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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Kaggle Machine Learning Competition: Predicting Survivors on the Titanic\n",
      "\n",
      "* 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 (Gender)\n",
      "* Feature: Embarked\n",
      "* Feature: Age\n",
      "* Feature: Family Size\n",
      "* Random Forest: Training\n",
      "* Random Forest: Predicting\n",
      "* Support Vector Machine: Training\n",
      "* Support Vector Machine: Predicting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Competition Site"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "https://www.kaggle.com/c/titanic-gettingStarted"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Description"
     ]
    },
    {
     "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 our matplotlib figures\n",
      "plt.rc('figure', figsize=(10, 5))"
     ],
     "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 = pd.read_csv('../data/titanic/train.csv')\n",
      "df.head(3)"
     ],
     "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",
        "  </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",
        "\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",
        "\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  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.tail(3)"
     ],
     "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>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",
        "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",
        "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.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": [
      "Get some basic information on the DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.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": [
      "Note 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.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=(10, 10)) \n",
      "\n",
      "# Plot death and survival counts\n",
      "ax0 = plt.subplot2grid((3, 2), (0, 0))\n",
      "df['Survived'].value_counts().plot(kind='bar', title='Death and Survival Counts')\n",
      "plt.xticks((0, 1), ('Died', 'Survived'), rotation=0)\n",
      "\n",
      "# Plot Pclass counts\n",
      "ax1 = plt.subplot2grid((3, 2), (0, 1))\n",
      "df['Pclass'].value_counts().plot(kind='bar', title='Passenger Class Counts')\n",
      "#plt.xticks((1, 2, 3), ('First', 'Second', 'Third'), rotation=0)\n",
      "\n",
      "# Plot Sex counts\n",
      "ax2 = plt.subplot2grid((3, 2), (1, 0))\n",
      "df['Sex'].value_counts().plot(kind='bar', title='Gender Counts')\n",
      "plt.xticks((0, 1), ('Female', 'Male'), rotation=0)\n",
      "\n",
      "# Plot Embarked counts\n",
      "ax3 = plt.subplot2grid((3, 2), (1, 1))\n",
      "df['Embarked'].value_counts().plot(kind='bar', title='Ports of Embarkation Counts')\n",
      "\n",
      "# Plot the Age histogram\n",
      "ax4 = plt.subplot2grid((3, 2), (2, 0))\n",
      "df['Age'].hist()\n",
      "plt.title('Age Histogram')\n",
      "plt.xlabel('Age')\n",
      "plt.ylabel('Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x10a5d70d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vkNS7hMIhpNNRPwu8Jr/2GtKF2CDdNuZYSVtK2g94EumGxYPldvZx0kknFY/BD6/7UT9K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\nLBdPQXYfpYQYZcXxFGQdeQrSzKpoJpehaAK3AX8AbE3bxVhnS9JiSbd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/Gvg34EbGp1sW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1084d9c50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Passenger Classes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a histogram of Pclass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values of Pclass:\n",
      "passenger_classes = sort(df['Pclass'].unique())\n",
      "\n",
      "df['Pclass'].hist(bins=len(passenger_classes))\n",
      "plt.title('Passenger Class Histogram')\n",
      "plt.xlabel('Passenger Class')\n",
      "plt.ylabel('Count')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LUKChcHXuAOiMOncAdEqdOwAKR4EGAADQMhRoKFyVOwA6o8odAJ1S5Q6AwlGg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\nAAC0zP8HY4b11pIaXGQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a28bb90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate a cross tab of Pclass and Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pclass_xt = pd.crosstab(df['Pclass'], df['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": 9,
       "text": [
        "Survived    0    1\n",
        "Pclass            \n",
        "1          80  136\n",
        "2          97   87\n",
        "3         372  119"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "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', stacked=True)\n",
      "plt.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": 10,
       "text": [
        "<matplotlib.text.Text at 0x10aaa1cd0>"
       ]
      },
      {
       "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 0x10aa7bfd0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that passenger class seems to have a significant impact on whether a passenger survived."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Sex (Gender)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll need to map Sex from a string to a number to prepare it for machine learning algorithms."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare to map Sex from a string to a number representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "genders = sort(df['Sex'].unique())\n",
      "genders_mapping = dict(zip(genders, range(0, len(genders) + 1)))\n",
      "genders_mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "{'female': 0, 'male': 1}"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Transform Sex from a string to a number representation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Sex_Val'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\n",
      "df.head(3)"
     ],
     "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",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       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",
        "\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  "
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a histogram of Sex_Val:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the unique values for gender\n",
      "genders = sort(df['Sex_Val'].unique())\n",
      "\n",
      "df['Sex_Val'].hist(bins=len(genders))\n",
      "plt.title('Gender Histogram')\n",
      "plt.xlabel('Gender')\n",
      "plt.ylabel('Count')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mXesAmknXOoAGZoEmSZI0MvagSZI9aJLmxh40SZKkpTC6Ai3JKUkuTXJZkme1\nzqPN1rUOoKl1rQNoJl3rAJpJ1zqABjaqAi3JjYE/AU4BTgAem+RubVNpc13cOoCm5tgtNsdvsTl+\nh5pRFWjAScDlVbWzqq4BXgs8tHEmbaqrWwfQ1By7xeb4LTbH71AztgLtGOCKNeu7+m2SJEmHjMNa\nB1hnQ6dQHXnkQ+adQ3PyjW98mCOO+GDrGJrCMo/dNdfs4pvfbJ1i3na2DqCZ7GwdQAMbW4H2WeD2\na9Zvz2QW7Xq++tX/O1ggbb6vfvWzrSNoSss/dgd9JvyCOat1AM3E8TuUjOo6aEkOAz4B/DTwOeBC\n4LFV9Q9Ng0mSJA1oVDNoVXVtkqcC7wRuDPy5xZkkSTrUjGoGTZIkSeM7i/M6G7lgbZI/6u//SJJ7\nDZ1R+3ag8Uvyi/24fTTJe5Pco0VO3dBGLxad5D5Jrk3y8CHzaf82+Nm5kuTDST6WpBs4ovZhA5+b\nRyU5N8nF/dhtaxBTe5HkFUl2J/n7/TzmoGqWURZoG7lgbZIHA3eqqjsDTwJeNnhQ7dUGLzj8j8BP\nVNU9gOcBfzpsSu3NRi8W3T/uhcC5LH9n/cLY4GfnFuAlwEOq6oeARwweVDewwf/3ngp8uKpOBFaA\nP+h7t9XeK5mM3V5NU7OMskBjYxesPZX+lJaqugDYkmTrsDG1Dwccv6p6f1V9pV+9ALjdwBm1dxu9\nWPRvAH8JfHHIcDqgjYzfLwBvqKpdAFX1pYEzau82MnafB47sl48ErqqqawfMqH2oqvcAX97PQw66\nZhlrgbaRC9bu7TH+kh+Hg73g8GnA2+eaSBt1wLFLcgyTXxyrfwHayDoeG/l/787ALZO8O8lFSX55\nsHTan42M3cuBuyf5HPAR4DcHyqbZHXTNMtap0Y1+4K8/tOIvinHY8Dgk+UngCcD95xdHB2EjY3cm\nsL2qKknwEOeYbGT8vge4N5PLGR0BvD/JB6rqsrkm04FsZOx+B7i4qlaS/CCwI8k9q+prc86mzXFQ\nNctYC7SNXLB2/WNu129Texu64HB/YsDLgVOqan9TwxrORsbuR4DXTmozjgJ+Jsk1VfWWYSJqPzYy\nflcAX6qqbwLfTPK3wD0BC7S2NjJ29wN+F6CqPpXk08BdgIsGSahZHHTNMtZDnBcBd05ybJLDgUcD\n6z/83wL8CkCSHweurqrdw8bUPhxw/JLcAXgj8EtVdXmDjNq7A45dVd2xqo6rquOY9KH9usXZaGzk\ns/OvgAdAGV2RAAAC6klEQVQkuXGSI4AfAy4ZOKduaCNjdynwIIC+f+kuTE640vgddM0yyhm0fV2w\nNsmv9vf/r6p6e5IHJ7kc+Bfg8Q0ja42NjB/wbOAWwMv6mZhrquqkVpk1scGx00ht8LPz0iTnAh8F\nvgu8vKos0Brb4P97zwdemeQjTCZYnllV/9wstK6T5GzggcBRSa4AzmDSTjB1zeKFaiVJkkZmrIc4\nJUmSDlkWaJIkSSNjgSZJkjQyFmiSJEkjY4EmSZI0MhZokiRJI2OBJmnhJdma5DVJPtV/v+T7kjxs\nE/a7kuStm5FRkg6GBZqkhdZ/H+ibga6qfrCqfhR4DAf4IuI5ZRnlxb8lLR4LNEmL7qeAb1XVn65u\nqKrPVNWf9F9n9PtJLkzykSRPgutmxrokr0/yD0levfrcJKf02z4I/Nya7TdL8ookFyT5UJJT++3b\nkrwlyfnAjsHetaSl5l97khbd3YEP7eO+05h8591JSW4C/F2S8/r7TgROAD4PvDfJ/fr9/Cnwk/2X\nUb8OWP26lf8MnF9VT0iyBbggybv6++4F/HBVXb3p707SIckCTdKiu9731SV5CXB/4NvAPwH3SPKI\n/u4jgTsB1wAXVtXn+udcDBwHfAP4dFV9qn/8q4En9csnAw9J8ox+/SbAHfrX32FxJmkzWaBJWnQf\nB35+daWqnpLkVsBFTAq0p1bV9Q49JlkBvrVm03eYfB6u/3LirFt/eFVdtm5fP8bky48ladPYgyZp\noVXVXwM3TfJrazbfrP/5TuDJq837SY5PcsS+dgVcChyb5I79tseuuf+dwNNWV5Lca3VxxrcgSTfg\nDJqkZfAw4MVJngl8kcmM1jOBv2Ry6PJD/dmeX2DS+F/ccLaMqvpWfyLB25J8A3gPe4q95wFnJvko\nkz9u/xE4dV/7kqRZpMrPFUmSpDHxEKckSdLIWKBJkiSNjAWaJEnSyFigSZIkjYwFmiRJ0shYoEmS\nJI2MBZokSdLIWKBJkiSNzP8HaGQZ6TcL/kUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a28bfd0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "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['Sex_Val'], df['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)\n",
      "plt.title('Survival Rate by Gender')\n",
      "plt.xlabel('Gender')\n",
      "plt.ylabel('Survival Rate')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x10b02e1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BMXipWezxklSJVt0FqCEMXpIkSQNi8FKz2OMlqRLtugtQQxi8JEmSBsTgpWax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\n8JIkSRqQ/w9uomDn+CN0rAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10acfd990>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The majority of females survived, whereas the majority of males did not."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Count males and females in each Pclass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for p_class in passenger_classes:\n",
      "    print 'M: ', p_class, len(df[(df['Sex'] == 'male') & (df['Pclass'] == p_class)])\n",
      "    print 'F: ', p_class, len(df[(df['Sex'] == 'female') & (df['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": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Embarked"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Embarked column might be an important feature but is missing a couple data points:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df['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": 16,
       "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": 16
    },
    {
     "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_locations = sort(df['Embarked'].unique())\n",
      "\n",
      "embarked_locations_mapping = dict(zip(embarked_locations, \n",
      "                                      range(0, len(embarked_locations) + 1)))\n",
      "embarked_locations_mapping"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "{nan: 0, 'C': 1, 'Q': 2, 'S': 3}"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "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['Embarked_Val'] = df['Embarked'].map(embarked_locations_mapping).astype(int)\n",
      "df.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": 18,
       "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": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Count the number of passengers by Embarked:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for embarked in range(0, len(embarked_locations)):\n",
      "    print embarked, len(df[df['Embarked_Val'] == embarked])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 2\n",
        "1 168\n",
        "2 77\n",
        "3 644\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the histogram for Embarked_Val:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Embarked_Val'].hist(bins=len(embarked_locations), 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 0x10a19f490>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since the vast majority of passengers embarked in 'S': 3, we assign the two missing values in Embarked to 'S': "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.replace({'Embarked_Val' : { embarked_locations_mapping[nan] : embarked_locations_mapping['S'] }}, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Verify we do not have any more NaNs for Embarked_Val:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sort(df['Embarked_Val'].unique())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([1, 2, 3])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "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['Embarked_Val'], df['Survived'])\n",
      "embarked_val_xt_pct = 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": 23,
       "text": [
        "<matplotlib.text.Text at 0x10a54f690>"
       ]
      },
      {
       "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 0x10aae21d0>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It appears those that embarked in location 'C': 1 had the highest rate of survival."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Feature: Age"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Age column seems like an important feature but is missing many values.  \n",
      "\n",
      "Get the first 10 rows of the Age column:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Age'][:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "0    22\n",
        "1    38\n",
        "2    26\n",
        "3    35\n",
        "4    35\n",
        "5   NaN\n",
        "6    54\n",
        "7     2\n",
        "8    27\n",
        "9    14\n",
        "Name: Age, dtype: float64"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Display the Age histogram:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Age'].hist()\n",
      "plt.title('Age Histogram')\n",
      "plt.xlabel('Age')\n",
      "plt.ylabel('Count')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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N6QAq1ZQOoDq+h7qZl27mpZt5mQ4LM0mSpErYY6a5s8esz+wxk6SNssdMkiSpxyzMBsB1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\nTJIkqRL/H1/o9UCol6axAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a5ac450>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Filter the DataFrame to the columns we'll be looking at with Age:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['Sex', 'Pclass', 'Age']].head(3)"
     ],
     "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>0</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "      <td> 38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "      Sex  Pclass  Age\n",
        "0    male       3   22\n",
        "1  female       1   38\n",
        "2  female       3   26"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Determine the max age:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_age = max(df['Age'])\n",
      "max_age"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "80.0"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Filter the DataFrame to see more information on seniors Age > 60:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df['Age'] > 60][['Sex', 'Pclass', 'Age']]"
     ],
     "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>33 </th>\n",
        "      <td>   male</td>\n",
        "      <td> 2</td>\n",
        "      <td> 66.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54 </th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96 </th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 71.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>116</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td> 70.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>170</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 61.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>252</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 62.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>275</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "      <td> 63.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>280</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td> 65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>326</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td> 61.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>438</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 64.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>456</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>483</th>\n",
        "      <td> female</td>\n",
        "      <td> 3</td>\n",
        "      <td> 63.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>493</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 71.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>545</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 64.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>555</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 62.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>570</th>\n",
        "      <td>   male</td>\n",
        "      <td> 2</td>\n",
        "      <td> 62.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>625</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 61.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>630</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>672</th>\n",
        "      <td>   male</td>\n",
        "      <td> 2</td>\n",
        "      <td> 70.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>745</th>\n",
        "      <td>   male</td>\n",
        "      <td> 1</td>\n",
        "      <td> 70.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>829</th>\n",
        "      <td> female</td>\n",
        "      <td> 1</td>\n",
        "      <td> 62.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>851</th>\n",
        "      <td>   male</td>\n",
        "      <td> 3</td>\n",
        "      <td> 74.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "        Sex  Pclass   Age\n",
        "33     male       2  66.0\n",
        "54     male       1  65.0\n",
        "96     male       1  71.0\n",
        "116    male       3  70.5\n",
        "170    male       1  61.0\n",
        "252    male       1  62.0\n",
        "275  female       1  63.0\n",
        "280    male       3  65.0\n",
        "326    male       3  61.0\n",
        "438    male       1  64.0\n",
        "456    male       1  65.0\n",
        "483  female       3  63.0\n",
        "493    male       1  71.0\n",
        "545    male       1  64.0\n",
        "555    male       1  62.0\n",
        "570    male       2  62.0\n",
        "625    male       1  61.0\n",
        "630    male       1  80.0\n",
        "672    male       2  70.0\n",
        "745    male       1  70.0\n",
        "829  female       1  62.0\n",
        "851    male       3  74.0"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see that most are men, most are 1st class, and most perished.  Age seems like a good feature to predict whether a passenger survived, but we'll need to do some cleaning, as there are missing values.  Missing values pose a problem for machine learning algorithms.\n",
      "\n",
      "Filter to view missing Age values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df['Age'].isnull()][['Sex', 'Pclass', 'Age']].head(3)"
     ],
     "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",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "       Sex  Pclass  Age\n",
        "5     male       3  NaN\n",
        "17    male       2  NaN\n",
        "19  female       3  NaN"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Determine the Age typical for each passenger class by Sex_Val.  We'll use the median as the Age histogram seems to be right skewed:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "median_ages = np.zeros((len(genders), len(passenger_classes)))\n",
      "median_ages"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([[ 0.,  0.,  0.],\n",
        "       [ 0.,  0.,  0.]])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fill in our median ages array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for genderIdx in range(0, len(genders)):\n",
      "    for pclassIdx in range(0, len(passenger_classes)):\n",
      "        median_age = df[(df['Sex_Val'] == genderIdx) & \\\n",
      "                        (df['Pclass'] == pclassIdx + 1)]\n",
      "        median_ages[genderIdx, pclassIdx] = \\\n",
      "            median_age['Age'].dropna().median()\n",
      "\n",
      "        df.loc[(df['Age'].isnull()) & \n",
      "               (df['Sex_Val'] == genderIdx) & \n",
      "               (df['Pclass'] == pclassIdx + 1), \\\n",
      "               'AgeFill'] = median_age\n",
      "        \n",
      "median_ages"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "array([[ 35. ,  28. ,  21.5],\n",
        "       [ 40. ,  30. ,  25. ]])"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make a copy of Age:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['AgeFill'] = df['Age']\n",
      "df.head(3)"
     ],
     "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",
        "    </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",
        "    </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",
        "    </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",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       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",
        "\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",
        "\n",
        "   AgeFill  \n",
        "0       22  \n",
        "1       38  \n",
        "2       26  "
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Populate AgeFill based on our median_ages array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for genderIdx in range(0, len(genders)):\n",
      "    for pclassIdx in range(0, len(passenger_classes)):\n",
      "        df.loc[(df['Age'].isnull()) & \n",
      "               (df['Sex_Val'] == genderIdx) & \n",
      "               (df['Pclass'] == pclassIdx + 1), \\\n",
      "               'AgeFill'] = median_ages[genderIdx, pclassIdx]\n",
      "        \n",
      "df[df['Age'].isnull()].head(3)"
     ],
     "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",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  6</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>             Moran, Mr. James</td>\n",
        "      <td>   male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 330877</td>\n",
        "      <td>  8.4583</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Q</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 25.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 18</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> Williams, Mr. Charles Eugene</td>\n",
        "      <td>   male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 244373</td>\n",
        "      <td> 13.0000</td>\n",
        "      <td> NaN</td>\n",
        "      <td> S</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 30.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 20</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>      Masselmani, Mrs. Fatima</td>\n",
        "      <td> female</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   2649</td>\n",
        "      <td>  7.2250</td>\n",
        "      <td> NaN</td>\n",
        "      <td> C</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 21.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "    PassengerId  Survived  Pclass                          Name     Sex  Age  \\\n",
        "5             6         0       3              Moran, Mr. James    male  NaN   \n",
        "17           18         1       2  Williams, Mr. Charles Eugene    male  NaN   \n",
        "19           20         1       3       Masselmani, Mrs. Fatima  female  NaN   \n",
        "\n",
        "    SibSp  Parch  Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\\n",
        "5       0      0  330877   8.4583   NaN        Q        1             2   \n",
        "17      0      0  244373  13.0000   NaN        S        1             3   \n",
        "19      0      0    2649   7.2250   NaN        C        0             1   \n",
        "\n",
        "    AgeFill  \n",
        "5      25.0  \n",
        "17     30.0  \n",
        "19     21.5  "
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ensure AgeFill does not contain any missing values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df[df['AgeFill'].isnull()])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a feature that records whether Age was originally missing:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['AgeIsNull'] = pd.isnull(df['Age']).astype(int)\n",
      "df.head(3)"
     ],
     "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>AgeIsNull</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> 0</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> 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",
        "      <td> 3</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       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",
        "\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",
        "\n",
        "   AgeFill  AgeIsNull  \n",
        "0       22          0  \n",
        "1       38          0  \n",
        "2       26          0  "
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot a normalized cross tab for AgeFill and Survived:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(3, 1, figsize=(10, 20))\n",
      "\n",
      "# Histogram of AgeFill segmented by Survived\n",
      "df1 = df[df['Survived'] == 0]['Age']\n",
      "df2 = df[df['Survived'] == 1]['Age']\n",
      "axes[0].hist([df1, df2], bins=max_age / 10, range=(1, max_age), stacked=True)\n",
      "axes[0].legend(('Died', 'Survived'), loc='best')\n",
      "axes[0].set_title('Survivors by Age Groups')\n",
      "axes[0].set_xlabel('Age')\n",
      "axes[0].set_ylabel('Count')\n",
      "\n",
      "# Plot a normalized cross tab for AgeFill and Survived\n",
      "age_fill_xt = pd.crosstab(df['AgeFill'], df['Survived'])\n",
      "age_fill_xt_pct = age_fill_xt.div(age_fill_xt.sum(1).astype(float), axis=0)\n",
      "age_fill_xt_pct.plot(ax=axes[1], stacked=True)\n",
      "axes[1].legend(loc='best')\n",
      "axes[1].set_title('Survival Rate by Age')\n",
      "axes[1].set_xlabel('Age')\n",
      "axes[1].set_ylabel('Count')\n",
      "\n",
      "# Scatter plot Survived and AgeFil\n",
      "axes[2].scatter(df['Survived'], df['AgeFill'])\n",
      "axes[2].set_title('Age by Survivors')\n",
      "axes[2].set_xlabel('Age')\n",
      "axes[2].set_ylabel('Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.text.Text at 0x10b5b8510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ACGcAAAABIZwBAAAEpF/RDUDB2otuAAAASCOctTwvugEZsaIbAABATRjWBAAA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VgicWbJsWN64qyszJmPzlme6TRCbAgg/CYqzqouadadPOIsDyGsrVkFUniNj6sfJ+fMP1atEu5+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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a5e2850>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot AgeFill density by Pclass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for pclass in passenger_classes:\n",
      "    df.AgeFill[df.Pclass == pclass].plot(kind='kde')\n",
      "\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": 37,
       "text": [
        "<matplotlib.legend.Legend at 0x10d2e3ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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89thjZ1y/d+9eOnfuzFlnnUWVKlXo0qULW7ZsyTn/3nvv0bBhw5xWwYkTJwKQ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b35b490>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "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."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}