Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic: Machine Learning from Disaster\n",
    "## Kaggle Competition \n",
    "\n",
    "### Competition Description\n",
    "\n",
    "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": [
    "## Data Ingestion\n",
    "Let's start out by loading in the data. The Kaggle competition supplies both the training and test data in two .csv files. Download the data and simply point the Panda's read_csv to the file location. After loading in the data, I like to look at the first few lines to get an idea of the schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns #visualizations\n",
    "import scipy as scipy\n",
    "\n",
    "trainpath = \"/Users/jreinhart/Desktop/Data Analysis Github/Titanic Kaggle/train.csv\"\n",
    "testpath = \"/Users/jreinhart/Desktop/Data Analysis Github/Titanic Kaggle/test.csv\"\n",
    "\n",
    "traindf = pd.read_csv(trainpath)\n",
    "testdf = pd.read_csv(testpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, some of the feature names could be confusing if there wasn't any other documentation. Thankfully the Kaggle competition contained a data dictionary that explains what each feature represents.\n",
    "\n",
    "#### FEATURE DESCRIPTIONS:\n",
    "\n",
    "|Feature      |Description                                |\n",
    "|:-----------:|:-----------------------------------------:|\n",
    "|survived     |Survival (0 = No; 1 = Yes)                 |\n",
    "|pclass       |Passenger Class (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(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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Exploration\n",
    "\n",
    "After loading up a new dataset it is often helpful to do some initial exploration of the data to get an idea of how \"clean\" the data is and note any obvious patterns. One of the easiest ways to do this in Pandas is through the .describe method. The describe method generates various summary statistics, excluding any missing/null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply running the describe method we can learn quite a bit about our training and test sets. First, we can tell that our training set is about twice as large as our test set. Second, by looking at the \"counts\" we know that there are missing values that will need to be addressed in order to run any classifier algorithms. We can also get an idea of the average age, fare, and family size of the passengers. \n",
    "\n",
    "Let's create some initial visualizations of our training set to see if any obvious patterns of survival emerge:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106464050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patagonia_colors = [\"#222343\", \"#bbca42\", \"#7abbdb\", \"#eca935\", \"#ec7869\", \"#332212\"]\n",
    "current_palette = sns.color_palette(patagonia_colors)\n",
    "sns.palplot(current_palette)\n",
    "sns.set_palette(current_palette)\n",
    "sns.set_context(\"talk\")\n",
    "sns.set_style(\"white\")\n",
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our plots formatted, let's get started by first comparing how many passengers in our training data survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AoEg0AgBQJBoBACgSjQAAFIlGAACKRCMAAEUvy2jcv39/brzxxkydOjVTpkzJ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b7c6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", data=traindf, color=\"#222343\")\n",
    "sns.despine(bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected there are more people who died than survived in the training data. Let's look a little deeper to see if there are any features that increase your chances of surviving. Even if all you know about the Titanic disaster was learned from the movie Titanic, you would know they tried to save women and children first. Let's take a look at the number of survivors by gender and age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oqOmS8ZEjR+R2uxUdHd2i/TscDlmW1eI8ERERxhnQwMDAFm8PAADQEZ2zJG7evLnFO5s8\nefI556SkpCgnJ0cJCQkKCwvTihUrNHLkSLlcLmPu//7v/8rlcikqKkqnTp3Siy++qJMnT2rkyJEt\nzgQAAADvnbMkLl682OP3hoYGNTQ0yOFwSJIsy5K/v78CAgJaVBLT0tJUVVWl6dOnq66uTiNGjFBW\nVpYkKT8/X0uWLGl65E1xcbEWLVqkiooKXXLJJYqNjdXvfvc7XXrppV4fKAAAAFrOYXlx7fadd97R\nypUr9fDDDysuLk6SVFhYqKysLC1YsOCCOcNXUlKisWPHaseOHbr88st9HQcAbFFYWKgpU+9RSEjo\nuScDsNXp09XatDGnqV+1B15948qyZcv02GOP6eqrr1ZQUJCCgoJ09dVX69FHH9VTTz3VVhkBAABg\nM69K4pEjRxQeHm6Mh4eHq6SkpNVCAQAAwLe8KokxMTF6+umn5Xa7m8bcbrd++ctftvjuZAAAALR/\nXn1385IlSzR79mxdf/31+slPfiJJOnTokIKDg7VmzZo2CQgAAAD7eVUSBwwYoLfffltbtmzRF198\nIUmaNm2akpKSvH6eIgAAANovr0qiJIWEhCglJaUtsgAAAKCd8OoziZK0e/duzZkzRzfffLPKysok\nSRs2bNB7773X6uEAAADgG16VxO3bt2v+/PlyuVwqKSlRfX29pH88YPuFF15ok4AAAACwn1clcc2a\nNVq6dKkWL14sf3//pvEhQ4aouLi41cMBAADAN7wqiYcPH9bw4cONcafTqaqqqlYLBQAAAN/yqiR2\n69ZNX331lTH+ySefyOVytVooAAAA+JZXJTE5OVnLli3T0aNH5XA4VFtbq7/85S9avny5pk2b1lYZ\nAQAAYDOvHoEzf/58lZWVafz48bIsS0lJSZKkyZMna9asWW0SEAAAAPbzqiQGBAQoKytL8+fP1/79\n+9XY2KgBAwaod+/ebZUPAAAAPuBVSXzjjTc0ZswYRUVFKSoqqq0yAQAAwMe8KokZGRkKCQnRhAkT\nNGXKFA0dOrStcgEAAMCHvLpx5d1339VDDz2kw4cP67bbbtO4ceOUnZ2to0ePtlU+AAAA+IBXJdHp\ndOqWW27R2rVr9fbbb2vatGnavn27xo8fr9tuu62tMgIAAMBmXn938z9FRkZq9uzZeuyxxxQdHa2P\nPvqoNXMBAADAh7z6TOI/HTx4UK+++qq2bt2qkydP6tprr1VaWlprZwMAAICPeFUSX3zxRW3ZskWf\nffaZ+vfvr1mzZikpKUldu3Ztq3wAAADwAa9K4tq1a5WcnKxVq1apX79+bZUJAAAAPuZVSdy5c6cc\nDkdbZQEAAEA7cc6S+Mknn2jAgAHy9/fXp59++r1zBw8e3GrBAAAA4DvnLIkpKSl699131a1bN6Wk\npMjhcMiyLGOew+HQgQMH2iQkAAAA7HXOkrhjx46mG1N27NjR5oEAAADge+csiZGRkU0/X3rppQoI\nOK+n5gAAAOAC4tXDtEeMGKFf/OIX+vjjj9sqDwAAANoBr0rigw8+qIMHD+o//uM/NGHCBD333HMq\nKSlpq2wAAADwEa9K4k9/+lO9/PLLevPNNzVp0iRt2bJF48aN04wZM5Sbm9tWGQEAAGCz8/ru5qio\nKN133316/fXX9Yc//EFut1uLFy9u7WwAAADwkfO+C+XQoUN69dVXlZ+fr+PHj+vaa69tzVwAAADw\nIa9K4okTJ7R161Zt2bJFBw4cUExMjGbOnKmkpCR17969rTICAADAZl6VxOuvv17du3dXUlKSli1b\npiuuuKKtcgEAAMCHWlwSLcvS0qVLlZycrJCQkLbMBAAAAB9r8Y0rlmXpiSeeUFlZWVvmAQAAQDvQ\n4pLo5+enyMhI1dTUtGUeAAAAtANePQJn3rx5WrFihY4fP95WeQAAANAOeHXjynPPPafy8nKNGjVK\nPXv2VKdOnTxe3759e6uGAwAAgG94VRInTpzYVjkAAADQjnhVEufNm/eDF2xsbNTy5cu1efNm1dbW\nasSIEXriiScUERHxvdutX79ev/jFL/TAAw9ozpw5PzgHAAAAzu68vpbvh1i9erV27typvLw8FRQU\nSJIyMjK+d5vS0lK99NJLio6OtiMiAABAh+fVmcTBgwfL4XCc9fWPP/74nPvIzc3VvHnzFBkZKUla\nuHChxo0bp7KyMvXq1avZbR577DEtWLBA69ev9yYuAAAAzpNXJXHp0qUeJbG+vl779u3TW2+9pXvv\nvfec21dXV6u0tFSxsbFNY1FRUXI6nSouLm62JL7yyiu65JJLdNNNN51XSayoqFBlZaXHWHl5udf7\nAQAA6Ei8KolTp041xm655RbFxsbqww8/POf2brdbDodDoaGhHuNdunRp9vmLpaWlWr16tTZs2OBN\nTA/r1q1Tdnb2eW8PAADQEXlVEs/mmmuuUWZm5jnnde7cWZZlqbq62mO8qqpKTqfTmP/zn/9cc+fO\nVY8ePc47W2pqqpKSkjzGysvLNXPmzPPeJwAAwMWuVUriu+++a5wdbE5oaKhcLpeKiooUExMjSTpy\n5IjcbnezN6W8++672r9/v1atWiVJqqmp0b59+/TOO+9o3bp1LcoWERFh3DkdGBjYom0BAAA6Kq9K\n4j333OPxu2VZ+vrrr/X555/rgQceaNE+UlJSlJOTo4SEBIWFhWnFihUaOXKkXC6XMfefdz//0/33\n369hw4bprrvu8iY2AAAAvORVSezevbvH7w6HQ/3799ejjz6qxMTEFu0jLS1NVVVVmj59uurq6jRi\nxAhlZWVJkvLz87VkyRIVFhZKki699FKPbYOCguR0OtW1a1dvYgMAAMBLDsuyLF+HsFtJSYnGjh2r\nHTt26PLLL/d1HACwRWFhoaZMvUchIef+eBAAe50+Xa1NG3MUFxfn6yhNftDDtPfu3as33njDeMQM\nAAAALmwtuty8bt06VVVVeTwLce7cudq5c6ckKSwsTOvXr1e/fv3aJiUAAABs1aIziZs3b/a4sWTH\njh3atWuXsrKylJeXp969e2v16tVtFhIAAAD2alFJPHr0qPr379/0e0FBgUaNGqVJkyZp4MCBWrBg\ngfbu3dtmIQEAAGCvFpXE06dPezzs+uOPP9bw4cObfu/du7e++eab1k8HAAAAn2hRSbzssst08OBB\nSf/4dpRDhw5pyJAhTa+fPHmy2W9MAQAAwIWpRTeu3HTTTXryySd1/Phx7dq1S5dddpkGDx7c9Pq+\nffvUp0+fNgsJAAAAe7WoJM6dO1fl5eVasWKFevTooeXLl8vP7/9OQm7btk3XX399m4UEAACAvVpU\nEoODg7Vs2bKzvr527dpWCwQAAADf+0EP0wYAAMDF6ZwlMSsrS99++22LdrZr1y699tprPzgUAAAA\nfOucl5tPnTqlsWPHavTo0Ro7dqwGDx6sSy+9VP7+/qqpqdGhQ4e0Z88e5efnq66uTllZWXbkBgAA\nQBs6Z0lcsmSJUlNT9fLLL2vJkiWqqqqSJPn7+6uhoUGSNGjQIN15552aNGmSAgJa9DFHAAAAtGMt\nanT9+vXT4sWL9fOf/1wHDx5USUmJzpw5o65du6p///4KDw9v65wAAACwkVen/RwOh2JiYhQTE9NW\neQAAANAOnNe14bKyMp04cUKWZXmM/+sDtgEAAHDh8qokHjhwQA8++KC+/PJLoyA6HA4dOHCgVcMB\nAADAN7wqiYsXL5bL5dKTTz6pnj17yuFwtFUuAAAA+JBXJfHzzz/Xxo0b1bdv37bKAwAAgHbAq29c\n+fGPf9z0CBwAAABcvLwqiY8//rhWrlypzz//vK3yAAAAoB3w6nLzXXfdpfr6ek2aNEn+/v7y9/f3\neP3jjz9u1XAAAADwDa9K4hNPPNFWOQAAANCOeFUSp0yZ0lY5AAAA0I6c9xctHz9+XHV1dR5jLpfr\nBwcCAACA73lVEmtqavTkk09q27ZtRkGUxMO0AQAALhJe3d28fPlyffrpp/rVr36l4OBgZWVlacGC\nBerZs6dWrVrVVhkBAABgM6/OJBYUFCgzM1NXXXWVHA6HBg8erOTkZPXs2VMbN27UTTfd1FY5AQAA\nYCOvziRWVlYqKipKkuR0OpserB0fH6+9e/e2fjoAAAD4hFcl0eVyqby8XJLUu3dvFRQUSJL27t2r\nzp07t346AAAA+IRXl5vHjRunPXv2KC4uTrfffrseeOAB5ebm6sSJE5o9e3ZbZQQAAIDNvCqJCxYs\naPp5/Pjx+sMf/qDCwkL16dNHo0aNau1sAAAA8JHzfk6iJF155ZW68sorWysLAAAA2gmvPpMoSbt3\n79acOXN08803q6ysTJK0YcMGvffee60eDgAAAL7hVUncvn275s+fL5fLpZKSEtXX10uSGhoa9MIL\nL7RJQAAAANjPq5K4Zs0aLV26VIsXL5a/v3/T+JAhQ1RcXNzq4QAAAOAbXpXEw4cPa/jw4cb4vz4z\nEQAAABc+r0pit27d9NVXXxnjn3zyiVwuV6uFAgAAgG95VRKTk5O1bNkyHT16VA6HQ7W1tfrLX/6i\n5cuXa9q0aW2VEQAAADbz6hE48+fPV1lZmcaPHy/LspSUlCTLsjRlyhTNmjWrrTICAADAZl6VxICA\nAGVlZWn+/Pnav3+/GhsbNWDAAPXu3bvF+2hsbNTy5cu1efNm1dbWasSIEXriiScUERFhzN27d6/+\n+7//W1999ZUaGxsVFRWluXPnaty4cd7EBgAAgJe8fph2QUGB3nvvPZ04cUKWZWnHjh1Nr61cufKc\n269evVo7d+5UXl6ewsLCtGjRImVkZCgnJ8eY27dvXz377LPq1auXpH+UxlmzZmnjxo3q27evt9EB\nAADQQl6VxKefflpr1qxR//791aNHDzkcDq8XzM3N1bx58xQZGSlJWrhwocaNG6eysrKmMvhPXbt2\nbfrZsqym/z9y5AglEQAAoA15VRI3bNigzMxMTZo06bwWq66uVmlpqWJjY5vGoqKi5HQ6VVxcbJTE\nfxo+fLhOnTql+vp6DR8+XCNGjGjxmhUVFaqsrPQYKy8vP6/8AAAAHYVXJdHPz0+DBw8+78Xcbrcc\nDodCQ0M9xrt06aKampqzbvfBBx+orq5Ou3bt0uHDhxUQ0PLY69atU3Z29nlnBgAA6Ii8Kom33nqr\n8vLy9NBDD53XYp07d5ZlWaqurvYYr6qqktPp/N5tAwMDNXbsWKWlpalLly5KSUlp0ZqpqalKSkry\nGCsvL9fMmTO9yg4AANCReFUS77vvPqWlpWnSpEmKjo42zug99dRT37t9aGioXC6XioqKFBMTI0k6\ncuSI3G63oqOjW5ShoaFBX375ZYszR0REGHdOBwYGtnh7AACAjsirh2n/6le/0u7du+VwOHTy5Ekd\nP37c438tkZKSopycHJWUlKi6ulorVqzQyJEjm/3GljfffFOfffaZGhoaVFtbqw0bNuj999/XyJEj\nvYkNAAAAL3l1JvHll1/WsmXLNHny5PNeMC0tTVVVVZo+fbrq6uo0YsQIZWVlSZLy8/O1ZMkSFRYW\nSpKOHz+ulStX6vjx4woMDFSfPn20atUqJSYmnvf6AAAAODevH6Y9dOjQH7Sgn5+fMjIylJGRYbyW\nnJys5OTkpt9nzJihGTNm/KD1AAAA4D2vLjenpKRo48aNbZUFAAAA7YRXZxIrKir02muv6d1331VM\nTIxx48rSpUtbMxsAAAB8xKuSePjw4aa7kv/+9797vHY+374CAACA9smrkrh27dq2ygEAAIB2xKvP\nJAIAAKBjoCQCAADAQEkEAACAgZIIAAAAAyURAAAABkoiAAAADJREAAAAGCiJAAAAMFASAQAAYKAk\nAgAAwEBJBAAAgIGSCAAAAAMlEQAAAAZKIgAAAAyURAAAABgoiQAAADBQEgEAAGCgJAIAAMBASQQA\nAICBkggAAAADJREAAAAGSiIAAAAMlEQAAAAYKIkAAAAwUBIBAABgCPB1AKAlLMtSQ+NpX8do1/z9\nQuRwOHwdAwBwkaAkot07XvkXFf/91zpT+7Wvo7RrwUE9FdM7XT3CE30dBQBwEeByM9q9A18+TUFs\ngTO1X+vAl6t8HQMAcJGgJAIAAMBASUS71//HCxQc1NPXMdq94KCe6v/jB30dAwBwkeAziWj3eoRf\no+5hie0oZX9OAAAS40lEQVTixpX6hlN65+NbPMauvTJXAf6dfJTo/3DjCgCgNVEScUFwOBztoog1\nJ8C/U7vNBgDA+eJyMwAAAAyURAAAABgoiQAAADDYXhIbGxuVmZmpxMRExcfHKz09XRUVFc3OLSgo\n0B133KGrr75aV111lVJTU7V3716bEwMAAHQ8tpfE1atXa+fOncrLy1NBQYEkKSMjo9m5VVVV+tnP\nfqa3335b7733niZOnKh77rlHx44dszMyAABAh2N7SczNzVVaWpoiIyPldDq1cOFC7d69W2VlZcbc\n5ORk3XDDDXI6nfLz89Ott96qzp0769NPP7U7NgAAQIdia0msrq5WaWmpYmNjm8aioqLkdDpVXFx8\nzu0PHjyoyspKXXHFFW0ZEwAAoMOz9TmJbrdbDodDoaGhHuNdunRRTU3N9277zTffKD09XXfffbd+\n9KMftXjNiooKVVZWeoyVl5e3PDQAAEAHZGtJ7Ny5syzLUnV1tcd4VVWVnE7nWbc7duyY7r77bo0c\nOVILFizwas1169YpOzv7vPICAAB0VLaWxNDQULlcLhUVFSkmJkaSdOTIEbndbkVHRze7TUlJie68\n806NHz9eCxcu9HrN1NRUJSUleYyVl5dr5syZXu8LAACgo7D9a/lSUlKUk5OjhIQEhYWFacWKFRo5\ncqRcLpcx94svvtBdd92lqVOn6v777z+v9SIiIhQREeExFhgYeF77AgAA6Chsv7s5LS1No0eP1vTp\n0zVq1ChJUlZWliQpPz9fcXFxTXNfeOEFff311/r973+voUOHaujQoYqLi9PWrVvtjg0AANChOCzL\nsnwdwm4lJSUaO3asduzYocsvv9yWNevq6nh0z0XAss6oovFxj7EIv/8nhyPYR4nQWgYNGnTRX2Uo\nLCzUlKn3KCQk9NyTAdjq9OlqbdqY43GyzNdsv9zcUX366aeamDRDQUGX+DoKfoDgEOm5teEeY7fO\nmKczp30UCK2itvY7bdv6crt6cwYAX6Mk2igo6BL+BX+BC27mhGFIcKgc9kcBAKBN2f6ZRAAAALR/\nlEQAAAAYKIkAAAAwUBIBAABgoCQCAADAQEkEAACAgZIIAAAAAyURAAAABkoiAAAADJREAAAAGPha\nPlwwmvtKPLsFNZOhuTFfOHPG1wkAABcTSiLavYFDpZ/e7qeu3dvnNyQve9bf1xEkSSdPWPrj7xu1\n76++TgIAuBhwuRnt3m13td+C2J507e7QbXfzVxoA0Dr4LwoAAAAMlES0e+t/26iTJyxfx2j3Tp6w\ntP7FRl/HAABcJPhMItq9fR9J+z5qbBc3rrRn3LgCAGhNlERcMChBAADYh8vNAAAAMFASAQAAYKAk\nAgAAwEBJBAAAgIGSCAAAAAMlEQAAAAZKIgAAAAyURAAAABgoiQAAADBQEgEAAGCgJAIAAMBASQQA\nAICBkggAAAADJREAAAAGSiIAAAAMlEQAAAAYKIkAAAAwUBIBAABgoCQCAADAQEkEAACAgZIIAAAA\ng+0lsbGxUZmZmUpMTFR8fLzS09NVUVHR7Nxjx47p3nvv1ZgxYxQTE6P8/Hyb0wIAAHRMtpfE1atX\na+fOncrLy1NBQYEkKSMjo9m5fn5+uvbaa7Vy5Ur16tXLzpgAAAAdWoDdC+bm5mrevHmKjIyUJC1c\nuFDjxo1TWVmZUQR79Oih2267TZLkcDjsjgoAANBh2Xomsbq6WqWlpYqNjW0ai4qKktPpVHFxsZ1R\nAAAA8D1sPZPodrvlcDgUGhrqMd6lSxfV1NS0yZoVFRWqrKz0GCsvL2+TtQAAAC4WtpbEzp07y7Is\nVVdXe4xXVVXJ6XS2yZrr1q1TdnZ2m+wbAADgYmVrSQwNDZXL5VJRUZFiYmIkSUeOHJHb7VZ0dHSb\nrJmamqqkpCSPsfLycs2cObNN1gMAALgY2H7jSkpKinJycpSQkKCwsDCtWLFCI0eOlMvlanZ+bW2t\nLMuSJNXX16u2tlb+/v7y9/dv0XoRERGKiIjwGAsMDPxhBwEAAHCRs/0ROGlpaRo9erSmT5+uUaNG\nSZKysrIkSfn5+YqLi/OYP3jwYA0ZMkRlZWVatGiRrrzySj3//PN2xwYAAOhQbD+T6Ofnp4yMjGaf\njZicnKzk5GSPMe56BgAAsB9fywcAAAADJREAAAAGSiIAAAAMlEQAAAAYKIkAAAAwUBIBAABgoCQC\nAADAQEkEAACAgZIIAAAAAyURAAAABkoiAAAADJREAAAAGCiJAAAAMFASAQAAYKAkAgAAwEBJBAAA\ngIGSCAAAAAMlEQAAAAZKIgAAAAyURAAAABgoiQAAADBQEgEAAGCgJAIAAMBASQQAAICBkggAAAAD\nJREAAAAGSiIAAAAMlEQAAAAYKIkAAAAwUBIBAABgoCQCAADAQEkEAACAgZIIAAAAAyURAAAABkoi\nAAAADJREAAAAGCiJAAAAMFASAQAAYKAkAgAAwEBJBAAAgMH2ktjY2KjMzEwlJiYqPj5e6enpqqio\nOOv8Xbt2KSkpSVdeeaWSk5P17rvv2pgWAACgY7K9JK5evVo7d+5UXl6eCgoKJEkZGRnNzj169KjS\n09M1Z84cffjhh0pLS9O8efNUWlpqZ2QAAIAOx/aSmJubq7S0NEVGRsrpdGrhwoXavXu3ysrKjLmb\nN2/WwIEDlZSUpICAACUnJys2NlabNm2yOzYAAECHYmtJrK6uVmlpqWJjY5vGoqKi5HQ6VVxcbMwv\nLi7WgAEDPMZiY2N18ODBNs8KAADQkQXYuZjb7ZbD4VBoaKjHeJcuXVRTU9Ps/ObmfvHFFy1es6Ki\nQpWVlR5j5eXlXqRuPbW13/lkXQDfryP93exIxwpcSNrj301bS2Lnzp1lWZaqq6s9xquqquR0Opud\n39K5Z7Nu3TplZ2efX+BWFBcXp7LSA76OgVZSUVGhdevWKTU1VREREb6OA7QI70MXF96H0NZsLYmh\noaFyuVwqKipSTEyMJOnIkSNyu92Kjo425sfExGjPnj0eY0VFRbrmmmtavGZqaqqSkpI8xhoaGnTm\nzBlddtll53EUgFRZWans7GwlJSXx5gzAJ3gfQluztSRKUkpKinJycpSQkKCwsDCtWLFCI0eOlMvl\nMuZOnjxZv/3tb7V9+3aNGzdOr732moqKirR8+fIWrxcREcFfHgAAAC/ZfndzWlqaRo8erenTp2vU\nqFGSpKysLElSfn6+4uLimuZGRUXp17/+tZ577jkNHz5cOTk5eu6555otlAAAAGg9DsuyLF+HAC40\nhw8f1o033qjXX39dffr08XUcAB0Q70Noa3wtHwAAAAyUROA8hIeHa968eQoPD/d1FAAdFO9DaGtc\nbgYAAICBM4kAAAAwUBIBAABgoCQCAADAQEkEAACAgZIIAAAAAyURAAAABkoiAAAADJREAAAAGCiJ\nQCv56quvFBMTo2PHjvk6CoB26Ntvv9Xdd9+tYcOGadq0abavv2nTJo0fP972dXHhCvB1AOBi4nA4\nfB0BQDv1yiuv6NSpU/rggw989l7BexS8wZlEAABscPToUfXr14+ihgsGJREd2pgxY/T888/r9ttv\n19ChQzVp0iQdPHhQ27Zt0/jx4zV8+HA9/vjjamxslCQ9+uijGjVqlOLi4pSUlKStW7d+7/7ffvtt\nTZ06VcOHD9fEiROVn59vx2EBaGfmzJmjzZs3a9OmTYqLi1N2drY+//xz3X333UpMTNSYMWO0atUq\nNTQ0SPq/j69s3rxZEydO1NChQzV79mxVVVVp5cqVuuaaa3Tttdfq5Zdfblrj2LFjmjVrlhITEzV8\n+HDNmDFD+/fvP2umhoYG/eY3v9GECROUkJCg2267Tfv27WvzPwtcQCygAxs9erQ1fvx4629/+5tV\nX19vPfTQQ9YNN9xg/fznP7dOnz5tlZaWWomJiVZ+fr5lWZaVl5dnffvtt1ZjY6O1bds2a8CAAdah\nQ4csy7KskpISKyYmxiovL7csy7Leeecd66qrrrI+/PBDy7Is65NPPrESEhKsDz74wDcHC8CnHnnk\nEevxxx+3LMuyvvnmGyshIcHasGGDVV9fbx07dsyaNm2a9eyzz1qW9Y/3k+joaGvu3LlWVVWVVVlZ\nad18883W+PHjrQ0bNlgNDQ1WQUGBNWDAAKusrMyyLMsqLS21/ud//sc6c+aMdebMGevJJ5+0Ro8e\nbdXX11uWZVkbN260xo8f35Rn1apVVkpKilVSUmI1NjZaeXl51lVXXWVVVVXZ/CeD9ooziejwfvrT\nn6pPnz7y9/dXUlKSSkpK9OCDDyo4OFi9evVSQkJC07+up02bpi5dusjhcOjmm29WdHS09uzZ0+x+\n165dq9tvv11xcXGSpEGDBik5OVmvvvqqbccGoH3atGmT+vfvr1tuuUX+/v7q2bOn0tLStHnzZo95\n9913n0JDQxUWFqZRo0YpKChIt9xyi/z8/HTdddepS5cuKioqkiT16tVLo0ePVlBQkIKCgpSenq7S\n0lJ9+eWXzWZYu3atFi5cqMjISDkcDk2bNk09evTQn//85zY+elwouHEFHV6PHj2afu7UqZP8/f0V\nHh7uMeZ2u2VZlp555hm99tpr+uabbyRJp06d0smTJ5vdb0lJid5//3299NJLkiTLstTY2Kjhw4e3\n3cEAuCB89dVX+vDDD5WQkNA01tjYKMuymn53OBzq3r170++dOnXyeL+SpJCQELndbklSRUWFli1b\npj179qi6uloOh0MOh0MVFRXG+idPntR3332nuXPnNn1G0rIs1dfXq7y8vFWPFRcuSiLQQlu3blVe\nXp5eeukl9evXT9I/ziz+65v6v3K5XJo6daruuusuO2MCuAC4XC6NGDFCv/nNb1ptnytXrtSJEyeU\nl5enbt26ye12Kz4+vtn3qK5du+qSSy7R7373Ow0cOLDVMuDiwuVmoIVqamoUGBio8PBw1dfXKy8v\nTwcPHvSY869vxnfccYdeeukl7d27V42NjaqtrdX+/fv5YDgATZ48Wfv27dOf/vQn1dbWyrIsHT16\nVLt3726ac7Z/gJ6N2+1WSEiIQkND5Xa7tXz58u+9k/r2229XZmam/v73vzdt/8477+j48ePnd1C4\n6FAS0aF58yiKqVOnatCgQRo3bpxGjRqlv/3tbxo2bNhZ9zdixAj913/9l5YvX66rr75a1113nZYt\nW6ZTp061Wn4AF6bu3bvr97//vd5++22NGTNGCQkJmj9/vkpKSprmtOT96V/npKen65tvvtFVV12l\nyZMnKz4+Xv7+/mfdNj09XWPHjtW9996rYcOG6cYbb9Qf//jHpqc5AA7L23+qAAAA4KLHmUQAAAAY\nKIkAAAAwUBIBAABgoCQCAADAQEkEAACAgZIIAAAAAyURAAAABkoiAAAADHx3MwC0kjNnzuj555/X\na6+9pvLycoWEhCgqKkqTJ09Wamqqr+MBgFcoiQDQSpYsWaIPPvhAjz32mKKjo1VTU6MDBw6otLTU\n19EAwGtcbgaAVrJjxw6lpaVpzJgxioyMVHR0tCZPnqx7773XY96rr76qSZMmafDgwRo/fryeeeYZ\n1dXVSZKOHDmiuLg4vfLKK03z9+/fr0GDBun111+39XgAdGyURABoJT169NCuXbtUVVV11jl5eXla\ntmyZ5syZo+3bt+uJJ57Qtm3b9Mwzz0iSfvSjH2nx4sV66qmndOjQIX333Xd68MEHNWXKFN144412\nHQoAyGFZluXrEABwMSgsLNTChQtVXl6un/zkJ7ryyit1/fXXa+zYsU1zxowZo/vuu0/Tpk1rGnv9\n9df16KOP6qOPPmoay8jIUFFRkWJiYnTgwAFt3LhRwcHBth4PgI6NkggAraixsVGffPKJ/vrXv+qD\nDz7Qn//8Z1133XV6/vnndfLkSV1zzTXq1KmTxzaWZam2tla7du1S9+7dJUlut1sTJ07UN998oz/9\n6U+64oorfHE4ADowblwBgFbk5+enIUOGaMiQIZo5c6a2bNmihx9+WHv37lWfPn0kSYsXL9awYcOM\nbbt27dr08xdffKETJ05Iko4ePUpJBGA7SiIAtKG+ffvKsiydPHlSw4YN02WXXaYvv/xSU6ZMOes2\nbrdbDz30kKZPny6Xy6VFixZpy5YtuvTSS21MDqCj81+6dOlSX4cAgIvBz372M9XW1kqS6uvrtW/f\nPmVmZur06dN65JFHFBISovDwcP3yl79UQECAwsPDVVFRoT179mjTpk0aMWKEJOnxxx/XyZMn9etf\n/1oJCQnavXu33nrrre8tlgDQ2vhMIgC0kpycHBUUFOjw4cOqqalRt27dNGzYMM2ePVv9+vVrmvfW\nW28pJydHn332mYKCgtS7d29NnjxZM2bMUH5+vh5//HHl5uY2XWI+duyYJk+erJkzZ2r27Nm+OjwA\nHQwlEQAAAAaekwgAAAADJREAAAAGSiIAAAAMlEQAAAAYKIkAAAAwUBIBAABgoCQCAADAQEkEAACA\n4f8DX1L4E7mW2vAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c7f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\", y=\"Survived\", capsize=.25, color=\"#222343\", errcolor=\"#bbca42\", data=traindf)\n",
    "sns.despine(bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the percentage of male and female passengers in the training set that survived. We can see that being female drastically increases a passenger's survival chances (~75% of females live vs. ~20% of males live). Let's see how age affects survival within the two sexs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eXgEPPPCsEjMY9OzZ/TEfr/+ad97ZqMR9fDzYv+8TJk95hEOHTijxXj07sm7d\nCn46OZ6SskwlHuQ7CW1pb/r1m6Ya97pu7VOUlpYx7/6nlJher2P3ro/YsGErb6/9rxL38nLjwP7P\nmDFjGT/+dEyJd+vWjg/ef57b8cPOn7nvvseVY51Oy84fPiQgoGInp5KSUt5c8wkHDhwlpHF9Fi6c\nhodHHeXxsjIj3XtMICmpYsmpmTPH3HLHqf+ltLSMNWs+Zf+BX2jUKJCFD03Fy8v9/12fEOKfS77y\nihpjNBYSnVjxD9dsLuNqwnpJRGtAfn6hKgkF+PnnE9WUhkOHTlgsAH/w4AkSE1JUsZKSUn799TSH\nDqrriomJJyoqVpWEAhw8dILcgiuqJBQgI+c4p4/YWLZ56ITFzkUlpaUc/fU0Bw+p24yNTeLq1ThV\nEgpw6OAJLlyIUiWhAAd/Po6ri5MqCb1RvvJi8aWlZRw9epqDlc4zMTGVqKhYDh/+zaLfeYXRqiT0\nxnleOemuSkJvlC8tLVXFysqMHPnllMV5Jienc+VKjEX80KHq38/qHDx4XHVsNJo4cuSkKhF9ZeX7\nvPfeJgBOn47kSlQsmzauVh6/ejVOlYSW13v7fbnZa699wLr/lO+2derURS5fjuHLL978Q3UKIf6Z\nZIyoqDEmcxkmc5kqVmYqrKa0uB12djYEBfmpYk2bNqy2fNOmjSxizZo2tHiORqMhNLShRXn36/uj\nN6y0nmTTpg2xt/VHq1UPCXC0s6zjRvmq2mwa2siifB1XZ/z8vGncOMiijvr1/bCzs6kUt6zjRrxZ\nFfHy81T3xcXFEX9/X0KbNLBo087GD51Wvcaok10jmjarus2mob/vNXd0tMff39ei701Dq38/q1PV\neVZub+fOn1XHx4+fJS29IsGuV8/bYtLXrT5bv8fOXQdVxydPXiA5Oa2a0kKIfzMZIypqjE5rTUFR\nHHmFV5VY/bqTcLYPqcVe/TNoNBpatgzh8JHfyM7OpVmzRrz80sNKApGRkU1sbBJ16jij0WjKJweZ\nzZw8dRGNRsvkSXczdeoImjZtRFxcEpcvx+DoZMejy+4jPLwtYWGhnDp1kYSEFHx8PHnl5YcJDPSj\nZasQfjlyiqysHEJDG/LSSw/jVscDO5u6ZOScwmQuxt25E40D5uJX1w+dTsvJk+fRaDRMmDCEGdNH\n0TS0EQkJKURGRuPgYMfSR2bRvXt7WoeFcvrUReLjk/H2dufll5dQv349WrVqwuEjJ8nKyiEkpD6v\nvLwEb28WaHiwAAAgAElEQVQP6gf58cvRkxQWFtOzRwdWrJhLQEBdrAx6Tvx2Ho2mfMLQzJljCA1t\nSFJyKhcvRmNvb8uSJTPp3asjYa1COXv2ErFxSXh5ufHSSw/TsIE/rVo14Zejp8jIyCakcRCvvLwE\nd3cP7G39ycw5idFUhJtTOxoH3I+Pty82NtacOHEes9nMmDEDuW/2WEJDG5KSnM6Fi1exs7Nh8aIZ\n9O3bhVYtm3Du3GViYxPx8nLjxRcWExwcSFhYKEeOlLfZqFEAK19Zipubyy0/B0UlqZSUZioz7Bs3\nDiI1LZMLF6KwsbHhoYemMqB/N6B8//rIS9H89tt5YmOTlDrq1HFh7px70OnKr0Po9XqCgwM5fOQk\n+fmFdO4cxlNPLrBI/CuLjo6nsLBYtbKC2Wwm8lI0J09e4Nq1BCXu4uLI3LkTLGbn5+bmER0dp3xu\na1JJSanymTMY5OafEH9V8tspalSg1zxOHi+ioDgazzodqdduaG136R8jLS2TtLTyK1kpKRlkZefi\nD6z7z+e89toHlJaW0aRJAz54/3nc3FyIjUuiqKgYgNi4JEpLy9DqShh3byJ9hhvR6/OpXzcZKN9Z\nJ/H6tpSZmdmkppa3k56epVw9S0lJJ/v6OqFbvkpj5cp8zBozQYHJfPB+IZ6edsTGJlJYWN5mXGx5\nmyUlpcTFJWEymcjPLyAuPqmKNnNITkkHypPqG7fzU1MzyMzKASC0VSrPrM6luNiEm2sSVjaFmM32\nXItNVPaLv3GepaVlxF5LxGg0kp9fSNz1tULz8gqIvz48ISsrl5Tk8ja96maz7LkMcnLNODll4O5V\n3mZxSRqlZeXnXFSSjNFYgEFfPuGpoKDw+nkmUlpahslkIjYuCaPRSEFBEbFx5W3a2hcwe1Eyo2eY\nsbHOJjiw/Jwz0rNJv/7apqVlkllp6EFlF2JeJy7lO8BEHafWtGz4FCUlGuJikygrM1JWVkjc9YQz\nPj6ZqdOWcuXKNfR6PS4uTmRl5eDgYMfTT823SMySk9PJuv46JySkkJdXUG1SXFRUzH1znuDAgaNo\nNBrGjLmLZ595kISEFKZNW8qlyzEYDDe1aW/HU08uwNraSlXP5s3f8/gTb1BUVIy/vy/vv/8cQYF+\nVbZ5u06cOMes2StIT8/CycmBVa8to3v39jVStxCiZskVUVGj7r//Gd575xA/7ctlx/bz2NhY07ZN\ns//9RHFLJpOJSZMeJjOzPFnIzy/k8uUYwsPbMnPWo8ri7WlpmZSUloLZzEsvvas8PyoqloCAuti5\nHCUpfSc6PWi0kJV3Bk/Xrjz++Pv8dn3x9rIyI4cOnWDy5GFMm75MSQoLCoqIvHiVnj07MOPe5ZSW\nGjEaNaSnZ1FUVIxer+P559cpbV6NjsPPz5vDh39j8xfluzOZzXD8+Dn69O7E6tUfc+z6GFSj0cjh\nQ78xefLdzLh3OampGUD5Qvznz0cxYkRnTlxaCpSh10NpWRZGUyGR52145pm3lTZjYuLx9vHg12Nn\n2HjTouy//Xaenj3as+atTzl69LTS5sGDJ5g4cSjnoh+juDQNgwFMpiJy8i/h6dqNE5GPYKZ8uElp\nWTalxnyuRjrwxJMV4x2vXUvE08ONkycv8N//blHip05dpGt4W3JKNpCZexK9HtCYyMg9gZ/nUGbO\nfIKE60lxUVExp05HMmFC1VuIZuWe4ULMKqB8DG5hcSJWehe2fneF9Z9UTDI7fTqSzp3CeO/9zcr4\nUZPJhNFo5PMNr/HIkpk0aaK+7Z6bm8ekyUsoLi4pbysrl4zMLPpfv7Ja2eefb+PDjyp2hDpz5hLt\n27Vg/fqv+fGnXyvaLDPy2acreeSRWRbDEPLzC5k4abHypSU7O5e01Azuuqt7lW3erjlznyA6Oh6A\n4uISfj12RrUUlxDir0PGiIoak5mZza7dh1SxL77YUU1pcTuKiopJqjTG7tq1ROLjkywmzly7lkDM\nNcvdgmJi4iksTrCIFxQnqG6jQvnVyuTkNOWKpVLHtQTi45NVuxbdaLNyHTfarCp+7VqiRTwvv4Dk\n5DTi49UTqmJi4ikqScFcafxxQdHttRkdY1m+sLCI1NQMCovVr1dhcTzFpamYzaWV4rfXZlWvuclU\nTHFpGtcqvUcxMfEWz7+hoNr3rer3uXJfSkvLsLOzxc7O1qJ8SkqGcuW8og7L9pTHqmkzpnKbZWXY\n2tpgb2/ZZlpaJgUFRb+7zdtV+fwTElIoKSmtprQQojZJIipqTFX/dKpbLFzcHjs7W7p2bauK9YsI\np0WLELy9PSrFu9KzRwesDAYlptVq6du3Cx6uXVVl9ToH6ji2ol+EOt68eTCBgX706KG+ndmvX1ea\nNQumbl0vVTyiX1d69OiAlZW6zYiIcPr1C1eVdXS0p3PnMCL6qdsMbdKAoKB69O7dSRXv368rjnYN\nsbX2UcU9XcPp3q296pavRqMpbzNC3aaDvR1dw9tYxIODAwkK8sPDVR33dO2Kg219bK3rquMu4XTr\n1g5b24rxkxqNhn79uhJR6Tzt7Gzo2q2txWtub+OPvU2ARfn+/dXlbubm3Fa14cCN84/o20U1ttLW\n1oZu3dpZnGdQkB/BwYFV1l2/fj0aNVI/1r9f9X2J6NtZ1aaNjTXdu7e3eJ/9/X1p0qR+lXUEBPgS\nEqJ+rN8tzv92RVQ6/149O6o+m0KIvw65NS9qjF6vx8bWhh9/LL89Z2dnwwvPL6JuXS9KSkrZum0/\nBw+dwMvTTZngUFqWR2LaD+TkR2Jn7YtWW55UJCQks3nzDuLikwkK9EOn01Xbbk1JSEhh8xc7iI1N\nJCjoz2nzdvTo0Z6oqFiKioqJiAjn8RXzsLa2Ijy8DefPR6HX6Zg+bQRTp47AxcWRpk0bcTHyKi4u\nTqxYMYeu4W2xt/WnpNiR1LR4MPoSFrIcB7u6tG3bnNzcPNLSMmnZMoTVq5bj4GBP9+7tiboaV95m\n3y488fj9WFtb0bVrOy5cuIJWq2XK5GHcO2M0Tk4OtGkTTGJSFF7ejixffh/hXdoTFORHHVcnrkbH\nU6+eD6teW0ZgoB9t2zQlL7+Q1NQMWjRvzOrVj+LoaE/37u3IyonHaC5i0KBOLFs6H4PBijqO7bgW\nd4XiYi3erncTHDgWJycHWrRozMWLUTg52rNs6Wx69+pEQEBdPNxduXo1Dr+63qxcuZQGDfxp06Yp\nBQWFpKSk07xZMK+//ihOTo64ObcjITGB/PxCHKw7EhbyAFrtjTavUlyswct1CI0Dx+PgYE+rViFc\nuBCFo6M9S5bMpG/fLvjX88HL042oqFjq1vXilZeXENwoEGeHULKzSsjMSkNHI9o2fQwrgyNdw9sS\nG5tIfn4B3bu359lnHsLKyoDJZGLnroPs3XsEV1dnXF2d0OtssbduSlxCFGUldjT0m0Fdr274+XlT\nv4EHKamx1G/gwfPPPUyjRvVp1aoJJaVlJCamEhrakNWrHsXVteovhRqNhp4925OSdhUrazMTJtzF\n7FmTqp085OvrhW9dL6KuXMPbx4OXXlxEaGhDWrYIocxoIjExlZDG9Xn99eXUqVP95KsePdoTeTEa\nk8nEqJH9eeCByWi1WoymEpLT95CZewobK0/0uvJF/ktKs0lM/4HcgijsbPzQassTy5iYBL74Ygcp\nKWkEBvqh1WoJ79KGpMRUsnPy6NQpjJdfehhrayvMZjP79h1h1+6DODrY/8/JYZmZ2Xz55Q9cuBBV\nPjFOklkhapwsaC9qVFTCeo6f/IKEeB0RPScQ0mAYZrOZeyYs4siRkwDY29uyaeNq6jdw58jZ2RSV\nlE+YsbX2oX3oWiIvJjJ23IPKrbsuXVrz8Ucv3dF+X7wYxajRC8jPL5+A0qFDSz795JUan8n7R7y2\n6kPefPMT5fjFFxczckQ/Zty7nL17jwDlV6c++3QlDRr4c/ewOVy9GgeAt5c733z7NtlZuYwYeT+5\nueXrbIaFhbLx81Xs3n2IufOeVG7z3zN+ME89tYA33ljPqtUfKW0+9+xDjBlzF7Nmr2DX9SV6rK2t\n+GT9K7RsGcSRc3MoKLoGgJXBjQ5N15IQX8Dw4fPIyckDoGXLEDZtXM2BA0eZNXuF0uaYMXfx3LMP\ncTXhM67Ev6e0GRLwAH6eg5k790m+3/Fjed1WBj7+6CWaNw/m7rvncOlyDFC+7NS337xNUVEJw0fM\nJev65KpmzYL5YvMbHDx4nHtnVoypHTmyHy++sJh1//mcl156R2nz8RXzmDTpbuYveIatW/ddPx8D\nH3zwPGFhoQwbPpeLF8tXh6hTx4Vvvn4Lk8nE3cPmKON4Q5s04Msv1/DLLyeZPmM5paXlQwvuvrsP\nK195hPfe28xzz69V2nx0+X1MnTqChx56nm++3Q2UL8T/3nvP0a5tc4aPmMf581fK23R15quv1uDu\noeHIuTmUlpWP43WwrU/70DUcOXKOadOWlo8X5tabHwCcjXqRxPQfgPLtRls1eg4357ZVlk1MTOXu\nYXOUiXONGgXyzddvcfLkBSZPXqK0edeAbrzxxopq21y6bKUyjlev17F27VP06N6Wo+fvJyf/Ynlc\n50j70DXotDYcOTebktLyscP2Nv60D32bE79dZuLExcpt94iIcN5+6wk2bNjK8kdfU9p68MEpzJs7\ngcdWrOKzz8rH8ep0Wt5+60mLK/A3pKVlMmTobJKvT2gLCvLjm6/frnKogRDi/0+uiIoak5LxIxdi\nVmFjV4KHdxE5hUfxrNONM6cTWLXqQ6VcaWkZZUYjIS0ySc7Yq8TLjHnYWnvzn7U/cerURSUeG5tI\nzx7t7+jOLCtfrZisA+WzjsO7tsXXx/MWz/rzlJSUMnv245SVVYyTvHLlGu3bt+DZ5yqSmbIyI/kF\nhZSWlrFp0/dKPC+/AHd3V376+ZgyWQcgKSmV9u1bsGbNp8rEGYBz5y4zduxAFjzwrJJAAVy6FE2X\nLm146uk1SsxoNJKXm0+bjmYS0rZWxE2FWOmd+fSjk8qXEIDk5DTatGnG2rUbiIurWFbo/PkrjBt3\nF5Hxz6rGZuYVRmEs7MiKx1+/qU0T2Tl56PQ6PvvsOyVeUFCEk7MDx46dUS3KnpKSTqtWTXj33U2q\n8YPnz0cxduxAHnzwOWWyDsCFC1fo27eLKpkxmkxkZeZgY2Ot2oWqsLAIRwc7Tp68wE837ZSUmpZJ\n8+bBfPjhl1yNrhj/efHiVUaO7M+iRS9QeNPYzLPnLjNwYHeWPPKKEjOZTKSnZ+HoaM+HN+1CVVhU\njK2tDXXrXyY9+xclXlKWiaNdI15+6RuuRMUq8cjIaEYMj8DJyXLL3aKSVM5dvfmLnpmS0gx83CMs\nygJ8+OGX7Nl7WDnOyMiiceMgNmzYonwhALh0OYahQ3rj4uJkUUdaWiYLF76gbIBgMplJTk6nR586\nxCR9XnH+5hI0Gh0FRbGkZVWsTVpalo2DbSCrXv2eyMhoJX7lyjUGDezBYytWK198AM6cjmTkyH48\n+NDzSptms5nEpFRGjexf5Xmu/+RbfvjhJ+U4KyuH+g3q0aTSmrM3Kygo5OSpi9jb21a7/a4QQk3G\niIoak5l3qlLETFbuaUyVJtMA5TGzZdxsNmI0Gi3ilSfk1DSzyfLGQFX9ri1ms9liByGj0WQRAzAZ\njdW+5tWdp6nSa242lycHpkrljSYTpireN6PJhJmq3k8TpipuuhhNJoymym2ay/tXqX6zubrzNN3+\neZoq111+jpU/c+Wv7e/r9414dedZOW42mzGbzRgr9aV8dnt151P1+2yu6neIavpe3efZbObGbPyK\nUPWf/arqMZqqbrOq9+1GvPLNOJPJdL0vlbrHLT5b1Zynxe/K9f5VbvNWf1cq/06Ux6ovf/ToacK7\njmfMmAfo3GUs27cfqLasEKKCJKKixjjbN7GMOTShTZumhLWqeMzGxpoJE4bi7dYbK4ObErc2eODl\n1pNJE+9WTUBp27YZrVpZ1l2T7pkwRHUFI6xVE9q2/essO2VtbcXEieo1We+dMYrQ0IZ07hymxKwM\nBiZPHsaAAV3LF7W/zs3NhWHD+jJ+/CDVQuVNmzaiU6dWTJs2UjUMYfjwvnh5uTF50t2qNmdMH0Xj\n4CDVxCmDQc+UycPwdO2qmlBk0Dvj4x7BuLGDVLczmzRpQHiXNkyfNhKttuJP0NChvfH0dMffW73M\nToD3aBo08FdNnNLrdUyeMoy+fTsTEFAxocjFxZGRI/sxduxAHOztlHhwcCBdu7Zl2tQRqjYHDeqJ\nt7c7M6aPUp/njFEEBtalz023bfV6HVOnDKd3r06qXa6cnR0ZNbI/o0cPUC3u3rCBPz26t2fqlOHK\n4vEAA/p3o25dL8s2p4+iXj0f1aQfnU7L1Gkj6NGjg2qXK0dHe0aPvgtf9wHodRVXOe1s6uHu0tGi\nzb59u6i2/byZjbUnXq49bopoLd6Dm40c2Q8Xl4qdmAID69K3T2emTBmmWrC+V8+O1K9fr8o6PD3d\nGDK4V0WLWi3Tpo2kjlMbHGwrJjHptHb4eQzC160fBn3FGFdbax88XMOZPGmYak3Ubt3aERwcyPRK\nr+2UKcPx8nJj2LC+Skyj0TB92shqz3PYsAjVGFc/P2/63WIS13PPryU7+/qas0XFPPX0mmoTcSFE\nBbk1L2pMUUk6hSVJlJZmodfb08hvJh6u5TNse/UNxsYhigbBVjzyyFjCWoaj09lga+1LXmE01oY6\nBNebg5N9Q7y93WnX2YCVbSI9evuzfOkD2Nm63nZ/oqPjef6FdXz22Ra0Gg2NG5f/g8vKPU1k7Nsk\nZezD2lAHW2svvDzdaN/JBoNtIj161WP58gXY27pVW3dZWR5X4j8gJulzikvTcXIIRaO5/e9127bt\n54UX/8NPPx2jYQP/aieUALRsEcLVq3EUF5cwoH835s6dgE6nvT5Z5yoGg55p00YydEhvrK2t6NXH\nH73NVVq0cmLF4zPx92uCu7srzk4ORMfEExjoy9NPLcDb24OGDf3Jyy8gLS2LsLAmrHhsLvb2drRs\nGUJMdBxFRSX06xfO/fdPRKfT0bJlCBcuRpUnhJOHMXxYBFqtgYykAA4fukpSnBP13GdQP6gZbm4u\nOLs4ERMTh7+/L089uQBfX08aNPCnSYtC7BwzGTQ0hAfnz8Ogt8PZPoSConiMpmI8XLvQoO7k8nGL\nLZuUt6nTMWniMEaN7I/BYKBRcCCRkVdxdnZg8aIZtG3bHFdXZ+rUceZqdBz16vnw1JML8PPzJijI\nj6atirB1yGDgkMY8+MD9WBnsadGiMTHXEigsLKJ3r4489OBU9HodrVo14WLkVbRaDRPuGcKYMXdh\nMOgJDg4iMvIqTk4OLHxoGh06tMTVxYkuPVzQW8fRKdybxx+fj4uzD4GBdSkpLiElOZ1mzYJ58sn7\ncXR0oHWbYHz8E3F1L2LClPZMHD8drUZPq1YhXIy8ikajYfy4wYwfNwi9XkdISH0uRl7F0dGehx6c\nQufOrTHoHbl6yY3fjseRmuhFk8D5eHvVJSCgLqVlZSQnpxEa2pCnnlyAk5MDxcUlrFnzCWve+pTo\n6HhatWqCwaBHUxbK/n3RxF8DfdldtG89FI1Gw4ULUTz73Nt8sXkH9va21K9fDycnB7y93LkSdQ0f\nH08ef3weQUF++Pv7YjKaSExKpUmThjz55HxcXBwpLi7h7bc/4801nxAVFUerViEYDAZatmzCpUvR\nYDYzatQAJk26G61Wh6NtI/IKo9BpbalfdxLuLu3R6+2JveLB8ePXSIn3oLH//fj6+FOvnjet2pmx\ntkslon8QjyxegLW1E6GhDUlOTiMnJ58uncNYunQ2VlYGWrUM4fLlGEwmMyNH9mPK5GHVjgN3cLCj\nbl1PLl++hreXG489NtdidYGbrVr1kbLJAUBhYTGzZo5Br69635grV67x3PNr+XzjNqwMBho1CgDg\n8OHfePa5tXy//QDe3h74+JSvipGYtpPLce+QmfMb9raBGPSOlJaW8c47n7P69Y+JjIymZcvGWFtb\nkZWVw8qV7/Pue5tIT8+iZcsQ1Rewyo4cOclzz69l27b9eHm74+tbPiTp229388KL73Do0AmCGwWp\nvoDcKd9t2cuLL77DzweP07BRAK4uThiNRt57fzOrV3/E+fNXaN48WIY+/IP8qyYrxcfH07t3b/bv\n34+Xl9f/foL43aIS1hMV/6FyHOw/D3+vYQAYTcX8fGqCMtEAoEXDJ7G38efw2ZnKeECtxkDHZu+S\nW3CF01eeUspaGzzo0uJjZUb971FcXEKvXpNUa2+++cYKuvesz6EzM5Q2NRoDHZv+h/yia5y6/LhS\n1srgRpcW69Fpq/5jdyJyGenZR5TjAO/RNKo363f3D2DPnsPcO/NR5djNzYV9e9dXudYjwKxZj6nW\naZ02dQRLlsykT9/Jqi0cV77yCP0GNOfQmamYTMXXz1NH+9C3OXY0nenTlyll67g6s2/fJ3z40Ze8\n+uoHSrx166Zs2riaOXOfYMeOinFyEycO5bFH59Cn71TVWMsXX1xM505hRPSbpuxypNNp+erLNWRm\n5jB5yhKlrIuLI/v2fkJ63rdcjqtYdN/JPoT2oWs4fflpkjP3KfG6HoNp7D+ffv2nE3XTuMfnnn2I\nHj3a06fvFGVim06nZfOm18nPL2LipMXKrVhnZ0f27vmYrMLvuRRbMabW0a4RHZqu5cGHnuPbb/co\n8dGjB/Dcsw8xYMAM1bjHp59aQEREOL17TyYvvwAov5q38fNVBDYs49iFh7hxm1uvs6dzi/V89uke\nnr5pTG1ISH22bvkPZ6NeIDF9pxL3ce9HaOBiBg6aqUyEgvKJU4MG9aRnr4nk5RVcfz81bPjvq2i0\nWsaOfVC58uZgb8fuPR/x/fYDPP7EG0odjRoFsn3bOyxdupJNmyvGDt89tA8rVz7CkCGzOXvushJf\nvmw2w4dH0LPXJGWspUaj4ZP1L2NjY82o0QuUNu3tbdm96yN27T7Io4+uUupo0MCf77e/y2MrVrNh\nQ8XY4UGDerJ61XLuHjaH06cjlfiSJfcybepAfj41gVJjrhIPC36JhFhHRoyYp9xKt7OzYecPH2LU\nHeV8dMWYWjvrunRq/gFPPvmWaqH/Af278eabKxg58n5O3DQWfNHCadx333iqcu7cZYYNn6tMbLOx\nsWbnD+/j66v+3/HDzp/Zs/sQUVfjOHbsjBLv27cLa99+ssq6CwoK6dFzorJZBMC77zyDb10vhgyZ\nrbRpbW3Fju/fw2B3jjNRzyllbay86Nz8Y1548T3ee2+TEu/VsyPvvPMM4+9ZqBqXff+8iTzwwOQq\n+3L5cgyDBs9SxoJbWRnYvu1dzp69xPwFzyjlvL092Lvn4zu6csCOHT8yZ27Fa+bl5cbePet5c80n\nvPXWZ0q8c+cw1n/88h3rh/hz/etuzf+VZkH/U5jNZmISN6piMYkblJ+zck+pklCA5Ix9pGT+qJqU\nYjKXkpL5s2oCE0BxaSpZeWdvq0+/HjtjsQD81q37SMn8SdWm2VxKSuZPFm2WlKaTlVt5zGu5MmO+\nKgkFSMrYd1v9A9iyRd1menqW6p/HzQoLi9i957AqtmXrPn47qd5HvDy+l9Ssg0oSCuVjb1Myf1Rm\ngN+QkZnNwYPHLeLHj58lJiaBnTsPquJbt+7n9OlIiwXDt2zZy969h5UkFMrH323/3rLNrKxcfv75\nGMmVXrOc/AsUFCWQkqkeW5ecsY+z5y6rktAb57l37xHVwuhKm9v2qcYDZmfn8uNPv1q8z7kFl8gv\njGXbtv2q+Lat+7lwIUqVhN44z337jihJKJSPbdy+/cD186los/xzctTifb5wIYrLl2Mszj85Yx+X\nLsWoktAb57l//y9KEgrlv3Nbt+1n+7b9qtu/efkF7N9/lC2VXvNLl6KJjLzK1m3q+NZt+4iKilUl\noVD+u3Lgx19VE37MZjPbtu1n+/cHVG3m5xeyd+8RtmxR133lyjXOn79i8f5v376f6Oh4VRJa3uZ+\n0nOOqZJQgJTM/Xy//YBqPGdBQRF79h62eD8LiuPJyb/Elq3q+I4ffiI6Ol6VhJafv/p9v9n3O35U\nbdxQVFTM7t3q38ENG7Zy332Ps2nz9xw7dobAwLp06dKa2bPG8srLSypXqTh06DdVEgrlv887vle3\nWVxcwq5dBy3+vhSVJJOdf46tlT5be/cd4dq1RIu/I5Xf95vt+OEn1YTEkpJSdu762eI5SUmpHDt2\ne3+Hb1flz21ycjpHj562+GwdPHiCjIxbb4kr/j7+dYmo+PPZWFnOPLex8rzNuIdF7Faqmu3u61tN\n3dbV96UqWq0NBr3L7yp7yz76Vt3HqlhZGSzWPPT18cDH28PilpuvT/XnU12bleP29ra4u7vg4eFq\nUdbLy92yzSrq+F/xyn3Uam2wMrhgZaVeHcHGyhMvTzfV+MNb1u3jWfX77+Nl2abGCmuDq3L78wYf\nX088POtY7Mnu4+uJTxVt+lT32ariNbeyMuDu7mpR3sbKEw8PV9VGBLc8z9s4f4NBj4eHm0Xcx8cT\nNzcXi33g/z/vZ+W4Xq/Dy8vdIu7t7YGbm4vFrdXqfj+tb/G5rVxegxZrK8vz9PR0w93dVTU+Gqr+\nO3Grxyr34/ON21TH0dHxvPjCYhYvnoGDgx3V+aO/K6DBxsrD4rNYp44z7u4uqrHK1Z3LrR6r6jOk\n0Wgsfk9qWlXn7+PjYRF3dnaUZbT+Qf7yiWivXr14++23mTRpEmFhYQwZMoSLFy+ydetWIiIiaNeu\nHY8++qjyDX3p0qX06NGD1q1bM2jQILZs2XLL+nft2sXw4cNp164dAwcO5LvvvrtleWFJo9EQ4D1a\nFQvwGav8bG8bgL/XKEBz/TgIf++ReNXpgZtzxQQUd5dOeLp2JcB7DPY2NyZmaAn0HoedjR//i9lc\ncSUhKMiPWTPHKAlTo4YB3HvvaDxdu+Hu3FEp5+bcAS/X7vh7j8LeNujGGRHgPRp72wCl3M2zqrUa\nHTiBJ5oAACAASURBVI3956LVlP/zNuidCK50W/5Ws45vmDp1hLK7jEajYeqU4co41spt6nQ6Hl8x\nT/nn7eLiyNJls6lb14t5c+9RzjMoyI/Zs8fh4dIRz5t29Knj1Bpvtz5MmTyM0OvLz2g0GiZOHErT\npo1YtHA6np7lY2KtDAaWLp2Fvb0dj6+Yp+wi5OzsyPJls/Hx8WD+/EnKZJiAgLrMnXMP3bu3V+0V\n3rFjK4YP68ukSXfTrFmwEh8/fhAtWoTQoO40rK9PVtNo9ATXm4VeZ0eI//3KLkJ6nT3B/vfh6enG\nAwsmK2361/Nh3twJhIe3YfDgnkrd7du1YMSICCZMGEKLFo2V+Jgxd9G6dSgN6k7F2uChtNmw3kz0\negceXzFP+cfm4GDHY4/eh7ubKw89NFVJgP38vJl//yQ6dwrj7qF9lLrbtGnK6FED8PMcjNNNE/Z8\n3Pvj6tiCBx+YovwD1+t1PLx4Bi4uTgQHzEOnLU9WdFo7GvvPxdXVmUWLpitt+vp6smD+ZP6PvfOO\ni+rM/v97KjMDQ++99yZSFBFs2Bv23o0ldmONpmmK0TQ3GpPNbpqJ6TFlY4klsZeoYANRpAiIdJHe\n5vfH6MBlIBv36/52N5n36+XrxRyf+5znuXMHztx7PufExIQxcmQ/3dydwgMYN3YQY8YMJCIiSGdP\nSkqkS5cwFi2aohOrSaUSli+fgaWlGevXP64LkIyNlTz11OOYmalZuXK2Luh2cLBhyZJpRHYOZszo\nAbq5w8L8mThxCKNH9ScqKkRnHzq0F926RbBwwSRcXLQ+JRIxy5ZOx9ragnXr5usCI5VKwdNPLUCt\nNmbN6sd0Pu3trFm6ZBpmJgE42QzWzW2q8sPFdhgjRvQlJiZMZx80qAfx3SPxcJyE0kgrwhIhxtNp\nGgq5DU8+OU9XqkqpVPD00wswMVFpc0XvB/q2tlYsXz6djhg+vA9du7YIAQf0jxcI5pqbmzFVC8th\nyWUyjIzaf3Td+vMcEODF1Fb5qQEBXkyfNpIhQ3oR162zbpw2DSQWd4dxqBQPhF9iPBwmojRyYO2a\nuVhYaMtjKRRGPPXU46hUStatm697hG5lZc6KFbM63OegQT2Ij4/Sve7Tuyt9+8bx2GNjdQI5sVjM\n3DnjcHdvEQa2V93k/8rsWWN0ebgikYjZs7VCxVWrZuuEY3K5jHXr5ul9efq9PKha8d/A7/k78Weg\n/Szq/zJ2797Njh07cHV1ZfXq1SxYsICuXbvy/fffU1paysiRI+nSpQuDBw8mMjKSNWvWoFar2bNn\nDytXriQgIAAvL/3ab8ePH2fdunVs376diIgILl26xKxZs3BwcCAysv1izgb0ycz/hJw7XyAWyTEz\nCcTTaSoW6lDBmGsX/di+w5ym5goiQv3o/JwasViGmXEAZRUXQSTCzDgAsViKVKpGrfKjqjYPudQU\ntbHPb/qvqLrGlczNVNVkYmkaQZDHaozkVgQH+2JlaUZpWQXBIb6YmpogFks5fiCQb7/XProaMiiQ\nTr4yZKhRq3ypqslBLlWjVml9FhWV8sQTmzh2/Bw+Pu5seukJwsL8Kb7twpsvOXKvKgsHWw9e3mSL\nmQncKT1Ces526htLsbPsRYD7MiQd5LaampoQEuLLjevZmJmrCQnRBk7FJWVan8fO4eXpwosvPkFE\nROB9QYgD6elZeHu56e5YBAR6Y21tTklJOcHBPlhYmCISSTj9cyBffHUOaKJfYgARy+SYmIgJDfMn\n/Xo2arVKF6x5elnxxl/duZBcgqurNV3DtUG4u7sTbq4OpF3LxMvLVdfaMzDACxsbSwoLS3Q+xWIx\nncIDOHLkVxobGwgP80ehMEIqlRIa4kta2k1MTJSEhfoDIJdbYqL0pK6hBIXcBmOlOwBlRY5sf9mZ\n8oob2Fi5selFeyxNYfR4L5z9LSkoKCYqwgcHR3PEYjHh4YH8/PMZ6usbCAv3vx84N/Pia/6cOpOB\nsYmC2EjtPhvrLfl4hxcZ2cUojGxYu8oDVzttO0p3dyeuXLmBp6crzs5a9f/IMZ44+lhy+3YRkRFe\nODmbIxKJCA8P4NDhk9TW1hMeFoBKpUCj0bDv60COHr+BWKRgyuQAgjy0gUBgoDcFBcXY2VnrWm3e\nK3VkxxZnSsvTsTJ35sUXHbAyAz8/D+zsrMnPLyQw0Adra+0f4PAwfw78dJzqmlrCwwN0gXOn8AAu\nX05HoZATHuaPSCTCysqcgAAv8vLuYGdrhf/9LzjOzvZ4erpw8eK1+++tNogbmuSOjbsVeXl36NzJ\nE1c37Z3wsDB/9u0/RnV1DWFh/qhUSsRiMRte9ufEmTQURjK6RgUgEolwdDLntR0+/Hq+AAcHK7qE\na7/YOTvZ4enpQkpKGu7uTrje9+nj446Dgw05Obfx9/fE1tYSgMtn/Xn3vSM0a6qJjfaj81MqFAox\nz77kz8mzV5HLZcRGBiAWi9E0mfHZ331JSy9AJrFi+VIvPBzByckOLy9XLly4ipubo65igI+3Gw6O\nNmRn5+Pv74GNTceCRIXCiPDwAC5cuIJEIiU8PACJREJNTS1r1r7Cnj1HMDNTY2Qk19WgtbaxICp6\nFIEBXmzevAp/f0/Onb/CmjWvcPPmLbp3j2TL5lVYWZkTGuKHhYUpFRVVhIT4olYbI5NJCe8UwLnz\nlxGJxISH+SOVSmisNeWr9/24lJqPVGLO4oU+eDlrvzR4e7tx9uwlXFzs8XDXfmH38nTB2cmOm5m5\n+Pt5YG/fcQ1muVxGp/AAzp69hEgEYWFaAZuZmZrgEF8ys3KxtDAjKEj7O/HOnWKWP7GJkycv4Ofn\nwaZNKwhp9UXz/4JabUxoqC83b97CwkJNcLDWp4O9Df5+Hpw4eQFnZ3u8PF3/yUzt89Zbn7Dj7U/R\naDTMnDGKxYvbz5v9d9Pc3Mi1nK3kF+9HJlXj4/wYDtaJ//zAPyj/9WKlXr16MWnSJGbMmAHAL7/8\nwty5czl58iTm5tpf0EuWLMHe3p7Vq1frHT9y5EhGjRrF+PHjycvLo0+fPvz888/Y2dkxd+5cQkND\nmT9/vm78xo0bqaurY8OGDb97jWVlZZSXl+vZzc3NsbB4eLX3/xJlFSmcu7ZMYAv3eQFr8xjd68LC\nEuITJgrykFavfowRoz25kC7Mo4rw28LdylRBZx2RSEb3sF3IZfrnUqPRcOLSVGrqWgqG21rE42ix\niO5xE3RdXgBWPDGT0DB/Jk9eIZjj/fdewtX7Jtdz327lU0pc6CesXLlDkOPn7GzPoYMf0H/ALEHO\nYmJiN7ZuXcaxlLE0t8pB9XSajqfjpHbOHLz33ldsfP4t3WupVMLPh3eyZcvf2P3tAZ3dwcGGX37e\nyaBBjwlyFnv17MKWLauI7TaO2laF0RcumEz3+EjGjFks8Pf2jufIzy/k2efe1NkkEjGHD31Eef0H\n3C7ep7MbyazpFvYJQ4bMIy3tps4eHx/F1jeeJLbbOEFu5rx5E0jsE8uIkQsEPrdve5qSknLWP/WG\nziYWizl44H0qm3aRX9zyaFMusyQudBfDkxZy5cp1nb1btwj+/vdnOJo8lqbmljxJN/ux1N3tybDh\n8wQ+t76xjvDoKlKzXm1lFdE15D02vfA1H7cqgG9pac6xo58wfsIyUlLSdPaYmDA++nAjR1PG0thU\npbO72o2iubofgwY/JvD52qtrqG9oZNWqFgGFSCRi7553+fiT7/nww906u7m5mhPHP2PS5BWcP9+S\ncxcZGcwH728itts4XRkggKlTkxg/bjD9B8wU+Hz55ZVIxGKWP/GSwP7jP97hyy/38ff3vtLZTE1N\nOHH8U2bMWMuZsy25z53CA/j881c4mjKOhsaWnDtn26HIm0bQt98Mwd2jF19YTnxvIy7ffF7gMybo\nbQpKDpJd0JIrLpEY0z3sM6ZNe4qTJ1uaC4SE+PL5Z2/QPX6CrjsTwLhxg3hs9lj6JE4T5KBu2LCE\n3v3UAgEjQHTgdt556xhv7dilsxkbKzl+7FMWLtqgazUM2juOu7/ZTvf4CRQWlujso0f156WXnqA9\n9v90nHnznhbYvvxiKz//ckbQ4UyhMOLZZxby5Vf7BM0ifH3d+eH7t4lPmERBQZHOnpSUyLKl0+nR\nc5Ig73Xduvm4uzkya3aLgBHg012vcepUsqDDmZGRnOPHdrF6zSu6DmfQIhDr0XMyeXl3dPahQ3vx\n2qtraY8jR84yfcYagW3nR1u4cOEqr7z6d51NLpdx7OgunnrqDV2HM9B+WT3w0/uPRH/RtsOZTCbl\n6JFP2LBxuyDX2MXFnkMHP/zNSgBtOX06hQkTlwtsf/vbC/RIiO7giH8fOXe+IT2n5fewCDHdQnei\nMPpziqj/J+6I2ti05KUolUokEokuCH1gq6qqQqPRsHXrVvbs2UNJifaXTU1NDaWlpXpzAuTm5nL6\n9Gnef/99oKVoeFRUVLvjO2Lnzp28+eabevYFCxawcOHCh5rrf427VVfbsaUJAtErV64LglCAlOQ0\neg+obXsodytTqagSCgo0mgbuVWe023KwofGuIAh94L88/4YgCAVITklrtwB2ckoaZg5CsYZG08i9\n6uukpAjXkptbQFZWnp5wJiUljcqaDEEQClBRKTy+NW2FE42NTVy+nE5yG5+3bxeRlZWnJ5xJTkkj\nLe2mIAjV2lMxNRXmiD0Yn9/qjxNoxT2XLl3DzEnos66hmLsVeYIg9ME+r6VnCYJQrT1VL5/0gc/W\nwQZoH2levHQNazehz/qGUiqr8wVBKGivlaqabEEQCtr3+VqKPW1JSUnDI7CyjVVDRdU1Qfcs0HYF\nysnJFwShD+aoqr0lCEIf+MxI0b8bk5ySpmszqfOo0ZByUd9nefk9srJy9a6tlJQ0MjNzBUEoQEpy\nqi6doq1dLNH/Q5ySck3v2qqoqOTmzVt611ZyShrVdfmCIBTgbmUa+elpeo8wk1NSCesizLPUjk/l\nbptrvampiqqabL19XrqUzq1bt/Wui5SUNFIupunV3kxJSSUqTr87093KVL19VlXVcP16tt45T03N\nICsrVxCEavcjfN8FfpP1P7vJKWkkXxDaa2vrcHNz4kabz2d6ehZZWbmCIFS7nzQuXU7XK6afkpJK\nWZm+ACc5JVXvfaurqyctLVNvnxkZOWRm5gqCUIDk5I732XaOB2tp+77V1zeQmpqhNz4rK4/y8orf\nLD33e2nrs6GhkStXb+j5vHWrgNLSu1hb//4bPe3uMzn1PxKItv27oKGZiqr0P20g+l+fI/ow/PDD\nD3z55Zds27aNs2fPcvbsWfz8/DrMB3F0dGThwoWcOXOGM2fOcPbsWc6dO8eOHTvaHd8RkyZNYu/e\nvXr/Jk1q/07YHwnzNo/gAb3H8qGh/nolP6KiQzo8tq1dLDbC1Lj9Rz9ymXmrfNL7c5iEtFtnLjoq\nhOjoENoSHaW/FrFIhqmxP1FRQruHhzOeni74+XkI7FFRIahVvrrcxge0t8fWfoV7kdGpUyDRbXy6\nujri4eGsF4xER4UQGOgtKNyutYcSGdX+PqPa7F8mk9KpU6DeOhVye8xNnQkJEZ73qKgQAvy99MQQ\n0VGhREW277PtPqVSCRGdgvR8GsltURs70alToNBndAgmSg+kEmENQwt1CJGRwXp3YqLaeT9FiDE3\nCSIqWmi3tbXC3d1ZkPP4YA5jpRsyqTAA6shne/sUi8V0jgjSu4asrS3w8HDRs0dFheDp6aInSouK\nDqVzZLDe3Z+o6FC9a0UkEhEZGay3FksLM7y93fTGR0eFoDJyRi6z1Ntn585BgqL42vGhep9vEGGu\n1j/nMokaE5WH3j47dw7Czc0J+zYte6OiQugcoe8zKioUc3Xba0vrs+0+1Wpj/P099PYZFuaPh4ez\nnuil7fECv9H6n92oSP3PkImxioAAL719BgX54OHhosubbb3PTuEBeqK06Cj997Mju7GxkuBgb731\n+/t74unpImjyALT7e69lPe3sMzpUz65UKggO9tHbv4+P+yMJQttbi1wuI/PmLUEDCaDdz8nDzg3t\nv8f/P2h7PYtEUsxM/r1NW/6b+a8vaP/BBx/QtWtX/Py0OV55eXl8++23gsfpBw8eRCwWY2ZmRnJy\nMlOmTEEul/P111/z1VdfERkZSXR0NPfu3eODDz5g+vTpmJiYYGlpySuvvEJISAj29vY0NDSQlpZG\nYWEhtra/XwWtVCqxsLDQ+6dU/vFVfQq5LTKpGZXVmYjEMqQSE3ILv6O88gqWZp2RSpSoVAoC/L24\nkJxKU1MTE8YP5vH5kzBWOiIRG3OvKh2JWIGPy2PYWcZjqvKjpvYe58/nINbY0CVsJWpVx/2dLdRh\n3KvJoLHxHtbmXfF3W4SJsRmBgd5cvXKDxqYmxo4dyMIFk3F2tsfcXM21tEyMTZQsXTqdwYN7ojb2\npaa2kgvns6HZmi7hKzA19iE6KoSc7HxuFxQRHOzLls2rsLGxJCYmjIsXr1FeXkFcXCTPb1yKiYk2\nn/Ve1Q00miYcbQbg6Ti1w0L3QUE+VFfXcvPmLZyc7Ni4cQkhIb5ERYVwK7eA/PxCggK92bJlFba2\nVnTpEs6li+mUld+lW2xnXnhhGRYWpoSE+HL7zlXsHOvp3bMfSxZPx9HRFhsbSy5eTMPISMaiRVMY\nOaIfQYHeVFfXkJ6eiY2NJS++sIywsAAsTEIoLc8j+XwBpsbuRIesRSG3JqZLGJcuXqOs7C6xXSN4\n8b7P0DB/kpOvUlfXQFJSH55YPhMHBxvs7axJuZiGTCZj4YJJjB49gIAAL2praklPz8LKyoLnNy4l\nIiIIc3Uw1bX51NTdxljhSrDnWhRGtsTEhHHpcjqlpeV06RLGSy8+gVptiqmxP+nXU8m8WY+fV2/8\n3B/Dxkaryk5JSUMqlTB//gTGjxuMidKTxsZ6LpzPpLHenJiwpdrgKiKI3FsF5OTcxtPLhddeXYuT\nkx0xMWHk5F7HwqaM4KBQNjy3AlNTU8xMArlbmUpTcy32lr3wcZmDtZUVzi72pKSkIRZLmDd3HBMn\nDsXX14PGpibSUm9iaWHGc88tIiYmjM6dgygsuoPSOA8PT2c2PLcGF2d7usSEceXKDYpLSomMDGbz\nyyuxsDCjU0Qg+bevYmNfQ1xcT1aueAxbGyvcXB1JTklFLBIze9YYpk1NwsfHHU1zM6mpNzE3V/Ps\nMwuJje1EREQQxaWFGKlycXd3ZOOGNbi6OhITE8aNG9kUF5cSFRXCppdWYGZmirlJMDduppGRXoO3\nRwIBnvOxsLDEzd2JlORURCIRM2aMZMb0kZgo3WjWiEm+cIO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42o9EqVTQOTKY9PTM+92m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\nkSQYRImIiIhIEv8fOcTzXJevwfoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c97e650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"Age\", y=\"Sex\", hue=\"Survived\", data=traindf)\n",
    "sns.despine(trim=True, left=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The swarmplot above is a useful way of looking at data when you want to look at the individual observations along with some representation of the underlying distribution. This plot is particularly useful for examining this dataset, because it allows us to quickly determine survival rate by both Age and Sex. For example, we can see that the chances of a male surviving are very low when compared to a female. However this does not apply to young males. It looks like being a child (no matter the Sex) drastically increased a passengers chances of survival. We can also see that there are more males than females in our dataset and that the distribution of ages is older for males than females. By looking at the proportion of green to blue dots it is easy to see why a simple gender model scores so well for this challenge!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ERLVVXR9y+pXElJQUTJo0CWFhYVatXdu1axciIyPRvXt3jB49Gnv37nVCStsou3SH17RZ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\nBoREAAAAGBASAQAAYPD/5yk9OzDbNmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ccaed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lines on markers represent 95% ci\n",
    "sns.pointplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", palette=[\"#222343\",\"#ec7869\"], order=[3,2,1], data=traindf)\n",
    "sns.despine(bottom=True, right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slopegraph above shows the increase in average survival rate when examining Passenger Class. From the graph, it appears that being a female in either first or second class all but guaranteed your survival. The survival rate of females in third class is about half as high as females in first or second class. However, females in third class still have more than double the survival rate of males in thrid class. While there is some increase in the survival rate of males from second to third class, the likelihood of a male surviving is still below 50%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling / Munging\n",
    "\n",
    "We know from running the describe method earlier that there are some missing values in our dataset that need to be addressed. Let's look at those missing values a little closer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print traindf.isnull().sum()\n",
    "print \"\\n\" \n",
    "print testdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined = traindf.append(testdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>39.0</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked      Fare                          Name  Parch  \\\n",
       "413   NaN   NaN        S    8.0500            Spector, Mr. Woolf      0   \n",
       "414  39.0  C105        C  108.9000  Oliva y Ocana, Dona. Fermina      0   \n",
       "415  38.5   NaN        S    7.2500  Saether, Mr. Simon Sivertsen      0   \n",
       "416   NaN   NaN        S    8.0500           Ware, Mr. Frederick      0   \n",
       "417   NaN   NaN        C   22.3583      Peter, Master. Michael J      1   \n",
       "\n",
       "     PassengerId  Pclass     Sex  SibSp  Survived              Ticket  \n",
       "413         1305       3    male      0       NaN           A.5. 3236  \n",
       "414         1306       1  female      0       NaN            PC 17758  \n",
       "415         1307       3    male      0       NaN  SOTON/O.Q. 3101262  \n",
       "416         1308       3    male      0       NaN              359309  \n",
       "417         1309       3    male      1       NaN                2668  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             263\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the training dataset is relatively small, I combined the training and test dataset into a new \"combined\" dataset will be used to interpolate the missing values. I believe this gives the best chance at an accurate result for filling in the missing values. This step may not be necessary for problems with a large training set.\n",
    "\n",
    "From the table above, we can see that the Cabin feature has the highest number of missing values. Because this is a categorical variable, I decided to simply create a new category, \"U\" for unknown. I didn't see the importance of noting the individual room of each passenger, so in order to simplify the feature, I simply took the first letter of the Cabin. The first letter should indicate the deck of the ship where the passenger's room was located. The code below fills in \"na\" for values with \"U\" and then maps a labmda function that replaces each cabin number with the first letter of the entry. i.e. C105 becomes \"C\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>39.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>38.5</td>\n",
       "      <td>U</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked      Fare                          Name  Parch  \\\n",
       "413   NaN     U        S    8.0500            Spector, Mr. Woolf      0   \n",
       "414  39.0     C        C  108.9000  Oliva y Ocana, Dona. Fermina      0   \n",
       "415  38.5     U        S    7.2500  Saether, Mr. Simon Sivertsen      0   \n",
       "416   NaN     U        S    8.0500           Ware, Mr. Frederick      0   \n",
       "417   NaN     U        C   22.3583      Peter, Master. Michael J      1   \n",
       "\n",
       "     PassengerId  Pclass     Sex  SibSp  Survived              Ticket  \n",
       "413         1305       3    male      0       NaN           A.5. 3236  \n",
       "414         1306       1  female      0       NaN            PC 17758  \n",
       "415         1307       3    male      0       NaN  SOTON/O.Q. 3101262  \n",
       "416         1308       3    male      0       NaN              359309  \n",
       "417         1309       3    male      1       NaN                2668  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replaces value of missing cabin with U for unknown\n",
    "combined.Cabin.fillna(\"U\",inplace=True) \n",
    "\n",
    "# mapping each Cabin value with the cabin letter\n",
    "combined['Cabin'] = combined['Cabin'].map(lambda c : c[0])\n",
    "\n",
    "combined.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our Cabin feature completed, let's see what the survival rate of passengers is broken down by cabin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1aqSkpCSNHDnS7cy5ubnKy8tzGcvJyXH7+QBgVU04Hg0ALJXENm3a6IsvvtCaNWt09OhR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105af1ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Cabin\",y=\"Survived\", capsize=.25, color=\"#222343\", errcolor=\"#bbca42\", data = combined)\n",
    "sns.despine(trim=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above it looks like the survival rates of known Cabins is about twice as high as the unknown (\"U\") cabin. Furthermore the survival rate of konwn cabins looks to be somewhere between .5 and .8. Because the biggest difference in survival rate appears to be between unknown and any known Cabin, I debated combining the features categories into a simple boolean of known cabin or unknown cabin. While I ultimately decided to leave the Cabins information broken out, combining the cabins could simplify the model without loosing much information.\n",
    "\n",
    "Now that we have the Cabin feature finalized, let's look at Sex. The code below changes the \"male\", \"female\" strings into simple 1s and 0s which are more friendly to machine learning models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>39.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>38.5</td>\n",
       "      <td>U</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>U</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked      Fare                          Name  Parch  \\\n",
       "413   NaN     U        S    8.0500            Spector, Mr. Woolf      0   \n",
       "414  39.0     C        C  108.9000  Oliva y Ocana, Dona. Fermina      0   \n",
       "415  38.5     U        S    7.2500  Saether, Mr. Simon Sivertsen      0   \n",
       "416   NaN     U        S    8.0500           Ware, Mr. Frederick      0   \n",
       "417   NaN     U        C   22.3583      Peter, Master. Michael J      1   \n",
       "\n",
       "     PassengerId  Pclass  Sex  SibSp  Survived              Ticket  \n",
       "413         1305       3    1      0       NaN           A.5. 3236  \n",
       "414         1306       1    0      0       NaN            PC 17758  \n",
       "415         1307       3    1      0       NaN  SOTON/O.Q. 3101262  \n",
       "416         1308       3    1      0       NaN              359309  \n",
       "417         1309       3    1      1       NaN                2668  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = combined.replace([\"male\",\"female\"],[1,0])\n",
    "combined.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            263\n",
       "Cabin            0\n",
       "Embarked         2\n",
       "Fare             1\n",
       "Name             0\n",
       "Parch            0\n",
       "PassengerId      0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Survived       418\n",
       "Ticket           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're getting closer, let's look at the two missing Embarked values next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ya5+PPUNr/3fW2udj9/de/hvb46Kyuro6TU1N2bRpU8n6xo0b07lz53c8fubM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\nKgEAKExUAgBQmKgEAKAwUQkAQGH/D9q88I8LbY26AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d365590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Embarked\", data=combined, color=\"#222343\")\n",
    "sns.despine(trim=True, bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are only missing values in 2 out of the 1309 entries in the combined dataset for the Embarked feature, I chose to simply fill the missing value with the most common value. The graph above shows that the vast majority of passengers Embarked at Southampton \"S\" so let's fill the missing values with \"S\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "combined.Embarked = combined.Embarked.fillna(\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            263\n",
       "Cabin            0\n",
       "Embarked         0\n",
       "Fare             1\n",
       "Name             0\n",
       "Parch            0\n",
       "PassengerId      0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Survived       418\n",
       "Ticket           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far we have cleaned up the values for categorical variables, but we still have to tackle the continuous variables of fare and age. Let's look at the one missing Fare value next. Because there is only one missing value, there isn't a need to get super fancy on filling in this value (since it will not have a large effect on our data overall). However, we know that fare is probably closely correlated to the demographics of the passenger and the passenger class of the ticket. Let's look at the passenger who does not have Fare information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>60.5</td>\n",
       "      <td>U</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>0</td>\n",
       "      <td>1044</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked  Fare                Name  Parch  PassengerId  \\\n",
       "152  60.5     U        S   NaN  Storey, Mr. Thomas      0         1044   \n",
       "\n",
       "     Pclass  Sex  SibSp  Survived Ticket  \n",
       "152       3    1      0       NaN   3701  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined['Fare'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now know that the missing Fare belongs to Mr. Thomas Storey, a 60 year old male that bought a third class ticket in Southampton. Using this information we can see what the median ticket price is for similar passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>C</th>\n",
       "      <td>38.00</td>\n",
       "      <td>83.15830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>701.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>35.00</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>35.00</td>\n",
       "      <td>79.65000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>C</th>\n",
       "      <td>23.00</td>\n",
       "      <td>27.72080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>30.00</td>\n",
       "      <td>12.35000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>313.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>28.00</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>C</th>\n",
       "      <td>15.00</td>\n",
       "      <td>14.45420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>22.00</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>669.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>22.00</td>\n",
       "      <td>13.77500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>568.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>C</th>\n",
       "      <td>39.00</td>\n",
       "      <td>62.66875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>44.00</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>42.00</td>\n",
       "      <td>35.50000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>C</th>\n",
       "      <td>29.00</td>\n",
       "      <td>15.04580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>818.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>59.00</td>\n",
       "      <td>12.35000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>908.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>29.00</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>C</th>\n",
       "      <td>24.25</td>\n",
       "      <td>7.22920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>747.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>25.00</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>25.00</td>\n",
       "      <td>8.05000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>638.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Age      Fare  Parch  PassengerId  SibSp  Survived\n",
       "Sex Pclass Embarked                                                      \n",
       "0   1      C         38.00  83.15830    0.0        701.0    0.0       1.0\n",
       "           Q         35.00  90.00000    0.0        858.0    1.0       1.0\n",
       "           S         35.00  79.65000    0.0        731.0    1.0       1.0\n",
       "    2      C         23.00  27.72080    0.0        867.0    1.0       1.0\n",
       "           Q         30.00  12.35000    0.0        313.5    0.0       1.0\n",
       "           S         28.00  23.00000    0.0        581.0    0.0       1.0\n",
       "    3      C         15.00  14.45420    1.0        645.0    0.0       1.0\n",
       "           Q         22.00   7.75000    0.0        669.5    0.0       1.0\n",
       "           S         22.00  13.77500    0.0        568.0    1.0       0.0\n",
       "1   1      C         39.00  62.66875    0.0        690.5    0.0       0.0\n",
       "           Q         44.00  90.00000    0.0        246.0    2.0       0.0\n",
       "           S         42.00  35.50000    0.0        624.0    0.0       0.0\n",
       "    2      C         29.00  15.04580    0.0        818.0    0.0       0.0\n",
       "           Q         59.00  12.35000    0.0        908.0    0.0       0.0\n",
       "           S         29.00  13.00000    0.0        696.0    0.0       0.0\n",
       "    3      C         24.25   7.22920    0.0        747.5    0.0       0.0\n",
       "           Q         25.00   7.75000    0.0        704.0    0.0       0.0\n",
       "           S         25.00   8.05000    0.0        638.5    0.0       0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = combined.groupby(['Sex','Pclass','Embarked'])\n",
    "grouped.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By grouping by Sex, Pclass, and Embarked, we can see that the median ticket price paid by a third class male that departed from Southampton was $8.05. We'll use that value to fill in the missing fare:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            263\n",
       "Cabin            0\n",
       "Embarked         0\n",
       "Fare             0\n",
       "Name             0\n",
       "Parch            0\n",
       "PassengerId      0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Survived       418\n",
       "Ticket           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Fare = combined.Fare.fillna(8.05)\n",
    "combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have filled in values for everything except Age. From the swarmplot earlier, we know that Age is an important variable in determining whether or not a passenger survives (most male children survive, most adult males parish). Therefore, we should try our best to fill in age using all the information available to us. To do this we can use K nearest neighbors to impute the missing age values. In order to implement a KNN regressor, we first have to change our categorical variables into dummy variables (since that is what the model expects as an input). The code below gets dummy values for Cabin, Embarked, and Pclass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "col = [\"Cabin\",\"Embarked\",\"Pclass\"]\n",
    "combined = pd.get_dummies(combined,columns=col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final feature engineering I did was to combine the sibling/spouses (SibSp) aboard and the parent/children aboard (Parch) features into one \"Party Size\" feature. My thought was that families might have a greater chance of surviving than people traveling alone. Combining the two columns is pretty simple in Pandas as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Party_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare                                               Name  Parch  \\\n",
       "0  22.0   7.2500                            Braund, Mr. Owen Harris      0   \n",
       "1  38.0  71.2833  Cumings, Mrs. John Bradley (Florence Briggs Th...      0   \n",
       "2  26.0   7.9250                             Heikkinen, Miss. Laina      0   \n",
       "3  35.0  53.1000       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0   \n",
       "4  35.0   8.0500                           Allen, Mr. William Henry      0   \n",
       "\n",
       "   PassengerId  Sex  SibSp  Survived            Ticket  Cabin_A     ...      \\\n",
       "0            1    1      1       0.0         A/5 21171      0.0     ...       \n",
       "1            2    0      1       1.0          PC 17599      0.0     ...       \n",
       "2            3    0      0       1.0  STON/O2. 3101282      0.0     ...       \n",
       "3            4    0      1       1.0            113803      0.0     ...       \n",
       "4            5    1      0       0.0            373450      0.0     ...       \n",
       "\n",
       "   Cabin_G  Cabin_T  Cabin_U  Embarked_C  Embarked_Q  Embarked_S  Pclass_1  \\\n",
       "0      0.0      0.0      1.0         0.0         0.0         1.0       0.0   \n",
       "1      0.0      0.0      0.0         1.0         0.0         0.0       1.0   \n",
       "2      0.0      0.0      1.0         0.0         0.0         1.0       0.0   \n",
       "3      0.0      0.0      0.0         0.0         0.0         1.0       1.0   \n",
       "4      0.0      0.0      1.0         0.0         0.0         1.0       0.0   \n",
       "\n",
       "   Pclass_2  Pclass_3  Party_Size  \n",
       "0       0.0       1.0           2  \n",
       "1       0.0       0.0           2  \n",
       "2       0.0       1.0           1  \n",
       "3       0.0       0.0           2  \n",
       "4       0.0       1.0           1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[\"Party_Size\"] = combined[\"SibSp\"] + combined[\"Parch\"] + 1\n",
    "combined.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I then binned the party size variable into solo travelors (0-1], small parties (1-4], and large parties (4-12]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Party_Size</th>\n",
       "      <th>(0, 1]</th>\n",
       "      <th>(1, 4]</th>\n",
       "      <th>(4, 12]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare                                               Name  Parch  \\\n",
       "0  22.0   7.2500                            Braund, Mr. Owen Harris      0   \n",
       "1  38.0  71.2833  Cumings, Mrs. John Bradley (Florence Briggs Th...      0   \n",
       "2  26.0   7.9250                             Heikkinen, Miss. Laina      0   \n",
       "3  35.0  53.1000       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0   \n",
       "4  35.0   8.0500                           Allen, Mr. William Henry      0   \n",
       "\n",
       "   PassengerId  Sex  SibSp  Survived            Ticket  Cabin_A   ...     \\\n",
       "0            1    1      1       0.0         A/5 21171      0.0   ...      \n",
       "1            2    0      1       1.0          PC 17599      0.0   ...      \n",
       "2            3    0      0       1.0  STON/O2. 3101282      0.0   ...      \n",
       "3            4    0      1       1.0            113803      0.0   ...      \n",
       "4            5    1      0       0.0            373450      0.0   ...      \n",
       "\n",
       "   Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  Pclass_3  \\\n",
       "0         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "1         1.0         0.0         0.0       1.0       0.0       0.0   \n",
       "2         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "3         0.0         0.0         1.0       1.0       0.0       0.0   \n",
       "4         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "\n",
       "   Party_Size  (0, 1]  (1, 4]  (4, 12]  \n",
       "0           2     0.0     1.0      0.0  \n",
       "1           2     0.0     1.0      0.0  \n",
       "2           1     1.0     0.0      0.0  \n",
       "3           2     0.0     1.0      0.0  \n",
       "4           1     1.0     0.0      0.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_bins = [0,1,4,12]\n",
    "party_dummies = pd.get_dummies(pd.cut(combined[\"Party_Size\"], bins=party_bins))\n",
    "combined = pd.concat([combined, party_dummies], axis=1)\n",
    "combined.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Age', u'Fare', u'Name', u'Parch', u'PassengerId', u'Sex', u'SibSp',\n",
       "       u'Survived', u'Ticket', u'Cabin_A', u'Cabin_B', u'Cabin_C', u'Cabin_D',\n",
       "       u'Cabin_E', u'Cabin_F', u'Cabin_G', u'Cabin_T', u'Cabin_U',\n",
       "       u'Embarked_C', u'Embarked_Q', u'Embarked_S', u'Pclass_1', u'Pclass_2',\n",
       "       u'Pclass_3', u'Party_Size', u'Solo', u'SmallFam', u'LargeFam'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.rename(columns={'(0, 1]':'Solo', '(1, 4]':'SmallFam', '(4, 12]':'LargeFam'}, inplace = True)\n",
    "combined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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3mjdvntq0aaOMjAz98pe/dHu92NhYxcbGuoyFhoZ6b4cAAAAuQKaXxKCgIGVmZjZ4b8TU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\nyspScnKykpKSlJGRodLS0gb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MUM+ePTVo0CDNnj1blZWV9a9dtWqVunfvrq1bt+qmm25S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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d858c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Party_Size', y='Survived', capsize=.25, color=\"#222343\", errcolor=\"#bbca42\", data=combined)\n",
    "sns.despine(bottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're now ready to impute our null Age values. The code below splits the combined dataset into a train and test set based on whether or not the Age feature is null:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "AgeTrain = combined[combined['Age'].notnull()]\n",
    "AgeTest = combined[combined['Age'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>...</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Party_Size</th>\n",
       "      <th>Solo</th>\n",
       "      <th>SmallFam</th>\n",
       "      <th>LargeFam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>263.00000</td>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19.823319</td>\n",
       "      <td>0.243346</td>\n",
       "      <td>653.558935</td>\n",
       "      <td>0.703422</td>\n",
       "      <td>0.482890</td>\n",
       "      <td>0.293785</td>\n",
       "      <td>0.011407</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.030418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220532</td>\n",
       "      <td>0.277567</td>\n",
       "      <td>0.501901</td>\n",
       "      <td>0.148289</td>\n",
       "      <td>0.060837</td>\n",
       "      <td>0.790875</td>\n",
       "      <td>1.726236</td>\n",
       "      <td>0.760456</td>\n",
       "      <td>0.18251</td>\n",
       "      <td>0.057034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.550667</td>\n",
       "      <td>0.949941</td>\n",
       "      <td>380.161812</td>\n",
       "      <td>0.457620</td>\n",
       "      <td>1.448443</td>\n",
       "      <td>0.456787</td>\n",
       "      <td>0.106394</td>\n",
       "      <td>0.087038</td>\n",
       "      <td>0.172063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415396</td>\n",
       "      <td>0.448652</td>\n",
       "      <td>0.500950</td>\n",
       "      <td>0.356064</td>\n",
       "      <td>0.239486</td>\n",
       "      <td>0.407460</td>\n",
       "      <td>2.017376</td>\n",
       "      <td>0.427619</td>\n",
       "      <td>0.38700</td>\n",
       "      <td>0.232350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>335.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.804150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>999.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>227.525000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age        Fare       Parch  PassengerId         Sex       SibSp  \\\n",
       "count  0.0  263.000000  263.000000   263.000000  263.000000  263.000000   \n",
       "mean   NaN   19.823319    0.243346   653.558935    0.703422    0.482890   \n",
       "std    NaN   27.550667    0.949941   380.161812    0.457620    1.448443   \n",
       "min    NaN    0.000000    0.000000     6.000000    0.000000    0.000000   \n",
       "25%    NaN    7.750000    0.000000   335.500000    0.000000    0.000000   \n",
       "50%    NaN    8.050000    0.000000   630.000000    1.000000    0.000000   \n",
       "75%    NaN   22.804150    0.000000   999.500000    1.000000    0.000000   \n",
       "max    NaN  227.525000    9.000000  1309.000000    1.000000    8.000000   \n",
       "\n",
       "         Survived     Cabin_A     Cabin_B     Cabin_C     ...      Embarked_C  \\\n",
       "count  177.000000  263.000000  263.000000  263.000000     ...      263.000000   \n",
       "mean     0.293785    0.011407    0.007605    0.030418     ...        0.220532   \n",
       "std      0.456787    0.106394    0.087038    0.172063     ...        0.415396   \n",
       "min      0.000000    0.000000    0.000000    0.000000     ...        0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000     ...        0.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000     ...        0.000000   \n",
       "75%      1.000000    0.000000    0.000000    0.000000     ...        0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000     ...        1.000000   \n",
       "\n",
       "       Embarked_Q  Embarked_S    Pclass_1    Pclass_2    Pclass_3  Party_Size  \\\n",
       "count  263.000000  263.000000  263.000000  263.000000  263.000000  263.000000   \n",
       "mean     0.277567    0.501901    0.148289    0.060837    0.790875    1.726236   \n",
       "std      0.448652    0.500950    0.356064    0.239486    0.407460    2.017376   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    1.000000    1.000000   \n",
       "50%      0.000000    1.000000    0.000000    0.000000    1.000000    1.000000   \n",
       "75%      1.000000    1.000000    0.000000    0.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000   11.000000   \n",
       "\n",
       "             Solo   SmallFam    LargeFam  \n",
       "count  263.000000  263.00000  263.000000  \n",
       "mean     0.760456    0.18251    0.057034  \n",
       "std      0.427619    0.38700    0.232350  \n",
       "min      0.000000    0.00000    0.000000  \n",
       "25%      1.000000    0.00000    0.000000  \n",
       "50%      1.000000    0.00000    0.000000  \n",
       "75%      1.000000    0.00000    0.000000  \n",
       "max      1.000000    1.00000    1.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeTest.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I only want to use features that I believe would be predictive of a passenger's age. Therefore I dropped the columns below (note: I decided to use the Party Size feature instead of the binned solo, small fam, large fam features):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dropcolumns = [\"Name\",\"PassengerId\",\"Survived\",\"Ticket\",\"Age\", \"Solo\", \"SmallFam\", \"LargeFam\"]\n",
    "AgeTrainX = AgeTrain.drop(dropcolumns, axis=1)\n",
    "AgeTrainY = AgeTrain[\"Age\"]\n",
    "AgeTestX = AgeTest.drop(dropcolumns, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below imports and trains the K nearest neighbor regressor model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "AgeTrainX = preprocessing.scale(AgeTrainX)\n",
    "AgeTestX = preprocessing.scale(AgeTestX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=2, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "knnclass = neighbors.KNeighborsRegressor(n_neighbors=2)\n",
    "\n",
    "knnclass.fit(AgeTrainX,AgeTrainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predage = knnclass.predict(AgeTrainX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.median_absolute_error(AgeTrainY,predage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I first fit the model on AgeTrainX and AgeTrainY. This trains the model on all known entries with an Age Value. When I try to then \"predict\" the value of the known ages using the fitted model, there is a median error of 6 years. Not too shabby. The code below looks at the first ten predictions compared to the actual values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 19.5  36.5  26.5  26.5  27.5  50.5   4.   31.5  19.    2.5]\n",
      "[ 22.  38.  26.  35.  35.  54.   2.  27.  14.   4.]\n"
     ]
    }
   ],
   "source": [
    "print predage[:10]\n",
    "print np.array(AgeTrainY[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a fitted model, let's predict the ages of our test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29.75 ,  33.5  ,  27.5  ,  34.   ,  17.5  ,  19.   ,  31.5  ,\n",
       "        17.5  ,  34.   ,  34.   ,  29.75 ,  30.75 ,  17.5  ,  22.5  ,\n",
       "        51.   ,  55.5  ,  13.5  ,  19.   ,  29.75 ,  17.5  ,  29.75 ,\n",
       "        29.75 ,  19.   ,  19.   ,  24.5  ,  29.75 ,  19.   ,  18.5  ,\n",
       "        15.   ,  19.5  ,  29.75 ,  11.5  ,  30.5  ,  22.5  ,  17.5  ,\n",
       "        13.   ,  32.   ,  29.5  ,  24.5  ,  19.   ,  17.5  ,  11.5  ,\n",
       "        36.5  ,  19.   ,   5.5  ,  17.5  ,  14.75 ,  24.5  ,  19.5  ,\n",
       "        36.   ,  19.   ,  17.5  ,  22.5  ,  17.5  ,  21.5  ,  29.5  ,\n",
       "        55.5  ,  51.   ,  17.5  ,  22.5  ,  44.75 ,  29.75 ,  43.5  ,\n",
       "        11.5  ,  20.   ,  42.   ,  19.   ,  24.5  ,  51.   ,  34.   ,\n",
       "        17.5  ,  17.5  ,  30.75 ,  27.5  ,  17.5  ,  36.   ,  19.   ,\n",
       "        19.5  ,   5.5  ,  19.   ,  19.5  ,  21.5  ,  17.5  ,  34.   ,\n",
       "        19.5  ,  19.   ,  24.5  ,  29.75 ,  30.75 ,  29.75 ,  57.   ,\n",
       "        19.   ,  29.75 ,  21.5  ,  19.5  ,  19.5  ,  29.5  ,  21.5  ,\n",
       "         5.5  ,  30.75 ,  18.5  ,  35.5  ,  17.5  ,  22.5  ,  29.75 ,\n",
       "        29.   ,  34.   ,  34.   ,  48.   ,  34.   ,  15.   ,  35.5  ,\n",
       "        32.   ,  19.   ,  40.   ,  19.   ,  29.75 ,  17.5  ,  34.   ,\n",
       "        17.5  ,  14.75 ,  18.5  ,  29.75 ,  31.5  ,  19.5  ,  34.   ,\n",
       "        19.   ,  22.5  ,  19.5  ,  24.5  ,  19.   ,  19.   ,  43.5  ,\n",
       "        30.75 ,  29.   ,  19.5  ,  19.   ,  17.5  ,  19.   ,  19.   ,\n",
       "        35.   ,  21.5  ,  17.5  ,  29.   ,  17.5  ,  13.5  ,  51.   ,\n",
       "        35.5  ,  17.5  ,  21.5  ,  19.   ,  19.   ,  54.   ,  35.5  ,\n",
       "        58.5  ,  30.75 ,  34.   ,  22.   ,  19.   ,  13.5  ,  19.   ,\n",
       "        13.   ,  58.5  ,  44.5  ,  19.5  ,  29.   ,  19.   ,  34.   ,\n",
       "        29.75 ,  41.   ,  11.5  ,  22.   ,  34.   ,  13.   ,  24.75 ,\n",
       "        19.   ,  16.585,  19.   ,  23.5  ,  22.5  ,  16.585,  17.5  ,\n",
       "        29.   ,  54.   ,  19.   ,  32.   ,  30.75 ,  19.5  ,  29.75 ,\n",
       "        19.   ,  43.5  ,  22.5  ,  17.5  ,  19.   ,  29.75 ,  19.   ,\n",
       "        19.   ,  29.75 ,  17.5  ,  34.   ,  36.5  ,  19.   ,  20.   ,\n",
       "        38.5  ,  15.   ,  50.5  ,  22.5  ,  34.   ,  17.5  ,  19.5  ,\n",
       "        42.5  ,  19.5  ,  34.   ,  19.   ,  13.   ,  22.5  ,  17.5  ,\n",
       "        13.5  ,  55.5  ,  19.5  ,  17.5  ,  29.75 ,  15.   ,  17.5  ,\n",
       "        19.   ,  19.   ,  13.5  ,  14.75 ,  19.5  ,  19.   ,  19.   ,\n",
       "        43.5  ,  19.5  ,  17.5  ,  19.   ,  24.5  ,  34.   ,  17.5  ,\n",
       "        19.5  ,  25.   ,  29.75 ,  22.5  ,  34.   ,  22.5  ,  28.   ,\n",
       "        17.5  ,  19.5  ,  34.   ,  34.   ,  41.5  ,  34.5  ,  19.   ,\n",
       "        19.   ,  41.5  ,  22.5  ,  19.   ,  17.5  ,  33.5  ,  17.5  ,\n",
       "        17.5  ,  29.75 ,  29.75 ,  13.5  ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputedage = knnclass.predict(AgeTestX)\n",
    "imputedage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "AgeTest[\"Age\"] = imputedage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Party_Size</th>\n",
       "      <th>Solo</th>\n",
       "      <th>SmallFam</th>\n",
       "      <th>LargeFam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>33.50</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>244373</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>27.50</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>330959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31.50</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Kraeff, Mr. Theodor</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349253</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Rogers, Mr. William John</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>S.C./A.4. 23567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>30.75</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>Lennon, Mr. Denis</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>O'Driscoll, Miss. Bridget</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>22.50</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>Samaan, Mr. Youssef</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>51.00</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>55.50</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>Stewart, Mr. Albert A</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17605</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>13.50</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>1</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Staneff, Mr. Ivan</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349208</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Moutal, Mr. Rahamin Haim</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>374746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7875</td>\n",
       "      <td>McDermott, Miss. Brigdet Delia</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>330932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Slocovski, Mr. Selman Francis</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SOTON/OQ 392086</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Shorney, Mr. Charles Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>374910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Petroff, Mr. Pastcho (\"Pentcho\")</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Moss, Mr. Albert Johan</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>24.50</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>Moran, Miss. Bertha</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>371110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Moore, Mr. Leonard Charles</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A4. 54510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>McMahon, Mr. Martin</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>18.50</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Miss. Anna</td>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>15.00</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>2</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2678</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>19.50</td>\n",
       "      <td>7.3125</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>17.50</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Howard, Miss. May Elizabeth</td>\n",
       "      <td>0</td>\n",
       "      <td>1160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A. 2. 39186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Fox, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>1163</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>24.50</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>Lennon, Miss. Mary</td>\n",
       "      <td>0</td>\n",
       "      <td>1165</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>370371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Saade, Mr. Jean Nassr</td>\n",
       "      <td>0</td>\n",
       "      <td>1166</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Fleming, Miss. Honora</td>\n",
       "      <td>0</td>\n",
       "      <td>1174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>19.50</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Franklin, Mr. Charles (Charles Fardon)</td>\n",
       "      <td>0</td>\n",
       "      <td>1178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>25.00</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Mardirosian, Mr. Sarkis</td>\n",
       "      <td>0</td>\n",
       "      <td>1180</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ford, Mr. Arthur</td>\n",
       "      <td>0</td>\n",
       "      <td>1181</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A/5 1478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>22.50</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>Rheims, Mr. George Alexander Lucien</td>\n",
       "      <td>0</td>\n",
       "      <td>1182</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17607</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Nasr, Mr. Mustafa</td>\n",
       "      <td>0</td>\n",
       "      <td>1184</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2652</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>22.50</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>Samaan, Mr. Hanna</td>\n",
       "      <td>0</td>\n",
       "      <td>1189</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>28.00</td>\n",
       "      <td>15.0458</td>\n",
       "      <td>Malachard, Mr. Noel</td>\n",
       "      <td>0</td>\n",
       "      <td>1193</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>McCarthy, Miss. Catherine Katie\"\"</td>\n",
       "      <td>0</td>\n",
       "      <td>1196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>19.50</td>\n",
       "      <td>7.5750</td>\n",
       "      <td>Sadowitz, Mr. Harry</td>\n",
       "      <td>0</td>\n",
       "      <td>1204</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LP 1588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Thomas, Mr. Tannous</td>\n",
       "      <td>0</td>\n",
       "      <td>1224</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>34.00</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>Betros, Master. Seman</td>\n",
       "      <td>0</td>\n",
       "      <td>1231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>41.50</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>Sage, Mr. John George</td>\n",
       "      <td>9</td>\n",
       "      <td>1234</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>34.50</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>van Billiard, Master. James William</td>\n",
       "      <td>1</td>\n",
       "      <td>1236</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Lockyer, Mr. Edward</td>\n",
       "      <td>0</td>\n",
       "      <td>1249</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>O'Keefe, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>1250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>368402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>41.50</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>Sage, Mrs. John (Annie Bullen)</td>\n",
       "      <td>9</td>\n",
       "      <td>1257</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>22.50</td>\n",
       "      <td>14.4583</td>\n",
       "      <td>Caram, Mr. Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>1258</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>19.00</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>O'Connor, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>1272</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366713</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>17.50</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>Risien, Mrs. Samuel (Emma)</td>\n",
       "      <td>0</td>\n",
       "      <td>1274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>33.50</td>\n",
       "      <td>12.8750</td>\n",
       "      <td>Wheeler, Mr. Edwin Frederick\"\"</td>\n",
       "      <td>0</td>\n",
       "      <td>1276</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SC/PARIS 2159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>17.50</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>0</td>\n",
       "      <td>1302</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>13.50</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age      Fare                                            Name  Parch  \\\n",
       "5    29.75    8.4583                                Moran, Mr. James      0   \n",
       "17   33.50   13.0000                    Williams, Mr. Charles Eugene      0   \n",
       "19   27.50    7.2250                         Masselmani, Mrs. Fatima      0   \n",
       "26   34.00    7.2250                         Emir, Mr. Farred Chehab      0   \n",
       "28   17.50    7.8792                   O'Dwyer, Miss. Ellen \"Nellie\"      0   \n",
       "29   19.00    7.8958                             Todoroff, Mr. Lalio      0   \n",
       "31   31.50  146.5208  Spencer, Mrs. William Augustus (Marie Eugenie)      0   \n",
       "32   17.50    7.7500                        Glynn, Miss. Mary Agatha      0   \n",
       "36   34.00    7.2292                                Mamee, Mr. Hanna      0   \n",
       "42   34.00    7.8958                             Kraeff, Mr. Theodor      0   \n",
       "45   29.75    8.0500                        Rogers, Mr. William John      0   \n",
       "46   30.75   15.5000                               Lennon, Mr. Denis      0   \n",
       "47   17.50    7.7500                       O'Driscoll, Miss. Bridget      0   \n",
       "48   22.50   21.6792                             Samaan, Mr. Youssef      0   \n",
       "55   51.00   35.5000                               Woolner, Mr. Hugh      0   \n",
       "64   55.50   27.7208                           Stewart, Mr. Albert A      0   \n",
       "65   13.50   15.2458                        Moubarek, Master. Gerios      1   \n",
       "76   19.00    7.8958                               Staneff, Mr. Ivan      0   \n",
       "77   29.75    8.0500                        Moutal, Mr. Rahamin Haim      0   \n",
       "82   17.50    7.7875                  McDermott, Miss. Brigdet Delia      0   \n",
       "87   29.75    8.0500                   Slocovski, Mr. Selman Francis      0   \n",
       "95   29.75    8.0500                     Shorney, Mr. Charles Joseph      0   \n",
       "101  19.00    7.8958                Petroff, Mr. Pastcho (\"Pentcho\")      0   \n",
       "107  19.00    7.7750                          Moss, Mr. Albert Johan      0   \n",
       "109  24.50   24.1500                             Moran, Miss. Bertha      0   \n",
       "121  29.75    8.0500                      Moore, Mr. Leonard Charles      0   \n",
       "126  19.00    7.7500                             McMahon, Mr. Martin      0   \n",
       "128  18.50   22.3583                               Peter, Miss. Anna      1   \n",
       "140  15.00   15.2458                   Boulos, Mrs. Joseph (Sultana)      2   \n",
       "154  19.50    7.3125                           Olsen, Mr. Ole Martin      0   \n",
       "..     ...       ...                                             ...    ...   \n",
       "268  17.50    8.0500                     Howard, Miss. May Elizabeth      0   \n",
       "271  19.00    7.7500                                Fox, Mr. Patrick      0   \n",
       "273  24.50   15.5000                              Lennon, Miss. Mary      0   \n",
       "274  34.00    7.2250                           Saade, Mr. Jean Nassr      0   \n",
       "282  17.50    7.7500                           Fleming, Miss. Honora      0   \n",
       "286  19.50    7.2500          Franklin, Mr. Charles (Charles Fardon)      0   \n",
       "288  25.00    7.2292                         Mardirosian, Mr. Sarkis      0   \n",
       "289  29.75    8.0500                                Ford, Mr. Arthur      0   \n",
       "290  22.50   39.6000             Rheims, Mr. George Alexander Lucien      0   \n",
       "292  34.00    7.2292                               Nasr, Mr. Mustafa      0   \n",
       "297  22.50   21.6792                               Samaan, Mr. Hanna      0   \n",
       "301  28.00   15.0458                             Malachard, Mr. Noel      0   \n",
       "304  17.50    7.7500               McCarthy, Miss. Catherine Katie\"\"      0   \n",
       "312  19.50    7.5750                             Sadowitz, Mr. Harry      0   \n",
       "332  34.00    7.2250                             Thomas, Mr. Tannous      0   \n",
       "339  34.00    7.2292                           Betros, Master. Seman      0   \n",
       "342  41.50   69.5500                           Sage, Mr. John George      9   \n",
       "344  34.50   14.5000             van Billiard, Master. James William      1   \n",
       "357  19.00    7.8792                             Lockyer, Mr. Edward      0   \n",
       "358  19.00    7.7500                            O'Keefe, Mr. Patrick      0   \n",
       "365  41.50   69.5500                  Sage, Mrs. John (Annie Bullen)      9   \n",
       "366  22.50   14.4583                               Caram, Mr. Joseph      0   \n",
       "380  19.00    7.7500                           O'Connor, Mr. Patrick      0   \n",
       "382  17.50   14.5000                      Risien, Mrs. Samuel (Emma)      0   \n",
       "384  33.50   12.8750                  Wheeler, Mr. Edwin Frederick\"\"      0   \n",
       "408  17.50    7.7208                 Riordan, Miss. Johanna Hannah\"\"      0   \n",
       "410  17.50    7.7500                          Naughton, Miss. Hannah      0   \n",
       "413  29.75    8.0500                              Spector, Mr. Woolf      0   \n",
       "416  29.75    8.0500                             Ware, Mr. Frederick      0   \n",
       "417  13.50   22.3583                        Peter, Master. Michael J      1   \n",
       "\n",
       "     PassengerId  Sex  SibSp  Survived              Ticket  Cabin_A    ...     \\\n",
       "5              6    1      0       0.0              330877      0.0    ...      \n",
       "17            18    1      0       1.0              244373      0.0    ...      \n",
       "19            20    0      0       1.0                2649      0.0    ...      \n",
       "26            27    1      0       0.0                2631      0.0    ...      \n",
       "28            29    0      0       1.0              330959      0.0    ...      \n",
       "29            30    1      0       0.0              349216      0.0    ...      \n",
       "31            32    0      1       1.0            PC 17569      0.0    ...      \n",
       "32            33    0      0       1.0              335677      0.0    ...      \n",
       "36            37    1      0       1.0                2677      0.0    ...      \n",
       "42            43    1      0       0.0              349253      0.0    ...      \n",
       "45            46    1      0       0.0     S.C./A.4. 23567      0.0    ...      \n",
       "46            47    1      1       0.0              370371      0.0    ...      \n",
       "47            48    0      0       1.0               14311      0.0    ...      \n",
       "48            49    1      2       0.0                2662      0.0    ...      \n",
       "55            56    1      0       1.0               19947      0.0    ...      \n",
       "64            65    1      0       0.0            PC 17605      0.0    ...      \n",
       "65            66    1      1       1.0                2661      0.0    ...      \n",
       "76            77    1      0       0.0              349208      0.0    ...      \n",
       "77            78    1      0       0.0              374746      0.0    ...      \n",
       "82            83    0      0       1.0              330932      0.0    ...      \n",
       "87            88    1      0       0.0     SOTON/OQ 392086      0.0    ...      \n",
       "95            96    1      0       0.0              374910      0.0    ...      \n",
       "101          102    1      0       0.0              349215      0.0    ...      \n",
       "107          108    1      0       1.0              312991      0.0    ...      \n",
       "109          110    0      1       1.0              371110      0.0    ...      \n",
       "121          122    1      0       0.0           A4. 54510      0.0    ...      \n",
       "126          127    1      0       0.0              370372      0.0    ...      \n",
       "128          129    0      1       1.0                2668      0.0    ...      \n",
       "140          141    0      0       0.0                2678      0.0    ...      \n",
       "154          155    1      0       0.0           Fa 265302      0.0    ...      \n",
       "..           ...  ...    ...       ...                 ...      ...    ...      \n",
       "268         1160    0      0       NaN         A. 2. 39186      0.0    ...      \n",
       "271         1163    1      0       NaN              368573      0.0    ...      \n",
       "273         1165    0      1       NaN              370371      0.0    ...      \n",
       "274         1166    1      0       NaN                2676      0.0    ...      \n",
       "282         1174    0      0       NaN              364859      0.0    ...      \n",
       "286         1178    1      0       NaN  SOTON/O.Q. 3101314      0.0    ...      \n",
       "288         1180    1      0       NaN                2655      0.0    ...      \n",
       "289         1181    1      0       NaN            A/5 1478      0.0    ...      \n",
       "290         1182    1      0       NaN            PC 17607      0.0    ...      \n",
       "292         1184    1      0       NaN                2652      0.0    ...      \n",
       "297         1189    1      2       NaN                2662      0.0    ...      \n",
       "301         1193    1      0       NaN              237735      0.0    ...      \n",
       "304         1196    0      0       NaN              383123      0.0    ...      \n",
       "312         1204    1      0       NaN             LP 1588      0.0    ...      \n",
       "332         1224    1      0       NaN                2684      0.0    ...      \n",
       "339         1231    1      0       NaN                2622      0.0    ...      \n",
       "342         1234    1      1       NaN            CA. 2343      0.0    ...      \n",
       "344         1236    1      1       NaN            A/5. 851      0.0    ...      \n",
       "357         1249    1      0       NaN                1222      0.0    ...      \n",
       "358         1250    1      0       NaN              368402      0.0    ...      \n",
       "365         1257    0      1       NaN            CA. 2343      0.0    ...      \n",
       "366         1258    1      1       NaN                2689      0.0    ...      \n",
       "380         1272    1      0       NaN              366713      0.0    ...      \n",
       "382         1274    0      0       NaN              364498      0.0    ...      \n",
       "384         1276    1      0       NaN       SC/PARIS 2159      0.0    ...      \n",
       "408         1300    0      0       NaN              334915      0.0    ...      \n",
       "410         1302    0      0       NaN              365237      0.0    ...      \n",
       "413         1305    1      0       NaN           A.5. 3236      0.0    ...      \n",
       "416         1308    1      0       NaN              359309      0.0    ...      \n",
       "417         1309    1      1       NaN                2668      0.0    ...      \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  Pclass_3  \\\n",
       "5           0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "17          0.0         0.0         1.0       0.0       1.0       0.0   \n",
       "19          1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "26          1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "28          0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "29          0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "31          1.0         0.0         0.0       1.0       0.0       0.0   \n",
       "32          0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "36          1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "42          1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "45          0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "46          0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "47          0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "48          1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "55          0.0         0.0         1.0       1.0       0.0       0.0   \n",
       "64          1.0         0.0         0.0       1.0       0.0       0.0   \n",
       "65          1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "76          0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "77          0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "82          0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "87          0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "95          0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "101         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "107         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "109         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "121         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "126         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "128         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "140         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "154         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "..          ...         ...         ...       ...       ...       ...   \n",
       "268         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "271         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "273         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "274         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "282         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "286         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "288         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "289         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "290         0.0         0.0         1.0       1.0       0.0       0.0   \n",
       "292         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "297         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "301         1.0         0.0         0.0       0.0       1.0       0.0   \n",
       "304         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "312         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "332         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "339         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "342         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "344         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "357         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "358         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "365         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "366         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "380         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "382         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "384         0.0         0.0         1.0       0.0       1.0       0.0   \n",
       "408         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "410         0.0         1.0         0.0       0.0       0.0       1.0   \n",
       "413         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "416         0.0         0.0         1.0       0.0       0.0       1.0   \n",
       "417         1.0         0.0         0.0       0.0       0.0       1.0   \n",
       "\n",
       "     Party_Size  Solo  SmallFam  LargeFam  \n",
       "5             1   1.0       0.0       0.0  \n",
       "17            1   1.0       0.0       0.0  \n",
       "19            1   1.0       0.0       0.0  \n",
       "26            1   1.0       0.0       0.0  \n",
       "28            1   1.0       0.0       0.0  \n",
       "29            1   1.0       0.0       0.0  \n",
       "31            2   0.0       1.0       0.0  \n",
       "32            1   1.0       0.0       0.0  \n",
       "36            1   1.0       0.0       0.0  \n",
       "42            1   1.0       0.0       0.0  \n",
       "45            1   1.0       0.0       0.0  \n",
       "46            2   0.0       1.0       0.0  \n",
       "47            1   1.0       0.0       0.0  \n",
       "48            3   0.0       1.0       0.0  \n",
       "55            1   1.0       0.0       0.0  \n",
       "64            1   1.0       0.0       0.0  \n",
       "65            3   0.0       1.0       0.0  \n",
       "76            1   1.0       0.0       0.0  \n",
       "77            1   1.0       0.0       0.0  \n",
       "82            1   1.0       0.0       0.0  \n",
       "87            1   1.0       0.0       0.0  \n",
       "95            1   1.0       0.0       0.0  \n",
       "101           1   1.0       0.0       0.0  \n",
       "107           1   1.0       0.0       0.0  \n",
       "109           2   0.0       1.0       0.0  \n",
       "121           1   1.0       0.0       0.0  \n",
       "126           1   1.0       0.0       0.0  \n",
       "128           3   0.0       1.0       0.0  \n",
       "140           3   0.0       1.0       0.0  \n",
       "154           1   1.0       0.0       0.0  \n",
       "..          ...   ...       ...       ...  \n",
       "268           1   1.0       0.0       0.0  \n",
       "271           1   1.0       0.0       0.0  \n",
       "273           2   0.0       1.0       0.0  \n",
       "274           1   1.0       0.0       0.0  \n",
       "282           1   1.0       0.0       0.0  \n",
       "286           1   1.0       0.0       0.0  \n",
       "288           1   1.0       0.0       0.0  \n",
       "289           1   1.0       0.0       0.0  \n",
       "290           1   1.0       0.0       0.0  \n",
       "292           1   1.0       0.0       0.0  \n",
       "297           3   0.0       1.0       0.0  \n",
       "301           1   1.0       0.0       0.0  \n",
       "304           1   1.0       0.0       0.0  \n",
       "312           1   1.0       0.0       0.0  \n",
       "332           1   1.0       0.0       0.0  \n",
       "339           1   1.0       0.0       0.0  \n",
       "342          11   0.0       0.0       1.0  \n",
       "344           3   0.0       1.0       0.0  \n",
       "357           1   1.0       0.0       0.0  \n",
       "358           1   1.0       0.0       0.0  \n",
       "365          11   0.0       0.0       1.0  \n",
       "366           2   0.0       1.0       0.0  \n",
       "380           1   1.0       0.0       0.0  \n",
       "382           1   1.0       0.0       0.0  \n",
       "384           1   1.0       0.0       0.0  \n",
       "408           1   1.0       0.0       0.0  \n",
       "410           1   1.0       0.0       0.0  \n",
       "413           1   1.0       0.0       0.0  \n",
       "416           1   1.0       0.0       0.0  \n",
       "417           3   0.0       1.0       0.0  \n",
       "\n",
       "[263 rows x 28 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgeTest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've filled in the missing values now we have to combine the test data back in with the training data, sort by passenger ID and then split the data back into the original training and test sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finalcombined = AgeTrain.append(AgeTest).sort_values(by='PassengerId',ascending=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              0\n",
       "Fare             0\n",
       "Name             0\n",
       "Parch            0\n",
       "PassengerId      0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Survived       418\n",
       "Ticket           0\n",
       "Cabin_A          0\n",
       "Cabin_B          0\n",
       "Cabin_C          0\n",
       "Cabin_D          0\n",
       "Cabin_E          0\n",
       "Cabin_F          0\n",
       "Cabin_G          0\n",
       "Cabin_T          0\n",
       "Cabin_U          0\n",
       "Embarked_C       0\n",
       "Embarked_Q       0\n",
       "Embarked_S       0\n",
       "Pclass_1         0\n",
       "Pclass_2         0\n",
       "Pclass_3         0\n",
       "Party_Size       0\n",
       "Solo             0\n",
       "SmallFam         0\n",
       "LargeFam         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalcombined.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Party_Size</th>\n",
       "      <th>Solo</th>\n",
       "      <th>SmallFam</th>\n",
       "      <th>LargeFam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>39.00</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>38.50</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>29.75</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>13.50</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age      Fare                          Name  Parch  PassengerId  Sex  \\\n",
       "413  29.75    8.0500            Spector, Mr. Woolf      0         1305    1   \n",
       "414  39.00  108.9000  Oliva y Ocana, Dona. Fermina      0         1306    0   \n",
       "415  38.50    7.2500  Saether, Mr. Simon Sivertsen      0         1307    1   \n",
       "416  29.75    8.0500           Ware, Mr. Frederick      0         1308    1   \n",
       "417  13.50   22.3583      Peter, Master. Michael J      1         1309    1   \n",
       "\n",
       "     SibSp  Survived              Ticket  Cabin_A    ...     Embarked_C  \\\n",
       "413      0       NaN           A.5. 3236      0.0    ...            0.0   \n",
       "414      0       NaN            PC 17758      0.0    ...            1.0   \n",
       "415      0       NaN  SOTON/O.Q. 3101262      0.0    ...            0.0   \n",
       "416      0       NaN              359309      0.0    ...            0.0   \n",
       "417      1       NaN                2668      0.0    ...            1.0   \n",
       "\n",
       "     Embarked_Q  Embarked_S  Pclass_1  Pclass_2  Pclass_3  Party_Size  Solo  \\\n",
       "413         0.0         1.0       0.0       0.0       1.0           1   1.0   \n",
       "414         0.0         0.0       1.0       0.0       0.0           1   1.0   \n",
       "415         0.0         1.0       0.0       0.0       1.0           1   1.0   \n",
       "416         0.0         1.0       0.0       0.0       1.0           1   1.0   \n",
       "417         0.0         0.0       0.0       0.0       1.0           3   0.0   \n",
       "\n",
       "     SmallFam  LargeFam  \n",
       "413       0.0       0.0  \n",
       "414       0.0       0.0  \n",
       "415       0.0       0.0  \n",
       "416       0.0       0.0  \n",
       "417       1.0       0.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinalTrain = finalcombined[finalcombined['Survived'].notnull()]\n",
    "FinalTest = finalcombined[finalcombined['Survived'].isnull()]\n",
    "FinalTest.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Random Forest Classifier\n",
    "\n",
    "Alright, now the exciting part. We have cleaned up our dataset and are ready to train our first classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validation scores are: [ 0.75757576  0.82828283  0.81481481]\n",
      "Precision: 0.740853658537\n",
      "Recall: 0.710526315789\n",
      "The F1 score is: 0.725373134328\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.cross_validation import cross_val_score, cross_val_predict\n",
    "\n",
    "dropcolumns = [\"Name\",\"PassengerId\",\"Survived\",\"Ticket\", \"Party_Size\"]\n",
    "FinalTrainX = FinalTrain.drop(dropcolumns, axis=1)\n",
    "FinalTrainY = FinalTrain[\"Survived\"]\n",
    "FinalTestX = FinalTest.drop(dropcolumns, axis=1)\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=1000)\n",
    "print\"The cross validation scores are: {0}\".format(cross_val_score(rfc, FinalTrainX, FinalTrainY))\n",
    "predicted = cross_val_predict(rfc, FinalTrainX, FinalTrainY)\n",
    "print \"Precision: {0}\".format(metrics.precision_score(FinalTrainY, predicted))\n",
    "print \"Recall: {0}\".format(metrics.recall_score(FinalTrainY, predicted))\n",
    "print \"The F1 score is: {0}\".format(metrics.f1_score(FinalTrainY, predicted))\n",
    "\n",
    "trained_rfc = rfc.fit(FinalTrainX,FinalTrainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Age', u'Fare', u'Parch', u'Sex', u'SibSp', u'Cabin_A', u'Cabin_B',\n",
      "       u'Cabin_C', u'Cabin_D', u'Cabin_E', u'Cabin_F', u'Cabin_G', u'Cabin_T',\n",
      "       u'Cabin_U', u'Embarked_C', u'Embarked_Q', u'Embarked_S', u'Pclass_1',\n",
      "       u'Pclass_2', u'Pclass_3', u'Solo', u'SmallFam', u'LargeFam'],\n",
      "      dtype='object')\n",
      "\n",
      "Feature ranking:\n",
      "1. feature 0 (0.264983)\n",
      "2. feature 1 (0.228558)\n",
      "3. feature 3 (0.220372)\n",
      "4. feature 19 (0.037564)\n",
      "5. feature 13 (0.031229)\n",
      "6. feature 2 (0.029462)\n",
      "7. feature 4 (0.027172)\n",
      "8. feature 21 (0.023452)\n",
      "9. feature 17 (0.020682)\n",
      "10. feature 16 (0.015897)\n",
      "11. feature 18 (0.014287)\n",
      "12. feature 14 (0.012664)\n",
      "13. feature 22 (0.012559)\n",
      "14. feature 20 (0.012379)\n",
      "15. feature 9 (0.009731)\n",
      "16. feature 15 (0.008369)\n",
      "17. feature 6 (0.008101)\n",
      "18. feature 7 (0.007124)\n",
      "19. feature 8 (0.006985)\n",
      "20. feature 5 (0.003449)\n",
      "21. feature 10 (0.003236)\n",
      "22. feature 11 (0.001214)\n",
      "23. feature 12 (0.000531)\n"
     ]
    }
   ],
   "source": [
    "importances = trained_rfc.feature_importances_\n",
    "std = np.std([trained_rfc.feature_importances_ for tree in trained_rfc.estimators_],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print FinalTrainX.columns\n",
    "print \n",
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "for f in range(FinalTrainX.shape[1]):\n",
    "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Tsurvived = trained_rfc.predict(FinalTestX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tsurvived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         1\n",
       "4          896         0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalresults = pd.DataFrame(FinalTest[\"PassengerId\"])\n",
    "finalresults[\"Survived\"] = Tsurvived.astype(int)\n",
    "finalresults.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#finalresults.to_csv(\"/Users/jreinhart/Desktop/Data Analysis Github/Titanic Kaggle/PartySize.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scored .75120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
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0BllNkAnmX5XeoOWvi68CIJf98/RZECzTAN7SUCjkTJo4knfe/Ry9Xo/SSqDngHiOXngF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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c100950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelresults = FinalTest\n",
    "modelresults[\"Survived\"] = Tsurvived\n",
    "sns.factorplot(x=\"Sex\", y=\"Age\", hue=\"Survived\", kind=\"swarm\", data=modelresults)\n",
    "sns.despine(bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c6DXoz+/9b/Weu9dt8MTJC0yfYnWhcXR04LNP/4/Jk+cIuX4ia4fj5+/Nb78d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\nD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11db17ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"Sex\", y=\"Age\", hue=\"Survived\", kind=\"swarm\", data=traindf, order=['female','male'])\n",
    "sns.despine(bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.49729484, -0.50244517, -0.47367361, ..., -1.2316449 ,\n",
       "         1.4322604 , -0.2734756 ],\n",
       "       [ 0.65162818,  0.78684529, -0.47367361, ..., -1.2316449 ,\n",
       "         1.4322604 , -0.2734756 ],\n",
       "       [-0.21006408, -0.48885426, -0.47367361, ...,  0.81192233,\n",
       "        -0.69819706, -0.2734756 ],\n",
       "       ..., \n",
       "       [-0.88613347, -0.17626324,  2.00893337, ..., -1.2316449 ,\n",
       "         1.4322604 , -0.2734756 ],\n",
       "       [-0.21006408, -0.04438104, -0.47367361, ...,  0.81192233,\n",
       "        -0.69819706, -0.2734756 ],\n",
       "       [ 0.22078205, -0.49237783, -0.47367361, ...,  0.81192233,\n",
       "        -0.69819706, -0.2734756 ]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledTrainX = preprocessing.scale(FinalTrainX)\n",
    "scaledTestX = preprocessing.scale(FinalTestX)\n",
    "scaledTrainX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.792717086835\n",
      "0.770308123249\n",
      "0.764705882353\n",
      "0.803921568627\n",
      "The cross validation scores are: [ 0.78787879  0.81481481  0.80808081]\n",
      "Precision: 0.75786163522\n",
      "Recall: 0.704678362573\n",
      "The F1 score is: 0.730303030303\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "C = 1  # SVM regularization parameter\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaledTrainX, FinalTrainY, test_size=0.4, random_state=0)\n",
    "\n",
    "svc = svm.SVC(kernel='linear', C=C).fit(X_train, y_train)\n",
    "rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X_train, y_train)\n",
    "poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X_train, y_train)\n",
    "lin_svc = svm.LinearSVC(C=C).fit(X_train, y_train)\n",
    "\n",
    "print svc.score(X_test, y_test)\n",
    "print rbf_svc.score(X_test, y_test)\n",
    "print poly_svc.score(X_test, y_test)\n",
    "print lin_svc.score(X_test, y_test)\n",
    "\n",
    "print\"The cross validation scores are: {0}\".format(cross_val_score(lin_svc, scaledTrainX, FinalTrainY))\n",
    "lsvc_predicted = cross_val_predict(lin_svc, scaledTrainX, FinalTrainY)\n",
    "print \"Precision: {0}\".format(metrics.precision_score(FinalTrainY, lsvc_predicted))\n",
    "print \"Recall: {0}\".format(metrics.recall_score(FinalTrainY, lsvc_predicted))\n",
    "print \"The F1 score is: {0}\".format(metrics.f1_score(FinalTrainY, lsvc_predicted))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "        1.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  1.,  1.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,\n",
       "        0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  1.,  0.,  1.,  1.,\n",
       "        0.,  0.,  0.,  1.,  1.,  1.,  0.,  0.,  1.,  0.,  1.,  1.,  0.,\n",
       "        1.,  0.,  0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  1.,  1.,  1.,\n",
       "        0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  1.,  0.,  0.,  0.,  0.,\n",
       "        0.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,  1.,  0.,  1.,\n",
       "        0.,  0.,  1.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,  0.,\n",
       "        0.,  0.])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_svc = svm.LinearSVC(C=C).fit(scaledTrainX, FinalTrainY)\n",
    "lin_svcpred = lin_svc.predict(scaledTestX)\n",
    "lin_svcpred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svcresults = pd.DataFrame(FinalTest[\"PassengerId\"])\n",
    "svcresults[\"Survived\"] = lin_svcpred.astype(int)\n",
    "svcresults.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svcresults.to_csv(\"/Users/jreinhart/Desktop/Data Analysis Github/Titanic Kaggle/svcResults.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scored .75598"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from tpot import TPOTClassifier\n",
    "\n",
    "pipeline_optimizer = TPOTClassifier(generations=50, population_size=25, num_cv_folds=5, random_state=42, verbosity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   2%|▏         | 25/1275 [00:02<33:28,  1.61s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.799677082422\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   4%|▍         | 48/1275 [00:00<32:49,  1.60s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.808901681353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   6%|▌         | 71/1275 [00:00<50:08,  2.50s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3 - Current best internal CV score: 0.808901681353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   8%|▊         | 97/1275 [00:00<1:13:03,  3.72s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4 - Current best internal CV score: 0.809810772262\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:   9%|▉         | 120/1275 [00:00<26:16,  1.37s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5 - Current best internal CV score: 0.809810772262\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  12%|█▏        | 148/1275 [00:00<26:21,  1.40s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 6 - Current best internal CV score: 0.809810772262\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  14%|█▎        | 174/1275 [00:00<20:13,  1.10s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 7 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  16%|█▌        | 201/1275 [04:57<19:14,  1.07s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 8 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  18%|█▊        | 224/1275 [00:00<33:07,  1.89s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  19%|█▉        | 248/1275 [00:00<50:03,  2.92s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 10 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  21%|██▏       | 273/1275 [00:00<22:52,  1.37s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 11 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  23%|██▎       | 296/1275 [00:00<22:08,  1.36s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 12 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
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     "output_type": "stream",
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      "GP Progress:  25%|██▌       | 323/1275 [00:00<21:20,  1.34s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 13 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
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      "GP Progress:  27%|██▋       | 348/1275 [00:00<25:29,  1.65s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 14 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "GP Progress:  29%|██▉       | 370/1275 [00:00<21:07,  1.40s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 15 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "GP Progress:  31%|███       | 397/1275 [00:00<35:23,  2.42s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 16 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "GP Progress:  33%|███▎      | 423/1275 [00:00<28:48,  2.03s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 17 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "GP Progress:  35%|███▌      | 448/1275 [00:00<34:20,  2.49s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 18 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "GP Progress:  37%|███▋      | 472/1275 [00:00<37:07,  2.77s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 19 - Current best internal CV score: 0.815118250005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  39%|███▉      | 498/1275 [00:00<20:31,  1.58s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 20 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "GP Progress:  41%|████      | 521/1275 [00:00<18:49,  1.50s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 21 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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      "GP Progress:  43%|████▎     | 548/1275 [00:00<16:29,  1.36s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Generation 22 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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      "GP Progress:  45%|████▍     | 572/1275 [00:00<1:26:28,  7.38s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Generation 23 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
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      "Generation 24 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "Generation 25 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 26 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
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      "GP Progress:  53%|█████▎    | 673/1275 [00:00<13:48,  1.38s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 27 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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      "GP Progress:  55%|█████▍    | 697/1275 [00:00<13:21,  1.39s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Generation 28 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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      "GP Progress:  57%|█████▋    | 722/1275 [00:00<17:04,  1.85s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Generation 29 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Generation 30 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 31 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "Generation 32 - Current best internal CV score: 0.815350163926\n"
     ]
    },
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    },
    {
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      "Generation 33 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 34 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 35 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 36 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 37 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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    },
    {
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      "Generation 38 - Current best internal CV score: 0.815350163926\n"
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    },
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    },
    {
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      "Generation 39 - Current best internal CV score: 0.815350163926\n"
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    },
    {
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    },
    {
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      "Generation 40 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 41 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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    },
    {
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      "Generation 42 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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    },
    {
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      "Generation 43 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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    },
    {
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      "Generation 44 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
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     ]
    },
    {
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      "Generation 45 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  90%|████████▉ | 1145/1275 [00:00<04:19,  2.00s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 46 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  92%|█████████▏| 1174/1275 [00:00<01:37,  1.04pipeline/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 47 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  94%|█████████▍| 1199/1275 [00:00<02:30,  1.98s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 48 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  96%|█████████▌| 1223/1275 [00:00<01:26,  1.66s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 49 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GP Progress:  98%|█████████▊| 1249/1275 [00:00<00:54,  2.10s/pipeline]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 50 - Current best internal CV score: 0.815350163926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best pipeline: GradientBoostingClassifier(ZeroCount(StandardScaler(input_matrix)), 0.23999999999999999, 0.69000000000000006)\n",
      "0.959999041319\n"
     ]
    }
   ],
   "source": [
    "pipeline_optimizer.fit(FinalTrainX, FinalTrainY)\n",
    "print(pipeline_optimizer.score(FinalTrainX, FinalTrainY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.959999041319\n"
     ]
    }
   ],
   "source": [
    "print(pipeline_optimizer.score(FinalTrainX, FinalTrainY))\n",
    "pipeline_optimizer.export('/Users/jreinhart/Desktop/Data Analysis Github/Titanic Kaggle/tpot_Titanic_pipeline.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier, VotingClassifier\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from sklearn.preprocessing import FunctionTransformer, StandardScaler\n",
    "from tpot.operators.preprocessors import ZeroCount\n",
    "\n",
    "exported_pipeline = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    ZeroCount(),\n",
    "    GradientBoostingClassifier(learning_rate=0.24, max_features=0.24, n_estimators=500)\n",
    ")\n",
    "\n",
    "exported_pipeline.fit(FinalTrainX, FinalTrainY)\n",
    "Tpotresults = exported_pipeline.predict(FinalTestX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
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       "      <td>893</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "      <td>895</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalTpotresults = pd.DataFrame(FinalTest[\"PassengerId\"])\n",
    "finalTpotresults[\"Survived\"] = Tpotresults.astype(int)\n",
    "finalTpotresults.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finalresults.to_csv(\"/Users/jreinhart/Desktop/Data Analysis Github/Titanic Kaggle/Tpot.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tpot Scored .74641"
   ]
  }
 ],
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