'].hist()\n",
      "plt.title('Age Histogram')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x10a4c4610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zumJ7h6d0hsdHcni+32+xh6ds3/dzeXp4w4YNTE5OAjAxMcFsfNVeERXljmzq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\nnJdKut7N5fHxcRI6D/gM8KWWdYuBFS05nouBZo7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zj1JKjCrdFxsszpEysyrqJEfqPOAz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\nbkiZmZmZdckNKTMzM7Mu/X+AP0780h2GHQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a1a5e90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next we'll explore various features to view their impact on survival rates."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Passenger Classes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From our exploratory data analysis in the previous section, we see there are three passenger classes: First, Second, and Third class.  We'll determine which proportion of passengers survived based on their passenger class."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate a cross tab of Pclass and Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pclass_xt = pd.crosstab(df_train['Pclass'], df_train['Survived'])\n",
      "pclass_xt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Survived</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pclass</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  80</td>\n",
        "      <td> 136</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  97</td>\n",
        "      <td>  87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 372</td>\n",
        "      <td> 119</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "Survived    0    1\n",
        "Pclass            \n",
        "1          80  136\n",
        "2          97   87\n",
        "3         372  119"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the cross tab:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Normalize the cross tab to sum to 1:\n",
      "pclass_xt_pct = pclass_xt.div(pclass_xt.sum(1).astype(float), axis=0)\n",
      "\n",
      "pclass_xt_pct.plot(kind='bar', \n",
      "                   stacked=True, \n",
      "                   title='Survival Rate by Passenger Classes')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x10a1a5bd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+FzNmZr55qJFJkiTNMtONeF1b/nl5ZV0CUf6p2ewmHPVSB/RwNECzXw/zfHRM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\nnk7w9PBiAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a164a90>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that passenger class seems to have a significant impact on whether a passenger survived.  Those in First Class the highest chance for survival."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Sex"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Gender might have also played a role in determining a passenger's survival rate.  We'll need to map Sex from a string to a number to prepare it for machine learning algorithms."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate a mapping of Sex from a string to a number representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sexes = sort(df_train['Sex'].unique())\n",
      "genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
      "genders_mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "{'female': 0, 'male': 1}"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Transform Sex from a string to a number representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train['Sex_Val'] = df_train['Sex'].map({'female': 0, 'male': 1}).astype(int)\n",
      "df_train.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "      <th>Sex_Val</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</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> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> female</td>\n",
        "      <td> 38</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</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</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</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</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</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</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> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "   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      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  \n",
        "0      0         A/5 21171   7.2500   NaN        S        1  \n",
        "1      0          PC 17599  71.2833   C85        C        0  \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S        0  \n",
        "3      0            113803  53.1000  C123        S        0  \n",
        "4      0            373450   8.0500   NaN        S        1  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a normalized cross tab for Sex_Val and Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sex_val_xt = pd.crosstab(df_train['Sex_Val'], df_train['Survived'])\n",
      "sex_val_xt_pct = sex_val_xt.div(sex_val_xt.sum(1).astype(float), axis=0)\n",
      "sex_val_xt_pct.plot(kind='bar', stacked=True, title='Survival Rate by Gender')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10a18b5d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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a2Zeg4dmXoOG5tmh4ri2D2PMkSZI0IoYnNVC37gIktVK37gLUEoYnSZKkEux5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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a48c150>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The majority of females survived, whereas the majority of males did not."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next we'll determine whether we can gain any insights on survival rate by looking at both Sex and Pclass."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Count males and females in each Pclass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Pclass:\n",
      "passenger_classes = sort(df_train['Pclass'].unique())\n",
      "\n",
      "for p_class in passenger_classes:\n",
      "    print 'M: ', p_class, len(df_train[(df_train['Sex'] == 'male') & \n",
      "                             (df_train['Pclass'] == p_class)])\n",
      "    print 'F: ', p_class, len(df_train[(df_train['Sex'] == 'female') & \n",
      "                             (df_train['Pclass'] == p_class)])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "M:  1 122\n",
        "F:  1 94\n",
        "M:  2 108\n",
        "F:  2 76\n",
        "M:  3 347\n",
        "F:  3 144\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot survival rate by Sex and Pclass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot survival rate by Sex\n",
      "females_df = df_train[df_train['Sex'] == 'female']\n",
      "females_xt = pd.crosstab(females_df['Pclass'], df_train['Survived'])\n",
      "females_xt_pct = females_xt.div(females_xt.sum(1).astype(float), axis=0)\n",
      "females_xt_pct.plot(kind='bar', \n",
      "                    stacked=True, \n",
      "                    title='Female Survival Rate by Passenger Class')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')\n",
      "\n",
      "# Plot survival rate by Pclass\n",
      "males_df = df_train[df_train['Sex'] == 'male']\n",
      "males_xt = pd.crosstab(males_df['Pclass'], df_train['Survived'])\n",
      "males_xt_pct = males_xt.div(males_xt.sum(1).astype(float), axis=0)\n",
      "males_xt_pct.plot(kind='bar', \n",
      "                  stacked=True, \n",
      "                  title='Male Survival Rate by Passenger Class')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Survival Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x10aef2e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3ZeaNq1pPk5SjAGoC81yNMFh3AKpo56rGf42IhRFxakTM7HhEkiRJk1Q79/Ea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\nggAuJN4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aaab150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NhX8laearMM8181WY58Ojn+b6LwJPAhZSv3tPAe4ZYEySJEkz0mqnGiPi4sxc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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ae98b90>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The vast majority of females in First and Second class survived.  Males in First class had the highest chance for survival."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Embarked"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Embarked column might be an important feature but it is missing a couple data points which might pose a problem for machine learning algorithms:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train[df_train['Embarked'].isnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "      <th>Sex_Val</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>61 </th>\n",
        "      <td>  62</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>                       Icard, Miss. Amelie</td>\n",
        "      <td> female</td>\n",
        "      <td> 38</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 113572</td>\n",
        "      <td> 80</td>\n",
        "      <td> B28</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>829</th>\n",
        "      <td> 830</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
        "      <td> female</td>\n",
        "      <td> 62</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 113572</td>\n",
        "      <td> 80</td>\n",
        "      <td> B28</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "     PassengerId  Survived  Pclass                                       Name  \\\n",
        "61            62         1       1                        Icard, Miss. Amelie   \n",
        "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
        "\n",
        "        Sex  Age  SibSp  Parch  Ticket  Fare Cabin Embarked  Sex_Val  \n",
        "61   female   38      0      0  113572    80   B28      NaN        0  \n",
        "829  female   62      0      0  113572    80   B28      NaN        0  "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare to map Embarked from a string to a number representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Embarked\n",
      "embarked_locs = sort(df_train['Embarked'].unique())\n",
      "\n",
      "embarked_locs_mapping = dict(zip(embarked_locs, \n",
      "                                 range(0, len(embarked_locs) + 1)))\n",
      "embarked_locs_mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "{nan: 0, 'C': 1, 'Q': 2, 'S': 3}"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Transform Embarked from a string to a number representation to prepare it for machine learning algorithms:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train['Embarked_Val'] = df_train['Embarked'] \\\n",
      "                               .map(embarked_locs_mapping) \\\n",
      "                               .astype(int)\n",
      "df_train.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "      <th>Sex_Val</th>\n",
        "      <th>Embarked_Val</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</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> 1</td>\n",
        "      <td> 3</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</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</td>\n",
        "      <td> 1</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</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</td>\n",
        "      <td> 3</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</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</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                          Allen, Mr. William Henry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35</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> 1</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "   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      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \n",
        "0      0         A/5 21171   7.2500   NaN        S        1             3  \n",
        "1      0          PC 17599  71.2833   C85        C        0             1  \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S        0             3  \n",
        "3      0            113803  53.1000  C123        S        0             3  \n",
        "4      0            373450   8.0500   NaN        S        1             3  "
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the histogram for Embarked_Val:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train['Embarked_Val'].hist(bins=len(embarked_locs), range=(0, 3))\n",
      "plt.title('Port of Embarkation Histogram')\n",
      "plt.xlabel('Port of Embarkation')\n",
      "plt.ylabel('Count')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kD6O+A5Dmzs+WPoz6DkAtWaBJkiQNjD1oWtfsE1F79qCpPT9b1J49aJIkSeuC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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aace410>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since the vast majority of passengers embarked in 'S': 3, we assign the missing values in Embarked to 'S': "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "if len(df_train[df_train['Embarked'].isnull()] > 0):\n",
      "    df_train.replace({'Embarked_Val' : \n",
      "                   { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
      "                   }\n",
      "               }, \n",
      "               inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Verify we do not have any more NaNs for Embarked_Val:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "embarked_locs = sort(df_train['Embarked_Val'].unique())\n",
      "embarked_locs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([1, 2, 3])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a normalized cross tab for Embarked_Val and Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "embarked_val_xt = pd.crosstab(df_train['Embarked_Val'], df_train['Survived'])\n",
      "embarked_val_xt_pct = \\\n",
      "    embarked_val_xt.div(embarked_val_xt.sum(1).astype(float), axis=0)\n",
      "embarked_val_xt_pct.plot(kind='bar', stacked=True)\n",
      "plt.title('Survival Rate by Port of Embarkation')\n",
      "plt.xlabel('Port of Embarkation')\n",
      "plt.ylabel('Survival Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x10b2f5150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BT5Zj3BwR/wnsBDw+QSzjnWpM4PuZ+QTwREQ8DIz2Yl3P6ivXJ3B2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\newAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b347cd0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It appears those that embarked in location 'C': 1 had the highest rate of survival.  We'll dig in some more to see why this might be the case.  Below we plot a graphs to determine gender and passenger class makeup for each port:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a grid of plots\n",
      "fig = plt.figure(figsize=fizsize_with_subplots) \n",
      "\n",
      "rows = 2\n",
      "cols = 3\n",
      "col_names = ('Sex_Val', 'Pclass')\n",
      "\n",
      "for portIdx in embarked_locs:\n",
      "    for colIdx in range(0, len(col_names)):\n",
      "        plt.subplot2grid((rows, cols), (colIdx, portIdx - 1))\n",
      "        df_train[df_train['Embarked_Val'] == portIdx][col_names[colIdx]] \\\n",
      "            .value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OrJYdwznRnhxosjz82rQhHoADBw7Q7/ePtr/t+BYHIyXqf+Nv/1dXm/kKcKLec9nOr3NL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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b2f5f90>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Age"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Age column seems like an important feature--unfortunately it is missing many values.  We'll need to fill in the missing values like we did with Embarked."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Filter to view missing Age values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train[df_train['Age'].isnull()][['Sex', 'Pclass', 'Age']].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Sex</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Age</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>   male</td>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "       Sex  Pclass  Age\n",
        "5     male       3  NaN\n",
        "17    male       2  NaN\n",
        "19  female       3  NaN\n",
        "26    male       3  NaN\n",
        "28  female       3  NaN"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Determine the Age typical for each passenger class by Sex_Val.  We'll use the median instead of the mean because the Age histogram seems to be right skewed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# To keep Age in tact, make a copy of it called AgeFill \n",
      "# that we will use to fill in the missing ages:\n",
      "df_train['AgeFill'] = df_train['Age']\n",
      "\n",
      "# Populate AgeFill\n",
      "df_train['AgeFill'] = df_train['AgeFill'] \\\n",
      "                        .groupby([df_train['Sex_Val'], df_train['Pclass']]) \\\n",
      "                        .apply(lambda x: x.fillna(x.median()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ensure AgeFill does not contain any missing values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df_train[df_train['AgeFill'].isnull()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a normalized cross tab for AgeFill and Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a grid of plots\n",
      "fig, axes = plt.subplots(2, 1, figsize=fizsize_with_subplots)\n",
      "\n",
      "# Histogram of AgeFill segmented by Survived\n",
      "df1 = df_train[df_train['Survived'] == 0]['Age']\n",
      "df2 = df_train[df_train['Survived'] == 1]['Age']\n",
      "max_age = max(df_train['AgeFill'])\n",
      "axes[0].hist([df1, df2], \n",
      "             bins=max_age / bin_size, \n",
      "             range=(1, max_age), \n",
      "             stacked=True)\n",
      "axes[0].legend(('Died', 'Survived'), loc='best')\n",
      "axes[0].set_title('Survivors by Age Groups Histogram')\n",
      "axes[0].set_xlabel('Age')\n",
      "axes[0].set_ylabel('Count')\n",
      "\n",
      "# Scatter plot Survived and AgeFill\n",
      "axes[1].scatter(df_train['Survived'], df_train['AgeFill'])\n",
      "axes[1].set_title('Survivors by Age Plot')\n",
      "axes[1].set_xlabel('Survived')\n",
      "axes[1].set_ylabel('Age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.text.Text at 0x10bbd1510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SVldEkJmxOsc0teUsIiYDBwKbRMRTwOeBfwWuiYgTgenAkQCZ+WBEXAM8CCwB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b9157d0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unfortunately, the graphs above do not seem to clearly show any insights.  We'll keep digging further."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot AgeFill density by Pclass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for pclass in passenger_classes:\n",
      "    df_train.AgeFill[df_train.Pclass == pclass].plot(kind='kde')\n",
      "plt.title('Age Density Plot by Passenger Class')\n",
      "plt.xlabel('Age')\n",
      "plt.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.legend.Legend at 0x10be02a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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89thjZ1y/d+9eOnfuzFlnnUWVKlXo0qULW7ZsyTn/3nvv0bBhw5xWwYkTJwKQ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\nd/2/TF+jQAH49FP4+2+zwES9evDcc7BunRmbJIQ7kaTNDblDP4LouGg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EEEIIFyBJmxBC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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aad7450>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When looking at AgeFill density by Pclass, we see the first class passengers were generally older then second class passengers, which in turn were older than third class passengers.  We've determined that first class passengers had a higher survival rate than second class passengers, which in turn had a higher survival rate than third class passengers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set up a grid of plots\n",
      "fig = plt.figure(figsize=fizsize_with_subplots) \n",
      "fig_dims = (3, 1)\n",
      "\n",
      "# Plot the AgeFill histogram for Survivors\n",
      "plt.subplot2grid(fig_dims, (0, 0))\n",
      "survived_df = df_train[df_train['Survived'] == 1]\n",
      "survived_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\n",
      "\n",
      "# Plot the AgeFill histogram for Females\n",
      "plt.subplot2grid(fig_dims, (1, 0))\n",
      "females_df = df_train[(df_train['Sex_Val'] == 0) & (df_train['Survived'] == 1)]\n",
      "females_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))\n",
      "\n",
      "# Plot the AgeFill histogram for first class passengers\n",
      "plt.subplot2grid(fig_dims, (2, 0))\n",
      "class1_df = df_train[(df_train['Pclass'] == 1) & (df_train['Survived'] == 1)]\n",
      "class1_df['AgeFill'].hist(bins=max_age / bin_size, range=(1, max_age))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10ce41190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f9/O3lvWmWuqpYT0YDKqqp6b1plrq6eP/v2kahsMhs5r7TUQj4lzgZ9rfFrwcuCszL4uI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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bddf690>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the first graph, we see that most survivors come from the 20's to 30's age ranges and might be explained by the following two graphs.  The second graph shows most females are within their 20's.  The third graph shows most first class passengers are within their 30's."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Family Size"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Feature enginering involves creating new features or modifying existing features which might be advantageous to a machine learning algorithm.\n",
      "\n",
      "Define a new feature FamilySize that is the sum of Parch (number of parents or children on board) and SibSp (number of siblings or spouses):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch']\n",
      "df_train.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "      <th>Sex_Val</th>\n",
        "      <th>Embarked_Val</th>\n",
        "      <th>AgeFill</th>\n",
        "      <th>FamilySize</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                           Braund, Mr. Owen Harris</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22</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> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> female</td>\n",
        "      <td> 38</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</td>\n",
        "      <td> 1</td>\n",
        "      <td> 38</td>\n",
        "      <td> 1</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</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</td>\n",
        "      <td> 3</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</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</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</td>\n",
        "      <td> 3</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1</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</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> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "   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      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\\n",
        "0      0         A/5 21171   7.2500   NaN        S        1             3   \n",
        "1      0          PC 17599  71.2833   C85        C        0             1   \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S        0             3   \n",
        "3      0            113803  53.1000  C123        S        0             3   \n",
        "4      0            373450   8.0500   NaN        S        1             3   \n",
        "\n",
        "   AgeFill  FamilySize  \n",
        "0       22           1  \n",
        "1       38           1  \n",
        "2       26           0  \n",
        "3       35           1  \n",
        "4       35           0  "
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a histogram of FamilySize:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train['FamilySize'].hist()\n",
      "plt.title('Family Size Histogram')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.text.Text at 0x10d31e0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10b358210>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a histogram of AgeFill segmented by Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Embarked and its maximum\n",
      "family_sizes = sort(df_train['FamilySize'].unique())\n",
      "family_size_max = max(family_sizes)\n",
      "\n",
      "df1 = df_train[df_train['Survived'] == 0]['FamilySize']\n",
      "df2 = df_train[df_train['Survived'] == 1]['FamilySize']\n",
      "plt.hist([df1, df2], \n",
      "         bins=family_size_max + 1, \n",
      "         range=(0, family_size_max), \n",
      "         stacked=True)\n",
      "plt.legend(('Died', 'Survived'), loc='best')\n",
      "plt.title('Survivors by Family Size')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.text.Text at 0x10d505710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GbW8Z6klNjS6gHU2NLkCStKNpbm6mubm52/t3N1ytioj9MnNlROwPrK62twCj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\nQn26Z51/swAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bd5f7d0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Based on the histograms, it is not immediately obvious what impact FamilySize has on survival.  The machine learning algorithms might benefit from this feature.\n",
      "\n",
      "Additional features we might want to engineer might be related to the Name column, for example honorrary or pedestrian titles might give clues and better predictive power for a male's survival."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Final Data Preparation for Machine Learning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Many machine learning algorithms do not work on strings and they usually require the data to be in an array, not a DataFrame.\n",
      "\n",
      "Show only the columns of type 'object' (strings):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train.dtypes[df_train.dtypes.map(lambda x: x == 'object')]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "Name        object\n",
        "Sex         object\n",
        "Ticket      object\n",
        "Cabin       object\n",
        "Embarked    object\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Drop the columns we won't use:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train = df_train.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], \n",
      "                         axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Drop the following columns:\n",
      "* The Age column since we will be using the AgeFill column instead.\n",
      "* The SibSp and Parch columns since we will be using FamilySize instead.\n",
      "* The PassengerId column since it won't be used as a feature."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_train = df_train.drop(['Age', 'SibSp', 'Parch', 'PassengerId'], axis=1)\n",
      "df_train.dtypes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "Survived          int64\n",
        "Pclass            int64\n",
        "Fare            float64\n",
        "Sex_Val           int64\n",
        "Embarked_Val      int64\n",
        "AgeFill         float64\n",
        "FamilySize        int64\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Convert the DataFrame to a numpy array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "train_data = df_train.values\n",
      "train_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "array([[  0.    ,   3.    ,   7.25  , ...,   3.    ,  22.    ,   1.    ],\n",
        "       [  1.    ,   1.    ,  71.2833, ...,   1.    ,  38.    ,   1.    ],\n",
        "       [  1.    ,   3.    ,   7.925 , ...,   3.    ,  26.    ,   0.    ],\n",
        "       ..., \n",
        "       [  0.    ,   3.    ,  23.45  , ...,   3.    ,  21.5   ,   3.    ],\n",
        "       [  1.    ,   1.    ,  30.    , ...,   1.    ,  26.    ,   0.    ],\n",
        "       [  0.    ,   3.    ,   7.75  , ...,   2.    ,  32.    ,   0.    ]])"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Data Munging Summary"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Below is a summary of the data munging we performed on our training data set.  We encapsulate this in a function since we'll need to do the same operations to our test set later."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def clean_data(df, drop_passenger_id):\n",
      "    \n",
      "    # Get the unique values of Sex\n",
      "    sexes = sort(df['Sex'].unique())\n",
      "    \n",
      "    # Generate a mapping of Sex from a string to a number representation    \n",
      "    genders_mapping = dict(zip(sexes, range(0, len(sexes) + 1)))\n",
      "\n",
      "    # Transform Sex from a string to a number representation\n",
      "    df['Sex_Val'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\n",
      "    \n",
      "    # Get the unique values of Embarked\n",
      "    embarked_locs = sort(df['Embarked'].unique())\n",
      "\n",
      "    # Generate a mapping of Embarked from a string to a number representation        \n",
      "    embarked_locs_mapping = dict(zip(embarked_locs, \n",
      "                                     range(0, len(embarked_locs) + 1)))\n",
      "    \n",
      "    # Transform Embarked from a string to a number representation\n",
      "    df['Embarked_Val'] = df['Embarked'].map(embarked_locs_mapping).astype(int)\n",
      "    \n",
      "    # Fill in missing values of Embarked\n",
      "    # Since the vast majority of passengers embarked in 'S': 3, \n",
      "    # we assign the missing values in Embarked to 'S':\n",
      "    if len(df[df['Embarked'].isnull()] > 0):\n",
      "        df.replace({'Embarked_Val' : \n",
      "                       { embarked_locs_mapping[nan] : embarked_locs_mapping['S'] \n",
      "                       }\n",
      "                   }, \n",
      "                   inplace=True)\n",
      "    \n",
      "    # Fill in missing values of Fare with the average Fare\n",
      "    if len(df[df['Fare'].isnull()] > 0):\n",
      "        avg_fare = df['Fare'].mean()\n",
      "        df.replace({ None: avg_fare }, inplace=True)\n",
      "    \n",
      "    # To keep Age in tact, make a copy of it called AgeFill \n",
      "    # that we will use to fill in the missing ages:\n",
      "    df['AgeFill'] = df['Age']\n",
      "\n",
      "    # Determine the Age typical for each passenger class by Sex_Val.  \n",
      "    # We'll use the median instead of the mean because the Age \n",
      "    # histogram seems to be right skewed.\n",
      "    df['AgeFill'] = df['AgeFill'] \\\n",
      "                        .groupby([df['Sex_Val'], df['Pclass']]) \\\n",
      "                        .apply(lambda x: x.fillna(x.median()))\n",
      "            \n",
      "    # Define a new feature FamilySize that is the sum of \n",
      "    # Parch (number of parents or children on board) and \n",
      "    # SibSp (number of siblings or spouses):\n",
      "    df['FamilySize'] = df['SibSp'] + df['Parch']\n",
      "    \n",
      "    # Drop the columns we won't use:\n",
      "    df = df.drop(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
      "    \n",
      "    # Drop the Age column since we will be using the AgeFill column instead.\n",
      "    # Drop the SibSp and Parch columns since we will be using FamilySize.\n",
      "    # Drop the PassengerId column since it won't be used as a feature.\n",
      "    df = df.drop(['Age', 'SibSp', 'Parch'], axis=1)\n",
      "    \n",
      "    if drop_passenger_id:\n",
      "        df = df.drop(['PassengerId'], axis=1)\n",
      "    \n",
      "    return df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Random Forest: Training"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create the random forest object:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.ensemble import RandomForestClassifier\n",
      "\n",
      "clf = RandomForestClassifier(n_estimators=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fit the training data and create the decision trees:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Training data features, skip the first column 'Survived'\n",
      "train_features = train_data[:, 1:]\n",
      "\n",
      "# 'Survived' column values\n",
      "train_target = train_data[:, 0]\n",
      "\n",
      "# Fit the model to our training data\n",
      "clf = clf.fit(train_features, train_target)\n",
      "score = clf.score(train_features, train_target)\n",
      "\"Mean accuracy of Random Forest: {0}\".format(score)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "'Mean accuracy of Random Forest: 0.980920314254'"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Random Forest: Predicting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read the test data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_test = pd.read_csv('../data/titanic/test.csv')\n",
      "df_test.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 892</td>\n",
        "      <td> 3</td>\n",
        "      <td>                             Kelly, Mr. James</td>\n",
        "      <td>   male</td>\n",
        "      <td> 34.5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  330911</td>\n",
        "      <td>  7.8292</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 893</td>\n",
        "      <td> 3</td>\n",
        "      <td>             Wilkes, Mrs. James (Ellen Needs)</td>\n",
        "      <td> female</td>\n",
        "      <td> 47.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  363272</td>\n",
        "      <td>  7.0000</td>\n",
        "      <td> NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 894</td>\n",
        "      <td> 2</td>\n",
        "      <td>                    Myles, Mr. Thomas Francis</td>\n",
        "      <td>   male</td>\n",
        "      <td> 62.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  240276</td>\n",
        "      <td>  9.6875</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Q</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 895</td>\n",
        "      <td> 3</td>\n",
        "      <td>                             Wirz, Mr. Albert</td>\n",
        "      <td>   male</td>\n",
        "      <td> 27.0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  315154</td>\n",
        "      <td>  8.6625</td>\n",
        "      <td> NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 896</td>\n",
        "      <td> 3</td>\n",
        "      <td> Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
        "      <td> female</td>\n",
        "      <td> 22.0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3101298</td>\n",
        "      <td> 12.2875</td>\n",
        "      <td> NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "   PassengerId  Pclass                                          Name     Sex  \\\n",
        "0          892       3                              Kelly, Mr. James    male   \n",
        "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
        "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
        "3          895       3                              Wirz, Mr. Albert    male   \n",
        "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
        "\n",
        "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
        "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
        "1  47.0      1      0   363272   7.0000   NaN        S  \n",
        "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
        "3  27.0      0      0   315154   8.6625   NaN        S  \n",
        "4  22.0      1      1  3101298  12.2875   NaN        S  "
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note the test data does not contain the column 'Survived', we'll use our trained model to predict these values."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Data munge the test set and convert it to a numpy array\n",
      "df_test = clean_data(df_test, drop_passenger_id=False)\n",
      "test_data = df_test.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Take the decision trees and run it on the test data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the test data features, skipping the first column 'PassengerId'\n",
      "test_x = test_data[:, 1:]\n",
      "\n",
      "# Predict the Survival values for the test data\n",
      "test_y = clf.predict(test_x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Random Forest: Prepare for Kaggle Submission\n",
      "\n",
      "Create a DataFrame by combining the index from the test data with the output of predictions, then write the results to the output:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_test['Survived'] = test_y\n",
      "df_test[['PassengerId', 'Survived']] \\\n",
      "    .to_csv('../data/titanic/results-rf.csv', index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Evaluate Model Accuracy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Submitting to Kaggle will give you an accuracy score.  It would be helpful to get an idea of accuracy without submitting to Kaggle.\n",
      "\n",
      "We'll split our training data, 80% will go to \"train\" and 20% will go to \"test\":"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import metrics\n",
      "from sklearn.cross_validation import train_test_split\n",
      "\n",
      "# Split 80-20 train vs test data\n",
      "train_x, test_x, train_y, test_y = train_test_split(train_features, \n",
      "                                                    train_target, \n",
      "                                                    test_size=0.20, \n",
      "                                                    random_state=0)\n",
      "print (train_features.shape, train_target.shape)\n",
      "print (train_x.shape, train_y.shape)\n",
      "print (test_x.shape, test_y.shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "((891, 6), (891,))\n",
        "((712, 6), (712,))\n",
        "((179, 6), (179,))\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use the new training data to fit the model, predict, and get the accuracy score:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = clf.fit(train_x, train_y)\n",
      "predict_y = clf.predict(test_x)\n",
      "\n",
      "from sklearn.metrics import accuracy_score\n",
      "print (\"Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy = 0.84\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "View the Confusion Matrix:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "|      | condition True | condition false|\n",
      "|------|----------------|---------------|\n",
      "|prediction true|True Positive|False positive|\n",
      "|Prediction False|False Negative|True Negative|"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image \n",
      "Image(filename='../data/confusion_matrix.png') "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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5bZs0c+63+zuF4wi0m4CznoWlK1baASPTcDNa+477H9xvH76aPUdm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Ed5+91fjx9abWfXXXzXR3hqV+tQfY3lVPucDlBIIdF2jXNoA6Jx01Hiv9ovjbW8VydV3dZn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\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MLItvXnJxWU9olM8s16oW28402qH7lPMVAgQI/NIEku4rBAgQIECAAAECBAgQIECAAAEC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\nGt3F8D/NAAAAAElFTkSuQmCC\n",
       "prompt_number": 45,
       "text": [
        "<IPython.core.display.Image at 0x10d040b10>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the model score and confusion matrix:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model_score = clf.score(test_x, test_y)\n",
      "print (\"Model Score %.2f \\n\" % (model_score))\n",
      "\n",
      "confusion_matrix = metrics.confusion_matrix(test_y,predict_y)\n",
      "print (\"Confusion Matrix \",confusion_matrix)\n",
      "\n",
      "print (\"          Predicted\")\n",
      "print (\"         |  0  |  1  |\")\n",
      "print (\"         |-----|-----|\")\n",
      "print (\"       0 | %3d | %3d |\" % (confusion_matrix[0, 0],\n",
      "                                   confusion_matrix[0, 1]))\n",
      "print (\"Actual   |-----|-----|\")\n",
      "print (\"       1 | %3d | %3d |\" % (confusion_matrix[1, 0],\n",
      "                                   confusion_matrix[1, 1]))\n",
      "print (\"         |-----|-----|\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Model Score 0.84 \n",
        "\n",
        "('Confusion Matrix ', array([[100,  10],\n",
        "       [ 18,  51]]))\n",
        "          Predicted\n",
        "         |  0  |  1  |\n",
        "         |-----|-----|\n",
        "       0 | 100 |  10 |\n",
        "Actual   |-----|-----|\n",
        "       1 |  18 |  51 |\n",
        "         |-----|-----|\n"
       ]
      }
     ],
     "prompt_number": 46
    }
   ],
   "metadata": {}
  }
 ]
}