Pytanic Random Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries\n",
    "----------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "import re\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data \n",
    "-------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.DataFrame.from_csv('train.csv')\n",
    "test = pd.DataFrame.from_csv('test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Analysis\n",
    "-------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Luckily the train and test data are split nicely on 'PassengerId'=891. Lets keep this in mind for when we need to train a model on just the train data later. But for now, lets combine the test data with the train data for general exploration of the data and to apply any feature engineering or imputation of missing values all at once\n",
    "* #### Bare in mind, some of the data (the test data, #892+, 418 rows), will be missing a 'Survived' value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>7.2250</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
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       "      <td>26.0000</td>\n",
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       "      <td>7.8958</td>\n",
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       "      <td>0</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.7500</td>\n",
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       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>23.0</td>\n",
       "      <td>B24</td>\n",
       "      <td>S</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>51.0</td>\n",
       "      <td>D28</td>\n",
       "      <td>S</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C.A. 2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>Gilbert, Mr. William</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C.A. 30769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>Kink-Heilmann, Mr. Anton</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>18.0</td>\n",
       "      <td>C31</td>\n",
       "      <td>S</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>48.0</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>Conlon, Mr. Thomas Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>30.0</td>\n",
       "      <td>C7</td>\n",
       "      <td>S</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>Bonnell, Miss. Caroline</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>Gale, Mr. Harry</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>43.0</td>\n",
       "      <td>D40</td>\n",
       "      <td>C</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>20.0</td>\n",
       "      <td>D38</td>\n",
       "      <td>C</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SC/PARIS 2166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>Ware, Mr. William Jeffery</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>50.0</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>37.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</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>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>1306</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>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>1307</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>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>1308</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>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</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>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",
       "<p>1309 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Cabin Embarked      Fare  \\\n",
       "PassengerId                                         \n",
       "1            22.0          NaN        S    7.2500   \n",
       "2            38.0          C85        C   71.2833   \n",
       "3            26.0          NaN        S    7.9250   \n",
       "4            35.0         C123        S   53.1000   \n",
       "5            35.0          NaN        S    8.0500   \n",
       "6             NaN          NaN        Q    8.4583   \n",
       "7            54.0          E46        S   51.8625   \n",
       "8             2.0          NaN        S   21.0750   \n",
       "9            27.0          NaN        S   11.1333   \n",
       "10           14.0          NaN        C   30.0708   \n",
       "11            4.0           G6        S   16.7000   \n",
       "12           58.0         C103        S   26.5500   \n",
       "13           20.0          NaN        S    8.0500   \n",
       "14           39.0          NaN        S   31.2750   \n",
       "15           14.0          NaN        S    7.8542   \n",
       "16           55.0          NaN        S   16.0000   \n",
       "17            2.0          NaN        Q   29.1250   \n",
       "18            NaN          NaN        S   13.0000   \n",
       "19           31.0          NaN        S   18.0000   \n",
       "20            NaN          NaN        C    7.2250   \n",
       "21           35.0          NaN        S   26.0000   \n",
       "22           34.0          D56        S   13.0000   \n",
       "23           15.0          NaN        Q    8.0292   \n",
       "24           28.0           A6        S   35.5000   \n",
       "25            8.0          NaN        S   21.0750   \n",
       "26           38.0          NaN        S   31.3875   \n",
       "27            NaN          NaN        C    7.2250   \n",
       "28           19.0  C23 C25 C27        S  263.0000   \n",
       "29            NaN          NaN        Q    7.8792   \n",
       "30            NaN          NaN        S    7.8958   \n",
       "...           ...          ...      ...       ...   \n",
       "1280         21.0          NaN        Q    7.7500   \n",
       "1281          6.0          NaN        S   21.0750   \n",
       "1282         23.0          B24        S   93.5000   \n",
       "1283         51.0          D28        S   39.4000   \n",
       "1284         13.0          NaN        S   20.2500   \n",
       "1285         47.0          NaN        S   10.5000   \n",
       "1286         29.0          NaN        S   22.0250   \n",
       "1287         18.0          C31        S   60.0000   \n",
       "1288         24.0          NaN        Q    7.2500   \n",
       "1289         48.0          B41        C   79.2000   \n",
       "1290         22.0          NaN        S    7.7750   \n",
       "1291         31.0          NaN        Q    7.7333   \n",
       "1292         30.0           C7        S  164.8667   \n",
       "1293         38.0          NaN        S   21.0000   \n",
       "1294         22.0          NaN        C   59.4000   \n",
       "1295         17.0          NaN        S   47.1000   \n",
       "1296         43.0          D40        C   27.7208   \n",
       "1297         20.0          D38        C   13.8625   \n",
       "1298         23.0          NaN        S   10.5000   \n",
       "1299         50.0          C80        C  211.5000   \n",
       "1300          NaN          NaN        Q    7.7208   \n",
       "1301          3.0          NaN        S   13.7750   \n",
       "1302          NaN          NaN        Q    7.7500   \n",
       "1303         37.0          C78        Q   90.0000   \n",
       "1304         28.0          NaN        S    7.7750   \n",
       "1305          NaN          NaN        S    8.0500   \n",
       "1306         39.0         C105        C  108.9000   \n",
       "1307         38.5          NaN        S    7.2500   \n",
       "1308          NaN          NaN        S    8.0500   \n",
       "1309          NaN          NaN        C   22.3583   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "6                                             Moran, Mr. James      0       3   \n",
       "7                                      McCarthy, Mr. Timothy J      0       1   \n",
       "8                               Palsson, Master. Gosta Leonard      1       3   \n",
       "9            Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      2       3   \n",
       "10                         Nasser, Mrs. Nicholas (Adele Achem)      0       2   \n",
       "11                             Sandstrom, Miss. Marguerite Rut      1       3   \n",
       "12                                    Bonnell, Miss. Elizabeth      0       1   \n",
       "13                              Saundercock, Mr. William Henry      0       3   \n",
       "14                                 Andersson, Mr. Anders Johan      5       3   \n",
       "15                        Vestrom, Miss. Hulda Amanda Adolfina      0       3   \n",
       "16                            Hewlett, Mrs. (Mary D Kingcome)       0       2   \n",
       "17                                        Rice, Master. Eugene      1       3   \n",
       "18                                Williams, Mr. Charles Eugene      0       2   \n",
       "19           Vander Planke, Mrs. Julius (Emelia Maria Vande...      0       3   \n",
       "20                                     Masselmani, Mrs. Fatima      0       3   \n",
       "21                                        Fynney, Mr. Joseph J      0       2   \n",
       "22                                       Beesley, Mr. Lawrence      0       2   \n",
       "23                                 McGowan, Miss. Anna \"Annie\"      0       3   \n",
       "24                                Sloper, Mr. William Thompson      0       1   \n",
       "25                               Palsson, Miss. Torborg Danira      1       3   \n",
       "26           Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...      5       3   \n",
       "27                                     Emir, Mr. Farred Chehab      0       3   \n",
       "28                              Fortune, Mr. Charles Alexander      2       1   \n",
       "29                               O'Dwyer, Miss. Ellen \"Nellie\"      0       3   \n",
       "30                                         Todoroff, Mr. Lalio      0       3   \n",
       "...                                                        ...    ...     ...   \n",
       "1280                                      Canavan, Mr. Patrick      0       3   \n",
       "1281                               Palsson, Master. Paul Folke      1       3   \n",
       "1282                                Payne, Mr. Vivian Ponsonby      0       1   \n",
       "1283            Lines, Mrs. Ernest H (Elizabeth Lindsey James)      1       1   \n",
       "1284                             Abbott, Master. Eugene Joseph      2       3   \n",
       "1285                                      Gilbert, Mr. William      0       2   \n",
       "1286                                  Kink-Heilmann, Mr. Anton      1       3   \n",
       "1287            Smith, Mrs. Lucien Philip (Mary Eloise Hughes)      0       1   \n",
       "1288                                      Colbert, Mr. Patrick      0       3   \n",
       "1289         Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...      1       1   \n",
       "1290                            Larsson-Rondberg, Mr. Edvard A      0       3   \n",
       "1291                                  Conlon, Mr. Thomas Henry      0       3   \n",
       "1292                                   Bonnell, Miss. Caroline      0       1   \n",
       "1293                                           Gale, Mr. Harry      0       2   \n",
       "1294                            Gibson, Miss. Dorothy Winifred      1       1   \n",
       "1295                                    Carrau, Mr. Jose Pedro      0       1   \n",
       "1296                              Frauenthal, Mr. Isaac Gerald      0       1   \n",
       "1297              Nourney, Mr. Alfred (Baron von Drachstedt\")\"      0       2   \n",
       "1298                                 Ware, Mr. William Jeffery      0       2   \n",
       "1299                                Widener, Mr. George Dunton      1       1   \n",
       "1300                           Riordan, Miss. Johanna Hannah\"\"      0       3   \n",
       "1301                                 Peacock, Miss. Treasteall      1       3   \n",
       "1302                                    Naughton, Miss. Hannah      0       3   \n",
       "1303           Minahan, Mrs. William Edward (Lillian E Thorpe)      0       1   \n",
       "1304                            Henriksson, Miss. Jenny Lovisa      0       3   \n",
       "1305                                        Spector, Mr. Woolf      0       3   \n",
       "1306                              Oliva y Ocana, Dona. Fermina      0       1   \n",
       "1307                              Saether, Mr. Simon Sivertsen      0       3   \n",
       "1308                                       Ware, Mr. Frederick      0       3   \n",
       "1309                                  Peter, Master. Michael J      1       3   \n",
       "\n",
       "                Sex  SibSp  Survived              Ticket  \n",
       "PassengerId                                               \n",
       "1              male      1       0.0           A/5 21171  \n",
       "2            female      1       1.0            PC 17599  \n",
       "3            female      0       1.0    STON/O2. 3101282  \n",
       "4            female      1       1.0              113803  \n",
       "5              male      0       0.0              373450  \n",
       "6              male      0       0.0              330877  \n",
       "7              male      0       0.0               17463  \n",
       "8              male      3       0.0              349909  \n",
       "9            female      0       1.0              347742  \n",
       "10           female      1       1.0              237736  \n",
       "11           female      1       1.0             PP 9549  \n",
       "12           female      0       1.0              113783  \n",
       "13             male      0       0.0           A/5. 2151  \n",
       "14             male      1       0.0              347082  \n",
       "15           female      0       0.0              350406  \n",
       "16           female      0       1.0              248706  \n",
       "17             male      4       0.0              382652  \n",
       "18             male      0       1.0              244373  \n",
       "19           female      1       0.0              345763  \n",
       "20           female      0       1.0                2649  \n",
       "21             male      0       0.0              239865  \n",
       "22             male      0       1.0              248698  \n",
       "23           female      0       1.0              330923  \n",
       "24             male      0       1.0              113788  \n",
       "25           female      3       0.0              349909  \n",
       "26           female      1       1.0              347077  \n",
       "27             male      0       0.0                2631  \n",
       "28             male      3       0.0               19950  \n",
       "29           female      0       1.0              330959  \n",
       "30             male      0       0.0              349216  \n",
       "...             ...    ...       ...                 ...  \n",
       "1280           male      0       NaN              364858  \n",
       "1281           male      3       NaN              349909  \n",
       "1282           male      0       NaN               12749  \n",
       "1283         female      0       NaN            PC 17592  \n",
       "1284           male      0       NaN           C.A. 2673  \n",
       "1285           male      0       NaN          C.A. 30769  \n",
       "1286           male      3       NaN              315153  \n",
       "1287         female      1       NaN               13695  \n",
       "1288           male      0       NaN              371109  \n",
       "1289         female      1       NaN               13567  \n",
       "1290           male      0       NaN              347065  \n",
       "1291           male      0       NaN               21332  \n",
       "1292         female      0       NaN               36928  \n",
       "1293           male      1       NaN               28664  \n",
       "1294         female      0       NaN              112378  \n",
       "1295           male      0       NaN              113059  \n",
       "1296           male      1       NaN               17765  \n",
       "1297           male      0       NaN       SC/PARIS 2166  \n",
       "1298           male      1       NaN               28666  \n",
       "1299           male      1       NaN              113503  \n",
       "1300         female      0       NaN              334915  \n",
       "1301         female      1       NaN  SOTON/O.Q. 3101315  \n",
       "1302         female      0       NaN              365237  \n",
       "1303         female      1       NaN               19928  \n",
       "1304         female      0       NaN              347086  \n",
       "1305           male      0       NaN           A.5. 3236  \n",
       "1306         female      0       NaN            PC 17758  \n",
       "1307           male      0       NaN  SOTON/O.Q. 3101262  \n",
       "1308           male      0       NaN              359309  \n",
       "1309           male      1       NaN                2668  \n",
       "\n",
       "[1309 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test = pd.concat([train,test])\n",
    "train_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>295</td>\n",
       "      <td>1307</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
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       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1309</td>\n",
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       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Cabin Embarked         Fare                  Name  \\\n",
       "count   1046.000000          295     1307  1308.000000                  1309   \n",
       "unique          NaN          186        3          NaN                  1307   \n",
       "top             NaN  C23 C25 C27        S          NaN  Connolly, Miss. Kate   \n",
       "freq            NaN            6      914          NaN                     2   \n",
       "mean      29.881138          NaN      NaN    33.295479                   NaN   \n",
       "std       14.413493          NaN      NaN    51.758668                   NaN   \n",
       "min        0.170000          NaN      NaN     0.000000                   NaN   \n",
       "25%       21.000000          NaN      NaN     7.895800                   NaN   \n",
       "50%       28.000000          NaN      NaN    14.454200                   NaN   \n",
       "75%       39.000000          NaN      NaN    31.275000                   NaN   \n",
       "max       80.000000          NaN      NaN   512.329200                   NaN   \n",
       "\n",
       "              Parch       Pclass   Sex        SibSp    Survived    Ticket  \n",
       "count   1309.000000  1309.000000  1309  1309.000000  891.000000      1309  \n",
       "unique          NaN          NaN     2          NaN         NaN       929  \n",
       "top             NaN          NaN  male          NaN         NaN  CA. 2343  \n",
       "freq            NaN          NaN   843          NaN         NaN        11  \n",
       "mean       0.385027     2.294882   NaN     0.498854    0.383838       NaN  \n",
       "std        0.865560     0.837836   NaN     1.041658    0.486592       NaN  \n",
       "min        0.000000     1.000000   NaN     0.000000    0.000000       NaN  \n",
       "25%        0.000000     2.000000   NaN     0.000000    0.000000       NaN  \n",
       "50%        0.000000     3.000000   NaN     0.000000    0.000000       NaN  \n",
       "75%        0.000000     3.000000   NaN     1.000000    1.000000       NaN  \n",
       "max        9.000000     3.000000   NaN     8.000000    1.000000       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age          263\n",
       "Cabin       1014\n",
       "Embarked       2\n",
       "Fare           1\n",
       "Name           0\n",
       "Parch          0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "SibSp          0\n",
       "Survived     418\n",
       "Ticket         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Preliminary Engineering\n",
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ### A significant percentage of 'Age' and 'Cabin' values are missing from the test data, so to simplify for the time being, drop these columns.\n",
    "\n",
    "* ### 'Embarked' is only missing two values, so just fill those rows with the most common value 'S'\n",
    "* ### 'Fare' is missing a single value :/\n",
    "* ### 'Ticket' and 'Name' do not immediately appear to contain useful data, so drop for now\n",
    "--------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Embarked     Fare  Parch  Pclass     Sex  SibSp  Survived\n",
       "PassengerId                                                          \n",
       "1                  S   7.2500      0       3    male      1       0.0\n",
       "2                  C  71.2833      0       1  female      1       1.0\n",
       "3                  S   7.9250      0       3  female      0       1.0\n",
       "4                  S  53.1000      0       1  female      1       1.0\n",
       "5                  S   8.0500      0       3    male      0       0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test.drop(['Age','Cabin', 'Ticket', 'Name'], axis=1, inplace=True)\n",
    "train_test['Embarked'].fillna('S', inplace=True)\n",
    "train_test['Fare'].fillna(30, inplace=True)\n",
    "train_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------\n",
    "### 'Embarked' and 'Sex' are *Categorical*, so lets turn them into *Numerical* values by creating dummy feature coumns\n",
    "----------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "embarked = pd.get_dummies(train_test['Embarked'], prefix='Embarked')\n",
    "train_test = train_test.join(embarked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test['Female'] = np.where(train_test['Sex'] == 'female', 1 ,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "1             7.2500      0       3      1       0.0           0           0   \n",
       "2            71.2833      0       1      1       1.0           1           0   \n",
       "3             7.9250      0       3      0       1.0           0           0   \n",
       "4            53.1000      0       1      1       1.0           0           0   \n",
       "5             8.0500      0       3      0       0.0           0           0   \n",
       "\n",
       "             Embarked_S  Female  \n",
       "PassengerId                      \n",
       "1                     1       0  \n",
       "2                     0       1  \n",
       "3                     1       1  \n",
       "4                     1       1  \n",
       "5                     1       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test.drop(['Sex','Embarked'], axis=1, inplace=True)\n",
    "train_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "### Preliminary Modeling\n",
    "-------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1    0\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "5    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain = train['Survived']\n",
    "ytrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                          \n",
       "1             7.2500      0       3      1           0           0   \n",
       "2            71.2833      0       1      1           1           0   \n",
       "3             7.9250      0       3      0           0           0   \n",
       "4            53.1000      0       1      1           0           0   \n",
       "5             8.0500      0       3      0           0           0   \n",
       "\n",
       "             Embarked_S  Female  \n",
       "PassengerId                      \n",
       "1                     1       0  \n",
       "2                     0       1  \n",
       "3                     1       1  \n",
       "4                     1       1  \n",
       "5                     1       0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain = train_test[train_test['Survived'].notnull()].drop(['Survived'], axis=1)\n",
    "xtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100)\n",
    "model.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IDtest = pd.Series(test.index.values, name=\"PassengerID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fare          0\n",
       "Parch         0\n",
       "Pclass        0\n",
       "SibSp         0\n",
       "Embarked_C    0\n",
       "Embarked_Q    0\n",
       "Embarked_S    0\n",
       "Female        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest = train_test[train_test['Survived'].isnull()].drop(['Survived'], axis=1)\n",
    "xtest.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testy = pd.Series(model.predict(xtest), name=\"Survived\")\n",
    "testy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerID</th>\n",
       "      <th>Survived</th>\n",
       "    </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>1</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         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.concat([IDtest, testy], axis=1)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results.to_csv(\"basic.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---------\n",
    "## That prediction had an accuracy of 0.7512 on the official test submission. Lets see if we can improve it!\n",
    "--------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets look at what features the RandomForest predicted were most important\n",
    "-------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ch1DcOHaIpH8H/L3tI2ourXHSvRNRj2N4NKTeNhZSNde0g4kOTkCjQh+41/bD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10284f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = model.feature_importances_\n",
    "names=xtrain.columns.values\n",
    "pd.Series(importances, index=names).plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First lets add back in the columns we completely removed from the original data, while keeping the dummy features we made for 'Embarked' and 'Sex'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16.7000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.2750</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>29.1250</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31.3875</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>263.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>93.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>39.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>20.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>22.0250</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>60.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>79.2000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>7.7333</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>164.8667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>59.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>47.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>27.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>13.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>211.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>7.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>13.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>90.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                     \n",
       "1              7.2500      0       3      1       0.0           0           0   \n",
       "2             71.2833      0       1      1       1.0           1           0   \n",
       "3              7.9250      0       3      0       1.0           0           0   \n",
       "4             53.1000      0       1      1       1.0           0           0   \n",
       "5              8.0500      0       3      0       0.0           0           0   \n",
       "6              8.4583      0       3      0       0.0           0           1   \n",
       "7             51.8625      0       1      0       0.0           0           0   \n",
       "8             21.0750      1       3      3       0.0           0           0   \n",
       "9             11.1333      2       3      0       1.0           0           0   \n",
       "10            30.0708      0       2      1       1.0           1           0   \n",
       "11            16.7000      1       3      1       1.0           0           0   \n",
       "12            26.5500      0       1      0       1.0           0           0   \n",
       "13             8.0500      0       3      0       0.0           0           0   \n",
       "14            31.2750      5       3      1       0.0           0           0   \n",
       "15             7.8542      0       3      0       0.0           0           0   \n",
       "16            16.0000      0       2      0       1.0           0           0   \n",
       "17            29.1250      1       3      4       0.0           0           1   \n",
       "18            13.0000      0       2      0       1.0           0           0   \n",
       "19            18.0000      0       3      1       0.0           0           0   \n",
       "20             7.2250      0       3      0       1.0           1           0   \n",
       "21            26.0000      0       2      0       0.0           0           0   \n",
       "22            13.0000      0       2      0       1.0           0           0   \n",
       "23             8.0292      0       3      0       1.0           0           1   \n",
       "24            35.5000      0       1      0       1.0           0           0   \n",
       "25            21.0750      1       3      3       0.0           0           0   \n",
       "26            31.3875      5       3      1       1.0           0           0   \n",
       "27             7.2250      0       3      0       0.0           1           0   \n",
       "28           263.0000      2       1      3       0.0           0           0   \n",
       "29             7.8792      0       3      0       1.0           0           1   \n",
       "30             7.8958      0       3      0       0.0           0           0   \n",
       "...               ...    ...     ...    ...       ...         ...         ...   \n",
       "1280           7.7500      0       3      0       NaN           0           1   \n",
       "1281          21.0750      1       3      3       NaN           0           0   \n",
       "1282          93.5000      0       1      0       NaN           0           0   \n",
       "1283          39.4000      1       1      0       NaN           0           0   \n",
       "1284          20.2500      2       3      0       NaN           0           0   \n",
       "1285          10.5000      0       2      0       NaN           0           0   \n",
       "1286          22.0250      1       3      3       NaN           0           0   \n",
       "1287          60.0000      0       1      1       NaN           0           0   \n",
       "1288           7.2500      0       3      0       NaN           0           1   \n",
       "1289          79.2000      1       1      1       NaN           1           0   \n",
       "1290           7.7750      0       3      0       NaN           0           0   \n",
       "1291           7.7333      0       3      0       NaN           0           1   \n",
       "1292         164.8667      0       1      0       NaN           0           0   \n",
       "1293          21.0000      0       2      1       NaN           0           0   \n",
       "1294          59.4000      1       1      0       NaN           1           0   \n",
       "1295          47.1000      0       1      0       NaN           0           0   \n",
       "1296          27.7208      0       1      1       NaN           1           0   \n",
       "1297          13.8625      0       2      0       NaN           1           0   \n",
       "1298          10.5000      0       2      1       NaN           0           0   \n",
       "1299         211.5000      1       1      1       NaN           1           0   \n",
       "1300           7.7208      0       3      0       NaN           0           1   \n",
       "1301          13.7750      1       3      1       NaN           0           0   \n",
       "1302           7.7500      0       3      0       NaN           0           1   \n",
       "1303          90.0000      0       1      1       NaN           0           1   \n",
       "1304           7.7750      0       3      0       NaN           0           0   \n",
       "1305           8.0500      0       3      0       NaN           0           0   \n",
       "1306         108.9000      0       1      0       NaN           1           0   \n",
       "1307           7.2500      0       3      0       NaN           0           0   \n",
       "1308           8.0500      0       3      0       NaN           0           0   \n",
       "1309          22.3583      1       3      1       NaN           1           0   \n",
       "\n",
       "             Embarked_S  Female  \n",
       "PassengerId                      \n",
       "1                     1       0  \n",
       "2                     0       1  \n",
       "3                     1       1  \n",
       "4                     1       1  \n",
       "5                     1       0  \n",
       "6                     0       0  \n",
       "7                     1       0  \n",
       "8                     1       0  \n",
       "9                     1       1  \n",
       "10                    0       1  \n",
       "11                    1       1  \n",
       "12                    1       1  \n",
       "13                    1       0  \n",
       "14                    1       0  \n",
       "15                    1       1  \n",
       "16                    1       1  \n",
       "17                    0       0  \n",
       "18                    1       0  \n",
       "19                    1       1  \n",
       "20                    0       1  \n",
       "21                    1       0  \n",
       "22                    1       0  \n",
       "23                    0       1  \n",
       "24                    1       0  \n",
       "25                    1       1  \n",
       "26                    1       1  \n",
       "27                    0       0  \n",
       "28                    1       0  \n",
       "29                    0       1  \n",
       "30                    1       0  \n",
       "...                 ...     ...  \n",
       "1280                  0       0  \n",
       "1281                  1       0  \n",
       "1282                  1       0  \n",
       "1283                  1       1  \n",
       "1284                  1       0  \n",
       "1285                  1       0  \n",
       "1286                  1       0  \n",
       "1287                  1       1  \n",
       "1288                  0       0  \n",
       "1289                  0       1  \n",
       "1290                  1       0  \n",
       "1291                  0       0  \n",
       "1292                  1       1  \n",
       "1293                  1       0  \n",
       "1294                  0       1  \n",
       "1295                  1       0  \n",
       "1296                  0       0  \n",
       "1297                  0       0  \n",
       "1298                  1       0  \n",
       "1299                  0       0  \n",
       "1300                  0       1  \n",
       "1301                  1       1  \n",
       "1302                  0       1  \n",
       "1303                  0       1  \n",
       "1304                  1       1  \n",
       "1305                  1       0  \n",
       "1306                  0       1  \n",
       "1307                  1       0  \n",
       "1308                  1       0  \n",
       "1309                  0       0  \n",
       "\n",
       "[1309 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_cabin_name = pd.concat([train[['Age','Cabin','Name']], test[['Age','Cabin','Name']]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test = train_test.join(age_cabin_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>E46</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16.7000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>G6</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>C103</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.2750</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>29.1250</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>D56</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31.3875</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>263.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Todoroff, Mr. Lalio</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canavan, Mr. Patrick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>93.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B24</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>39.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>D28</td>\n",
       "      <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>20.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gilbert, Mr. William</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>22.0250</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kink-Heilmann, Mr. Anton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>60.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>C31</td>\n",
       "      <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>79.2000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>B41</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>7.7333</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conlon, Mr. Thomas Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>164.8667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>C7</td>\n",
       "      <td>Bonnell, Miss. Caroline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gale, Mr. Harry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>59.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>47.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>27.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>D40</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>13.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>D38</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ware, Mr. William Jeffery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>211.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>C80</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>7.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>13.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>90.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>C105</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                     \n",
       "1              7.2500      0       3      1       0.0           0           0   \n",
       "2             71.2833      0       1      1       1.0           1           0   \n",
       "3              7.9250      0       3      0       1.0           0           0   \n",
       "4             53.1000      0       1      1       1.0           0           0   \n",
       "5              8.0500      0       3      0       0.0           0           0   \n",
       "6              8.4583      0       3      0       0.0           0           1   \n",
       "7             51.8625      0       1      0       0.0           0           0   \n",
       "8             21.0750      1       3      3       0.0           0           0   \n",
       "9             11.1333      2       3      0       1.0           0           0   \n",
       "10            30.0708      0       2      1       1.0           1           0   \n",
       "11            16.7000      1       3      1       1.0           0           0   \n",
       "12            26.5500      0       1      0       1.0           0           0   \n",
       "13             8.0500      0       3      0       0.0           0           0   \n",
       "14            31.2750      5       3      1       0.0           0           0   \n",
       "15             7.8542      0       3      0       0.0           0           0   \n",
       "16            16.0000      0       2      0       1.0           0           0   \n",
       "17            29.1250      1       3      4       0.0           0           1   \n",
       "18            13.0000      0       2      0       1.0           0           0   \n",
       "19            18.0000      0       3      1       0.0           0           0   \n",
       "20             7.2250      0       3      0       1.0           1           0   \n",
       "21            26.0000      0       2      0       0.0           0           0   \n",
       "22            13.0000      0       2      0       1.0           0           0   \n",
       "23             8.0292      0       3      0       1.0           0           1   \n",
       "24            35.5000      0       1      0       1.0           0           0   \n",
       "25            21.0750      1       3      3       0.0           0           0   \n",
       "26            31.3875      5       3      1       1.0           0           0   \n",
       "27             7.2250      0       3      0       0.0           1           0   \n",
       "28           263.0000      2       1      3       0.0           0           0   \n",
       "29             7.8792      0       3      0       1.0           0           1   \n",
       "30             7.8958      0       3      0       0.0           0           0   \n",
       "...               ...    ...     ...    ...       ...         ...         ...   \n",
       "1280           7.7500      0       3      0       NaN           0           1   \n",
       "1281          21.0750      1       3      3       NaN           0           0   \n",
       "1282          93.5000      0       1      0       NaN           0           0   \n",
       "1283          39.4000      1       1      0       NaN           0           0   \n",
       "1284          20.2500      2       3      0       NaN           0           0   \n",
       "1285          10.5000      0       2      0       NaN           0           0   \n",
       "1286          22.0250      1       3      3       NaN           0           0   \n",
       "1287          60.0000      0       1      1       NaN           0           0   \n",
       "1288           7.2500      0       3      0       NaN           0           1   \n",
       "1289          79.2000      1       1      1       NaN           1           0   \n",
       "1290           7.7750      0       3      0       NaN           0           0   \n",
       "1291           7.7333      0       3      0       NaN           0           1   \n",
       "1292         164.8667      0       1      0       NaN           0           0   \n",
       "1293          21.0000      0       2      1       NaN           0           0   \n",
       "1294          59.4000      1       1      0       NaN           1           0   \n",
       "1295          47.1000      0       1      0       NaN           0           0   \n",
       "1296          27.7208      0       1      1       NaN           1           0   \n",
       "1297          13.8625      0       2      0       NaN           1           0   \n",
       "1298          10.5000      0       2      1       NaN           0           0   \n",
       "1299         211.5000      1       1      1       NaN           1           0   \n",
       "1300           7.7208      0       3      0       NaN           0           1   \n",
       "1301          13.7750      1       3      1       NaN           0           0   \n",
       "1302           7.7500      0       3      0       NaN           0           1   \n",
       "1303          90.0000      0       1      1       NaN           0           1   \n",
       "1304           7.7750      0       3      0       NaN           0           0   \n",
       "1305           8.0500      0       3      0       NaN           0           0   \n",
       "1306         108.9000      0       1      0       NaN           1           0   \n",
       "1307           7.2500      0       3      0       NaN           0           0   \n",
       "1308           8.0500      0       3      0       NaN           0           0   \n",
       "1309          22.3583      1       3      1       NaN           1           0   \n",
       "\n",
       "             Embarked_S  Female   Age        Cabin  \\\n",
       "PassengerId                                          \n",
       "1                     1       0  22.0          NaN   \n",
       "2                     0       1  38.0          C85   \n",
       "3                     1       1  26.0          NaN   \n",
       "4                     1       1  35.0         C123   \n",
       "5                     1       0  35.0          NaN   \n",
       "6                     0       0   NaN          NaN   \n",
       "7                     1       0  54.0          E46   \n",
       "8                     1       0   2.0          NaN   \n",
       "9                     1       1  27.0          NaN   \n",
       "10                    0       1  14.0          NaN   \n",
       "11                    1       1   4.0           G6   \n",
       "12                    1       1  58.0         C103   \n",
       "13                    1       0  20.0          NaN   \n",
       "14                    1       0  39.0          NaN   \n",
       "15                    1       1  14.0          NaN   \n",
       "16                    1       1  55.0          NaN   \n",
       "17                    0       0   2.0          NaN   \n",
       "18                    1       0   NaN          NaN   \n",
       "19                    1       1  31.0          NaN   \n",
       "20                    0       1   NaN          NaN   \n",
       "21                    1       0  35.0          NaN   \n",
       "22                    1       0  34.0          D56   \n",
       "23                    0       1  15.0          NaN   \n",
       "24                    1       0  28.0           A6   \n",
       "25                    1       1   8.0          NaN   \n",
       "26                    1       1  38.0          NaN   \n",
       "27                    0       0   NaN          NaN   \n",
       "28                    1       0  19.0  C23 C25 C27   \n",
       "29                    0       1   NaN          NaN   \n",
       "30                    1       0   NaN          NaN   \n",
       "...                 ...     ...   ...          ...   \n",
       "1280                  0       0  21.0          NaN   \n",
       "1281                  1       0   6.0          NaN   \n",
       "1282                  1       0  23.0          B24   \n",
       "1283                  1       1  51.0          D28   \n",
       "1284                  1       0  13.0          NaN   \n",
       "1285                  1       0  47.0          NaN   \n",
       "1286                  1       0  29.0          NaN   \n",
       "1287                  1       1  18.0          C31   \n",
       "1288                  0       0  24.0          NaN   \n",
       "1289                  0       1  48.0          B41   \n",
       "1290                  1       0  22.0          NaN   \n",
       "1291                  0       0  31.0          NaN   \n",
       "1292                  1       1  30.0           C7   \n",
       "1293                  1       0  38.0          NaN   \n",
       "1294                  0       1  22.0          NaN   \n",
       "1295                  1       0  17.0          NaN   \n",
       "1296                  0       0  43.0          D40   \n",
       "1297                  0       0  20.0          D38   \n",
       "1298                  1       0  23.0          NaN   \n",
       "1299                  0       0  50.0          C80   \n",
       "1300                  0       1   NaN          NaN   \n",
       "1301                  1       1   3.0          NaN   \n",
       "1302                  0       1   NaN          NaN   \n",
       "1303                  0       1  37.0          C78   \n",
       "1304                  1       1  28.0          NaN   \n",
       "1305                  1       0   NaN          NaN   \n",
       "1306                  0       1  39.0         C105   \n",
       "1307                  1       0  38.5          NaN   \n",
       "1308                  1       0   NaN          NaN   \n",
       "1309                  0       0   NaN          NaN   \n",
       "\n",
       "                                                          Name  \n",
       "PassengerId                                                     \n",
       "1                                      Braund, Mr. Owen Harris  \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  \n",
       "3                                       Heikkinen, Miss. Laina  \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  \n",
       "5                                     Allen, Mr. William Henry  \n",
       "6                                             Moran, Mr. James  \n",
       "7                                      McCarthy, Mr. Timothy J  \n",
       "8                               Palsson, Master. Gosta Leonard  \n",
       "9            Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  \n",
       "10                         Nasser, Mrs. Nicholas (Adele Achem)  \n",
       "11                             Sandstrom, Miss. Marguerite Rut  \n",
       "12                                    Bonnell, Miss. Elizabeth  \n",
       "13                              Saundercock, Mr. William Henry  \n",
       "14                                 Andersson, Mr. Anders Johan  \n",
       "15                        Vestrom, Miss. Hulda Amanda Adolfina  \n",
       "16                            Hewlett, Mrs. (Mary D Kingcome)   \n",
       "17                                        Rice, Master. Eugene  \n",
       "18                                Williams, Mr. Charles Eugene  \n",
       "19           Vander Planke, Mrs. Julius (Emelia Maria Vande...  \n",
       "20                                     Masselmani, Mrs. Fatima  \n",
       "21                                        Fynney, Mr. Joseph J  \n",
       "22                                       Beesley, Mr. Lawrence  \n",
       "23                                 McGowan, Miss. Anna \"Annie\"  \n",
       "24                                Sloper, Mr. William Thompson  \n",
       "25                               Palsson, Miss. Torborg Danira  \n",
       "26           Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  \n",
       "27                                     Emir, Mr. Farred Chehab  \n",
       "28                              Fortune, Mr. Charles Alexander  \n",
       "29                               O'Dwyer, Miss. Ellen \"Nellie\"  \n",
       "30                                         Todoroff, Mr. Lalio  \n",
       "...                                                        ...  \n",
       "1280                                      Canavan, Mr. Patrick  \n",
       "1281                               Palsson, Master. Paul Folke  \n",
       "1282                                Payne, Mr. Vivian Ponsonby  \n",
       "1283            Lines, Mrs. Ernest H (Elizabeth Lindsey James)  \n",
       "1284                             Abbott, Master. Eugene Joseph  \n",
       "1285                                      Gilbert, Mr. William  \n",
       "1286                                  Kink-Heilmann, Mr. Anton  \n",
       "1287            Smith, Mrs. Lucien Philip (Mary Eloise Hughes)  \n",
       "1288                                      Colbert, Mr. Patrick  \n",
       "1289         Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...  \n",
       "1290                            Larsson-Rondberg, Mr. Edvard A  \n",
       "1291                                  Conlon, Mr. Thomas Henry  \n",
       "1292                                   Bonnell, Miss. Caroline  \n",
       "1293                                           Gale, Mr. Harry  \n",
       "1294                            Gibson, Miss. Dorothy Winifred  \n",
       "1295                                    Carrau, Mr. Jose Pedro  \n",
       "1296                              Frauenthal, Mr. Isaac Gerald  \n",
       "1297              Nourney, Mr. Alfred (Baron von Drachstedt\")\"  \n",
       "1298                                 Ware, Mr. William Jeffery  \n",
       "1299                                Widener, Mr. George Dunton  \n",
       "1300                           Riordan, Miss. Johanna Hannah\"\"  \n",
       "1301                                 Peacock, Miss. Treasteall  \n",
       "1302                                    Naughton, Miss. Hannah  \n",
       "1303           Minahan, Mrs. William Edward (Lillian E Thorpe)  \n",
       "1304                            Henriksson, Miss. Jenny Lovisa  \n",
       "1305                                        Spector, Mr. Woolf  \n",
       "1306                              Oliva y Ocana, Dona. Fermina  \n",
       "1307                              Saether, Mr. Simon Sivertsen  \n",
       "1308                                       Ware, Mr. Frederick  \n",
       "1309                                  Peter, Master. Michael J  \n",
       "\n",
       "[1309 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------\n",
    "# Exploratory Analysis\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of values for numerical features\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6e9yxzMOPVdXJ9LoSXJDk9f0LJ+DffAlwMnBFVb0a+Ca7dW2cgBif1p49eAvw\nh7svG3ec7ZmAlfQS5pcAh/LsbmeS5mHcf8+jMg3fCRbSpH/fWEhT9l1mIU3696SndTW52wIc1zd/\nbCubRI8nOQagvW8bczwkeT69/8Svq6pPtOKJi3OXqnoK+DS9bgFLkyxpi8b97/464C1JNgHX0+vO\ncCmTFSPw9N0cqmobvWfETmGy/s03A5ur6s42fyO9ZG+SYux3BvD5qnq8zU9SnD8BfKmqvlJVfw98\ngt5ndeI+l5pY03SOXQh7+3vuZLvs53eCTrbBLvP8vvF0G7TlhwNfHXGow7S/32W6dvzAfn9PGuvf\nQVeTu88BJ7SRfF5Ar3vUTWOOaW9uAla16VX0+rOPTZIAVwIPVtWH+hZNWpw/mGRpmz6E3jMAD9L7\nT/etrdpY46yq91fVsVW1nN5n8FNVdQ4TFCNAkkOTvHjXNL1nxe5jgv7Nq+ox4NEkL29FpwEPMEEx\n7uYdPNMlEyYrzi8DpyZ5Uft739WWE/W51ESbpnPsQtjb3/NNwLltpLxTge19Xbam0gF8J+hiG+zv\n943+tnkrvXP/RNzRORAH8F2mU8cPB/Q9abx/B6N6uG/UL+BM4K/p9Yv+9+OOp8X0UXrPuPw9vTsR\n59Prh3w78BDwv4Ajxxzjj9G7rXwvcE97nTmBcf4z4C9bnPcB/6GV/2Pgs8BGel3iDh73v3uLaxa4\neRJjbPH8VXvdv+vvZQL/zV8F3NX+zf8ncMSkxdjiPJTeVcrD+8omKk7g14AvtL+d3wcOnrTPpa/J\nfk3iOXaBjnPe5216g0x8uLXJBnoj0o79GAY8/v36TtDRNtiv7xvAC9v8xrb8H4/7GIbYFvv8LtPF\n49/f70nj/jtIC0KSJEmSNMW62i1TkiRJkhYVkztJkiRJ6gCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ\n6gCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ6gCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ6gCTO0mSJEnq\nAJM7SZIkSeoAkztpQiWZS/Jz445DkqRJlGQ2yeZxxyFNEpM76QAk2ZTk20l2JHk8ydVJDht3XJIk\nTSvPrdLgTO6kA/cvq+ow4GRgBfB/78/KSZYsSFSSJE2vgc6t0mJncicNqKq2AH8M/EiSdyV5MMk3\nkjyc5Od31dvVfSTJ+5I8Bnykla9Mck+Sryf5mySn923+pUn+d9veJ5McPdqjkyRp9HY7tx6Z5CNJ\n/jbJk0kq4tpEAAAgAElEQVT+557WSbK2nUe/keSBJP+qb9nLkvxZku1JnkjysVaeJJck2dbOwxuS\n/MhojlIaPu8cSANKchxwJvAJYBvw08DDwOuBP07yuar6fKv+Q8CRwEuB5yU5BbgWeCtwO3AM8OK+\nzf9r4AzgUXonuX8LrF3oY5IkaZx2O7f+PrAD+Kft/V/sZbW/AX4ceAx4G/A/krysqrYCvw58EngD\n8AJ6dwUB3kTvfP3DwHbgFcBTC3BI0kiY3EkH7n8m2UnvZHAL8F+q6tt9y/8sySfpnWh2JXf/AHyg\nqv4OIMn5wFVVdVtbvmW3fXykqv661b0BeMvCHIokSRNh93Prb9M7Nx5VVU+2On+2pxWr6g/7Zj+W\n5P3AKcB64O/pXVh9SVVtBj7T6v09vYuqrwA+W1UPDvl4pJGyW6Z04M6qqqVV9dKq+oWq+naSM5Lc\nkeRrSZ6id9WxvyvlV6rqO33zx9G70rg3j/VNfwvwwXJJUpd937mV3nnya32J3V4lObc95vBUOwf/\nCM+cg38FCPDZJPcn+VmAqvoU8N+ADwPbkqxL8gMLcWDSKJjcSUOS5GDg48BvAjNVtRS4ld7JZJfa\nbbVHgf9rNBFKkjR1HgWOTLL0uSoleSnwu8C76d3lWwrcRzsHV9VjVfVvquolwM8Dv53kZW3ZZVX1\no8CJ9Lpn/rsFOxppgZncScPzAuBg4CvAziRn0OvL/1yuBN6V5LQkz0uyLMkrFjpQSZKmQXte7o/p\nJWNHJHl+ktfvoeqh9C6gfgUgybvo3bmjzb8tybFt9slW9x+S/PMkr0nyfOCbwHfoPUIhTSWTO2lI\nquobwC8BN9A7cfxr4KZ9rPNZ4F3AJfSeL/gzes8ESJKknnfSezbuC/QGLnvP7hWq6gHgYuAvgMeB\nk4D/3VflnwN3JtlB79x8YVU9DPwAvTt+TwKPAF8F/uuCHYm0wFK1ey8xSZIkSdK08c6dJEmSJHWA\nyZ0kSZIkdYDJnSRJkiR1gMmdJEmSJHWAyZ0kSZIkdcCScQfwXI4++uhavnz5QNv45je/yaGHHjqc\ngDrKNpof22nfbKP56WI73X333U9U1Q+OOw7N32I5x05DjDAdcRrj8ExDnMY4PIPGuT/n2IlO7pYv\nX85dd9010Dbm5uaYnZ0dTkAdZRvNj+20b7bR/HSxnZI8Mu4YtH8Wyzl2GmKE6YjTGIdnGuI0xuEZ\nNM79OcfaLVOSJEmSOsDkTpIkSZI6YJ/JXZKrkmxLcl9f2ZFJbkvyUHs/opUnyWVJNia5N8nJfeus\navUfSrJqYQ5HkiRJkhan+dy5uxo4fbeytcDtVXUCcHubBzgDOKG9VgNXQC8ZBD4AvAY4BfjAroRQ\nkiRJkjS4fSZ3VfXnwNd2K14JXNOmrwHO6iu/tnruAJYmOQb4KeC2qvpaVT0J3MazE0ZJkiRJ0gE6\n0NEyZ6pqa5t+DJhp08uAR/vqbW5leyt/liSr6d31Y2Zmhrm5uQMMsWfb17Zz+XXrB9rGoE5advhY\n978vO3bsGLidFwPbad9so/mxnSRJGo3la28Zdwhcffrofq5h4J9CqKpKUsMIpm1vHbAOYMWKFTXo\n8KaXX7eeizeM9xcfNp0zO9b978u0DCM7brbTvtlG82M7SZKkhXCgo2U+3rpb0t63tfItwHF99Y5t\nZXsrlyRJkiQNwYEmdzcBu0a8XAWs7ys/t42aeSqwvXXf/FPgTUmOaAOpvKmVSZIkSZKGYJ/9FZN8\nFJgFjk6ymd6olxcBNyQ5H3gEeHurfitwJrAR+BbwLoCq+lqSXwc+1+r9p6rafZAWSZIkSdIB2mdy\nV1Xv2Mui0/ZQt4AL9rKdq4Cr9is6SZIkSdK8HGi3TEmSJEnSBDG5kyRJkqQOMLmTJEmSpA4wuZMk\nSZKkDjC5kyRJkqQOMLmTJEmSpA4wuZMkSZKkDjC5kyRJkqQOMLmTJEmSpA4wuZMkSZKkDjC5kyRJ\nkqQOMLmTJEmSpA4wuZMkSZKkDjC5kyRpAiVZmuTGJF9I8mCS1yY5MsltSR5q70e0uklyWZKNSe5N\ncvK445ckjZ7JnSRJk+lS4E+q6hXAK4EHgbXA7VV1AnB7mwc4AzihvVYDV4w+XEnSuJncSZI0YZIc\nDrweuBKgqr5bVU8BK4FrWrVrgLPa9Erg2uq5A1ia5JgRhy1JGrMl4w5AkiQ9y/HAV4CPJHklcDdw\nITBTVVtbnceAmTa9DHi0b/3NrWxrXxlJVtO7s8fMzAxzc3MDBbljx46Bt7HQpiFGmI44jXF4piHO\nrsS45qSdownmOYyyLU3uJEmaPEuAk4FfrKo7k1zKM10wAaiqSlL7s9GqWgesA1ixYkXNzs4OFOTc\n3ByDbmOhTUOMMB1xGuPwTEOcXYnxvLW3jCaY53D16YeOrC3tlilJ0uTZDGyuqjvb/I30kr3Hd3W3\nbO/b2vItwHF96x/byiRJi4jJnSRJE6aqHgMeTfLyVnQa8ABwE7Cqla0C1rfpm4Bz26iZpwLb+7pv\nSpIWiYG6ZSb5ZeDngAI2AO8CjgGuB46i94zAO6vqu0kOBq4FfhT4KvAzVbVpkP1LktRhvwhcl+QF\nwMP0zrHPA25Icj7wCPD2VvdW4ExgI/CtVleStMgccHKXZBnwS8CJVfXtJDcAZ9M7uVxSVdcn+e/A\n+fSGZD4feLKqXpbkbOCDwM8MfASSJHVQVd0DrNjDotP2ULeACxY8KEnSRBu0W+YS4JAkS4AX0RuV\n6430ng2AZw/TvGv45huB05JkwP1LkiRJkhjgzl1VbUnym8CXgW8Dn6TXDfOpqto15uiuoZihb5jm\nqtqZZDu9rptP9G932MM0zxwy/iFQuzCMrGyn+bCN5sd2kiRJC2GQbplH0LsbdzzwFPCHwOmDBjTs\nYZovv249F28Y7y8+bDpndqz735dpGOp2EthO+2YbzY/tJEmSFsIg3TJ/AvhSVX2lqv4e+ATwOmBp\n66YJ3z8U89PDNLflh9MbWEWSJEmSNKBBkrsvA6cmeVF7dm7XMM2fBt7a6uw+TPOu4ZvfCnyqPQAu\nSZIkSRrQASd37YdVbwQ+T+9nEJ5Hrzvl+4D3JtlI75m6K9sqVwJHtfL3AmsHiFuSJEmS1Gegh9Gq\n6gPAB3Yrfhg4ZQ91vwO8bZD9SZIkSZL2bNCfQpAkSZIkTQCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ\n6gCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ6gCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ6gCTO0mSJEnq\nAJM7SZIkSeoAkztJkiRJ6gCTO0mSJEnqAJM7SZIkSeoAkztJkiRJ6gCTO0mSJEnqAJM7SZIkSeoA\nkztJkiRJ6oCBkrskS5PcmOQLSR5M8tokRya5LclD7f2IVjdJLkuyMcm9SU4eziFIkiRJkga9c3cp\n8CdV9QrglcCDwFrg9qo6Abi9zQOcAZzQXquBKwbctyRJkiSpOeDkLsnhwOuBKwGq6rtV9RSwErim\nVbsGOKtNrwSurZ47gKVJjjngyCVJkiRJT1sywLrHA18BPpLklcDdwIXATFVtbXUeA2ba9DLg0b71\nN7eyrX1lJFlN784eMzMzzM3NDRAizBwCa07aOdA2BjXoMSy0HTt2THyMk8B22jfbaH5sJ0mStBAG\nSe6WACcDv1hVdya5lGe6YAJQVZWk9mejVbUOWAewYsWKmp2dHSBEuPy69Vy8YZDDHNymc2bHuv99\nmZubY9B2Xgxsp32zjebHdpIkSQthkGfuNgObq+rONn8jvWTv8V3dLdv7trZ8C3Bc3/rHtjJJkrQH\nSQ5K8pdJbm7zxye5sw1O9rEkL2jlB7f5jW358nHGLUkajwNO7qrqMeDRJC9vRacBDwA3Aata2Spg\nfZu+CTi3jZp5KrC9r/umJEl6tgvpDVa2yweBS6rqZcCTwPmt/HzgyVZ+SasnSVpkBh0t8xeB65Lc\nC7wK+C/ARcBPJnkI+Ik2D3Ar8DCwEfhd4BcG3LckSZ2V5FjgzcDvtfkAb6TXUwaePWjZrsHMbgRO\na/UlSYvIQA+jVdU9wIo9LDptD3ULuGCQ/UmStIj8FvArwIvb/FHAU1W1a5SwXQOTQd+gZVW1M8n2\nVv+J/g0Oe9CyaRgcaBpihOmI0xiHZxri7EqM4x5YEUbbluMdaUSSJD1Lkp8GtlXV3Ulmh7XdYQ9a\nNg2DA01DjDAdcRrj8ExDnF2J8by1t4wmmOdw9emHjqwtTe4kSZo8rwPekuRM4IXADwCX0vuN2CXt\n7l3/wGS7Bi3bnGQJcDjw1dGHLUkap0GfuZMkSUNWVe+vqmOrajlwNvCpqjoH+DTw1lZt90HLdg1m\n9tZWf79+ikiSNP1M7iRJmh7vA96bZCO9Z+qubOVXAke18vey2+/OSpIWB7tlSpI0wapqDphr0w8D\np+yhzneAt400MEnSxPHOnSRJkiR1gMmdJEmSJHWAyZ0kSZIkdYDJnSRJkiR1gMmdJEmSJHWAyZ0k\nSZIkdYDJnSRJkiR1gMmdJEmSJHWAyZ0kSZIkdYDJnSRJkiR1wJJxByBJkqbThi3bOW/tLWONYdNF\nbx7r/iVpknjnTpIkSZI6wOROkiRJkjrA5E6SJEmSOsDkTpIkSZI6YODkLslBSf4yyc1t/vgkdybZ\nmORjSV7Qyg9u8xvb8uWD7luSJEmS1DOMO3cXAg/2zX8QuKSqXgY8CZzfys8Hnmzll7R6kiRJkqQh\nGCi5S3Is8Gbg99p8gDcCN7Yq1wBntemVbZ62/LRWX5IkSZI0oEF/5+63gF8BXtzmjwKeqqqdbX4z\nsKxNLwMeBaiqnUm2t/pP9G8wyWpgNcDMzAxzc3MDBThzCKw5aee+Ky6gQY9hoe3YsWPiY5wEttO+\n2UbzYztJkqSFcMDJXZKfBrZV1d1JZocVUFWtA9YBrFixomZnB9v05det5+IN4/2t9k3nzI51//sy\nNzfHoO28GNhO+2YbzY/tJEkLY8OW7Zy39paxxrDpojePdf9a3AbJel4HvCXJmcALgR8ALgWWJlnS\n7t4dC2xp9bcAxwGbkywBDge+OsD+JUmSJEnNAT9zV1Xvr6pjq2o5cDbwqao6B/g08NZWbRWwvk3f\n1OZpyz9VVXWg+5ckSZIkPWMhfufufcB7k2yk90zdla38SuCoVv5eYO0C7FuSJEmSFqWhPIxWVXPA\nXJt+GDhlD3W+A7xtGPuTJEmSJH2/hbhzJ0mSJEkaMZM7SZIkSeoAkztJkiRJ6gCTO0mSJEnqAJM7\nSZIkSeoAkztJkiRJ6gCTO0mSJEnqAJM7SZIkSeoAkztJkiZMkuOSfDrJA0nuT3JhKz8yyW1JHmrv\nR7TyJLksycYk9yY5ebxHIEkaB5M7SZImz05gTVWdCJwKXJDkRGAtcHtVnQDc3uYBzgBOaK/VwBWj\nD1mSNG4md5IkTZiq2lpVn2/T3wAeBJYBK4FrWrVrgLPa9Erg2uq5A1ia5JgRhy1JGrMl4w5AkiTt\nXZLlwKuBO4GZqtraFj0GzLTpZcCjfattbmVb+8pIsprenT1mZmaYm5sbKLaZQ2DNSTsH2sag9nUM\nO3bsGPg4R2Ea4pyGGKfhMwnT0ZZdiXHcnwcYbVua3EmSNKGSHAZ8HHhPVX09ydPLqqqS1P5sr6rW\nAesAVqxYUbOzswPFd/l167l4w3i/Smw6Z/Y5l8/NzTHocY7CNMQ5DTFOw2cSpqMtuxLjeWtvGU0w\nz+Hq0w8dWVvaLVOSpAmU5Pn0ErvrquoTrfjxXd0t2/u2Vr4FOK5v9WNbmSRpETG5kyRpwqR3i+5K\n4MGq+lDfopuAVW16FbC+r/zcNmrmqcD2vu6bkqRFwm6ZkiRNntcB7wQ2JLmnlf0qcBFwQ5LzgUeA\nt7dltwJnAhuBbwHvGm24kqRJYHInSdKEqarPANnL4tP2UL+ACxY0KEnSxLNbpiRJkiR1gMmdJEmS\nJHWAyZ0kSZIkdcABJ3dJjkvy6SQPJLk/yYWt/MgktyV5qL0f0cqT5LIkG5Pcm+TkYR2EJEmSJC12\ng9y52wmsqaoTgVOBC5KcCKwFbq+qE4Db2zzAGcAJ7bUauGKAfUuSJEmS+hxwcldVW6vq8236G8CD\nwDJgJXBNq3YNcFabXglcWz13AEt3/RCrJEmSJGkwQ/kphCTLgVcDdwIzfT+c+hgw06aXAY/2rba5\nlX3fj6wmWU3vzh4zMzPMzc0NFNvMIbDmpJ0DbWNQgx7DQtuxY8fExzgJbKd9s43mx3aSJEkLYeDk\nLslhwMeB91TV15NnfpanqipJ7c/2qmodsA5gxYoVNTs7O1B8l1+3nos3jPfn/DadMzvW/e/L3Nwc\ng7bzYmA77ZttND+2kyRJWggDjZaZ5Pn0ErvrquoTrfjxXd0t2/u2Vr4FOK5v9WNbmSRJkiRpQIOM\nlhngSuDBqvpQ36KbgFVtehWwvq/83DZq5qnA9r7um5IkSZKkAQzSX/F1wDuBDUnuaWW/ClwE3JDk\nfOAR4O1t2a3AmcBG4FvAuwbY91RZvvaWcYfApovePO4QJEmSJC2gA07uquozQPay+LQ91C/gggPd\nnyRJkiRp7wZ65k6SJEmSNBlM7iRJkiSpA0zuJEmSJKkDTO4kSZIkqQNM7iRJkiSpA0zuJEmSJKkD\nBvmdO02R5/qtvTUn7eS8EfwWn7+1J0mSJC0c79xJkiRJUgeY3EmSJElSB5jcSZIkSVIHmNxJkiRJ\nUgeY3EmSJElSB5jcSZIkSVIHmNxJkiRJUgeY3EmSJElSB5jcSZIkSVIHmNxJkiRJUgeY3EmSJElS\nB5jcSZIkSVIHjDy5S3J6ki8m2Zhk7aj3L0lSV3mOlaTFbaTJXZKDgA8DZwAnAu9IcuIoY5AkqYs8\nx0qSlox4f6cAG6vqYYAk1wMrgQdGHIfGYPnaW8YdApsuevO4Q5CkheI5VpIWuVEnd8uAR/vmNwOv\nGXEMWsQGSTDXnLST8yYgQZ1k+9NGJtrP8MKHhsRzrCQtcqNO7vYpyWpgdZvdkeSLA27yaOCJAbfR\nab9kG82L7bRv+9NG+eACBzPZJu6zNIR/j5cOIQwtsC6eY+fx2R17jPM0DXEa4zzM8//Tscc5D8Y4\nJG/44MBxzvscO+rkbgtwXN/8sa3saVW1Dlg3rB0muauqVgxre11kG82P7bRvttH82E5aIJ5j92Aa\nYoTpiNMYh2ca4jTG4RllnKMeLfNzwAlJjk/yAuBs4KYRxyBJUhd5jpWkRW6kd+6qameSdwN/ChwE\nXFVV948yBkmSushzrCRp5M/cVdWtwK0j3OXQup90mG00P7bTvtlG82M7aUF4jt2jaYgRpiNOYxye\naYjTGIdnZHGmqka1L0mSJEnSAhn1M3eSJEmSpAXQ2eQuyelJvphkY5K1445nEiU5LsmnkzyQ5P4k\nF447pkmV5KAkf5nk5nHHMqmSLE1yY5IvJHkwyWvHHdOkSfLL7W/tviQfTfLCccck7UmSq5JsS3Lf\nXpYnyWXtHHtvkpP7lq1K8lB7rRpjjOe02DYk+T9JXtm3bFMrvyfJXQsV4zzjnE2yvcVyT5L/0Lds\nJN9l5hHjv+uL774k30tyZFs2kracz3eWcX8u5xnj2D+X84xzrJ/LecY4CZ/LFyb5bJK/anH+2h7q\nHJzkY6297kyyvG/Z+1v5F5P81FCCqqrOveg9SP43wD8GXgD8FXDiuOOatBdwDHBym34x8Ne2017b\n6r3AHwA3jzuWSX0B1wA/16ZfACwdd0yT9KL3A9NfAg5p8zcA5407Ll++9vQCXg+cDNy3l+VnAn8M\nBDgVuLOVHwk83N6PaNNHjCnGf7Fr38AZu2Js85uAoyekLWf3dG4Z5XeZfcW4W91/CXxq1G05n+8s\n4/5czjPGsX8u5xnnWD+X84lxQj6XAQ5r088H7gRO3a3OLwD/vU2fDXysTZ/Y2u9g4PjWrgcNGlNX\n79ydAmysqoer6rvA9cDKMcc0capqa1X9/+3df/BddZ3f8eerfGGFRAmKkyKJJlOpWwtdxe8iFmu/\nMeqiWONO3R0cVsGyk84WLS60K9pOnW67szi7+GOttZMSFbuRyEYsGfyFRTLW3YGVIDVAXDdihEQk\nYDAYZFfjvvvHPdGv4Ztf33O/33Pv4fmY+U7uOfecc1/35nyT87rnc8+9o7n9Q2ArgwNQTZNkCXAe\ncHXXWUZVkhMZHBysBaiqH1fVD7pNNZImgOOTTAAnAN/tOI80o6r6MrD7EIusAj5eA7cCi5KcAvwa\n8MWq2l1VjwBfBM7tImNV/UWTAeBWBt/7N++O4LU8mHk7ljnKjG8Erp2LHIdyhMcsne6XR5JxFPbL\nlsd/87JfziJjV/tlVdXeZvLY5ufAC5qsYvAGOMAGYGWSNPPXV9XfVtW3gW0MXt9W+lruTgXunza9\nA0vLITWniF/I4B0H/aL3A78H/F3XQUbYcuAh4KMZDF+9OsmCrkONkqraCfwxcB/wALCnqm7qNpU0\nawf7f3ZU//+9mMEZnf0KuCnJ5iSrO8o03UuaYV2fS/KPm3kj91omOYFBKfrUtNnz/loe4phlZPbL\nIzyu6ny/PEzOkdgvD/dadr1fZvDRnTuBXQzeRDjofllV+4A9wDOYo9eyr+VORyHJQga/EG+vqke7\nzjNKkrwW2FVVm7vOMuImGAzp+XBVvRB4DPCzrtMkOYnBu3TLgWcBC5L8VreppP5LsoLBQfQ7ps1+\naVWdyWBY3CVJXtZJuIE7gOdU1a8AHwT+d4dZDudfAH9eVdPP8s3razkOxyxHknEU9svD5ByJ/fII\n/7473S+r6qdV9QIGZ2HPSnL6XD3WkehrudsJLJ02vaSZpwMkOZbBL826qrq+6zwj6BzgdUm2Mxh6\n8PIkf9ptpJG0A9gx7d2qDQzKnn7uFcC3q+qhqvoJcD2Dz15I4+hg/8+O1P+/Sf4JgyH1q6rq+/vn\nN2fSqapdwKcZwlCo2aqqR/cP66rB9xQem+RkRuy1bJzPAUPf5vO1PIJjls73yyM5rhqF/fJwOUdh\nvzyKY9RO98tpj/kD4BaeOOT3Z69Z87GME4HvM0evZV/L3VeB05IsT3Icg7/0jR1nGjnNeN+1wNaq\nem/XeUZRVb2zqpZU1TIG+9GXqsqzLQeoqu8B9yd5XjNrJXBPh5FG0X3A2UlOaH73VjL4DIE0jjYC\nb87A2QyGGT8AfAF4VZKTmrPVr2rmzbskz2bwJsqbquqb0+YvSPLU/bebjDNeJXI+JPn7zb8JJDmL\nwbHZ9xmxY5nms9X/HLhh2rx5ey2P8Jil0/3ySDKOwn55hDk73S+P9Bh1BPbLZyZZ1Nw+Hngl8I0D\nFtsI7L9C6xsYHEtWM//8DK6muRw4DfjLtpkm2m5gFFXVviRvZfCLewzwkaq6u+NYo+gc4E3Almas\nMMC7mndopKP1NmBd84/9vcBbOs4zUqrqtiQbGAx12Qd8DVjTbSppZkmuZXC1vJOT7ADezeBCAVTV\n/wA+y+DKhNuAH9H8vlfV7iT/hcEBIMDvHzBUaj4z/icGn2v5780x6r6qmgQWA59u5k0An6iqz89F\nxiPM+Qbgd5LsAx4Hzm8O/ObtWOYIMgL8OnBTVT02bdX5fC1nPGYBnj0tZ9f75ZFkHIX98khydr1f\nHklG6H6/PAW4JskxDArwdVV1Y5LfB26vqo0MSur/SrKNwYWLzm+ew91JrmPwZvg+4JKq+mnbQBn8\nPUmSJEmSxllfh2VKkiRJ0pOK5U6SJEmSesByJ0mSJEk9YLmTJEmSpB6w3EmSJElSD1juJEmSJKkH\nLHeSJEmS1AOWO0mSJEnqAcudJEmSJPWA5U6SJEmSesByJ0mSJEk9YLmTJEmSpB6w3EmSJElSD1ju\nJEmSJKkHLHfSECW5IMlN06YryXO7zCRJkqQnB8udNAtJXprkL5LsSbI7yZ8n+dWqWldVrzrCbRyX\n5KokO5LsTbI9yfvnOrskSZL6aaLrANK4SfI04Ebgd4DrgOOAfwb87VFu6p3AJHAW8ADwHOBlw0sq\nSZKkJxPP3ElH7x8CVNW1VfXTqnq8qm6qqq8nuSjJVw5Y/jVJ7k3ycJI/SrL/9+5XgU9X1XdrYHtV\nfXz/Ss2ZvHcmuSfJI0k+muQp8/QcJUmSNGYsd9LR+ybw0yTXJHl1kpMOs/yvMzhDdyawCvhXzfxb\ngcuS/JskZyTJDOteAPwa8A8YlMr/OJRnIEmSpN6x3ElHqaoeBV4KFPA/gYeSbEyy+CCrvKeqdlfV\nfcD7gTc28/8QeA+DAnc7sDPJhQes+9+q6v6q2g38wbR1JUmSpF9guZNmoaq2VtVFVbUEOB14FoPi\nNpP7p93+TrMszZDOD1XVOcAiBuXtI0n+0eHWlSRJkg5kuZNaqqpvAB9jUPJmsnTa7WcD351hG49X\n1YeAR4DnH826kiRJEljupKOW5JeTXJ5kSTO9lMFwyVsPssq/T3JSs9ylwCeb9d6eZCrJ8UkmmiGZ\nTwW+Nm3dS5IsSfJ04D/sX1eSJEk6kOVOOno/BF4M3JbkMQal7i7g8oMsfwOwGbgT+Aywtpn/I+Aq\n4HvAw8AlwL+sqnunrfsJ4CbgXuBbwH8d6jORJElSb6Squs4gaQZJtgO/XVX/p+sskiRJGn2euZMk\nSZKkHrDcSZIkSVIPOCxTkiRJknrAM3eSJEmS1AMTXQc4lJNPPrmWLVvWahuPPfYYCxYsGE6gOTIO\nGWE8cppxeMYhpxmHp23OzZs3P1xVzxxiJEmSdJRGutwtW7aM22+/vdU2Nm3axNTU1HACzZFxyAjj\nkdOMwzMOOc04PG1zJvnO8NJIkqTZcFimJEmSJPWA5U6SJEmSesByJ0mSJEk9YLmTJEmSpB6w3EmS\nJElSD4z01TKHYcvOPVx0xWc6zbD9yvM6fXxJkiRJ/eeZO0mSJEnqAcudJEmSJPWA5U6SJEmSesBy\nJ0mSJEk9YLmTJEmSpB6w3EmSJElSD1juJEmSJKkHLHeSJEmS1AOWO0mSJEnqAcudJEmSJPWA5U6S\nJORCknoAAA/fSURBVEmSeqBVuUvyu0nuTnJXkmuTPCXJ8iS3JdmW5JNJjmuW/aVmeltz/7JhPAFJ\nkiRJUotyl+RU4N8Ck1V1OnAMcD7wHuB9VfVc4BHg4maVi4FHmvnva5aTJEmSJA1B22GZE8DxSSaA\nE4AHgJcDG5r7rwFe39xe1UzT3L8ySVo+viRJkiQJSFXNfuXkUuAPgMeBm4BLgVubs3MkWQp8rqpO\nT3IXcG5V7Wju+xbw4qp6+IBtrgZWAyxevPhF69evn3U+gF279/Dg46020doZp554yPv37t3LwoUL\n5ynN7I1DTjMOzzjkNOPwtM25YsWKzVU1OcRIkiTpKE3MdsUkJzE4G7cc+AHwZ8C5bQNV1RpgDcDk\n5GRNTU212t4H193AVVtm/TSHYvsFU4e8f9OmTbR9nvNhHHKacXjGIacZh2dcckqSpINrMyzzFcC3\nq+qhqvoJcD1wDrCoGaYJsATY2dzeCSwFaO4/Efh+i8eXJEmSJDXalLv7gLOTnNB8dm4lcA9wC/CG\nZpkLgRua2xubaZr7v1RtxoRKkiRJkn5m1uWuqm5jcGGUO4AtzbbWAO8ALkuyDXgGsLZZZS3wjGb+\nZcAVLXJLkiRJkqZp9WG0qno38O4DZt8LnDXDsn8D/Eabx5MkSZIkzaztVyFIkiRJkkaA5U6SJEmS\nesByJ0mSJEk9YLmTJEmSpB6w3EmSJElSD1juJEmSJKkHLHeSJEmS1AOWO0mSJEnqAcudJEmSJPWA\n5U6SJEmSesByJ0mSJEk9YLmTJEmSpB6w3EmSJElSD1juJEmSJKkHLHeSJEmS1AOWO0mSJEnqAcud\nJEmSJPWA5U6SJEmSesByJ0mSJEk9YLmTJEmSpB6w3EmSJElSD1juJEmSJKkHLHeSJEmS1AOtyl2S\nRUk2JPlGkq1JXpLk6Um+mOSvmz9PapZNkj9Jsi3J15OcOZynIEmSJElqe+buA8Dnq+qXgV8BtgJX\nADdX1WnAzc00wKuB05qf1cCHWz62JEmSJKkx63KX5ETgZcBagKr6cVX9AFgFXNMsdg3w+ub2KuDj\nNXArsCjJKbNOLkmSJEn6mVTV7FZMXgCsAe5hcNZuM3ApsLOqFjXLBHikqhYluRG4sqq+0tx3M/CO\nqrr9gO2uZnBmj8WLF79o/fr1s8q3367de3jw8VabaO2MU0885P179+5l4cKF85Rm9sYhpxmHZxxy\nmnF42uZcsWLF5qqaHGIkSZJ0lCZarnsm8Laqui3JB/j5EEwAqqqSHFV7rKo1DEojk5OTNTU11SIi\nfHDdDVy1pc3TbG/7BVOHvH/Tpk20fZ7zYRxymnF4xiGnGYdnXHJKkqSDa/OZux3Ajqq6rZnewKDs\nPbh/uGXz567m/p3A0mnrL2nmSZIkSZJamnW5q6rvAfcneV4zayWDIZobgQubeRcCNzS3NwJvbq6a\neTawp6oemO3jS5IkSZJ+ru14xbcB65IcB9wLvIVBYbwuycXAd4DfbJb9LPAaYBvwo2ZZSZIkSdIQ\ntCp3VXUnMNMH6FfOsGwBl7R5PEmSJEnSzNp+z50kSZIkaQRY7iRJkiSpByx3kiRJktQDljtJkiRJ\n6gHLnSRJkiT1gOVOkiRJknrAcidJkiRJPWC5kyRJkqQesNxJkiRJUg9Y7iRJkiSpByx3kiRJktQD\nljtJkiRJ6gHLnSRJkiT1gOVOkiRJknrAcidJkiRJPWC5kyRJkqQesNxJkiRJUg9Y7iRJkiSpByx3\nkiRJktQDljtJkiRJ6gHLnSRJkiT1gOVOkiRJknrAcidJkiRJPdC63CU5JsnXktzYTC9PcluSbUk+\nmeS4Zv4vNdPbmvuXtX1sSZIkSdLAMM7cXQpsnTb9HuB9VfVc4BHg4mb+xcAjzfz3NctJkiRJkoag\nVblLsgQ4D7i6mQ7wcmBDs8g1wOub26uaaZr7VzbLS5IkSZJaSlXNfuVkA/CHwFOBfwdcBNzanJ0j\nyVLgc1V1epK7gHOrakdz37eAF1fVwwdsczWwGmDx4sUvWr9+/azzAezavYcHH2+1idbOOPXEQ96/\nd+9eFi5cOE9pZm8ccppxeMYhpxmHp23OFStWbK6qySFGkiRJR2litismeS2wq6o2J5kaVqCqWgOs\nAZicnKypqXab/uC6G7hqy6yf5lBsv2DqkPdv2rSJts9zPoxDTjMOzzjkNOPwjEtOSZJ0cG1azznA\n65K8BngK8DTgA8CiJBNVtQ9YAuxslt8JLAV2JJkATgS+3+LxJUmSJEmNWX/mrqreWVVLqmoZcD7w\npaq6ALgFeEOz2IXADc3tjc00zf1fqjZjQiVJkiRJPzMX33P3DuCyJNuAZwBrm/lrgWc08y8DrpiD\nx5YkSZKkJ6WhfBitqjYBm5rb9wJnzbDM3wC/MYzHkyRJkiT9ork4cydJkiRJmmeWO0mSJEnqAcud\nJEmSJPWA5U6SJEmSesByJ0mSJEk9YLmTJEmSpB6w3EmSJElSDwzle+50aMuu+Mwh77/8jH1cdJhl\n2tp+5Xlzun1JkiRJ3fLMnSRJkiT1gOVOkiRJknrAcidJkiRJPWC5kyRJkqQesNxJkiRJUg9Y7iRJ\nkiSpByx3kiRJktQDljtJkiRJ6gHLnSRJkiT1gOVOkiRJknrAcidJkiRJPWC5kyRJkqQesNxJkiRJ\nUg9Y7iRJkiSpByx3kiRJktQDljtJkiRJ6oFZl7skS5PckuSeJHcnubSZ//QkX0zy182fJzXzk+RP\nkmxL8vUkZw7rSUiSJEnSk12bM3f7gMur6vnA2cAlSZ4PXAHcXFWnATc30wCvBk5rflYDH27x2JIk\nSZKkaWZd7qrqgaq6o7n9Q2ArcCqwCrimWewa4PXN7VXAx2vgVmBRklNmnVySJEmS9DOpqvYbSZYB\nXwZOB+6rqkXN/ACPVNWiJDcCV1bVV5r7bgbeUVW3H7Ct1QzO7LF48eIXrV+/vlW2Xbv38ODjrTYx\n5xYfz5xnPOPUE1tvY+/evSxcuHAIaeaOGYdnHHKacXja5lyxYsXmqpocYiRJknSUJtpuIMlC4FPA\n26vq0UGfG6iqSnJU7bGq1gBrACYnJ2tqaqpVvg+uu4GrtrR+mnPq8jP2zXnG7RdMtd7Gpk2baPv3\nMdfMODzjkNOMwzMuOSVJ0sG1ulpmkmMZFLt1VXV9M/vB/cMtmz93NfN3Akunrb6kmSdJkiRJaqnN\n1TIDrAW2VtV7p921EbiwuX0hcMO0+W9urpp5NrCnqh6Y7eNLkiRJkn6uzVjAc4A3AVuS3NnMexdw\nJXBdkouB7wC/2dz3WeA1wDbgR8BbWjy2JEmSJGmaWZe75sIoOcjdK2dYvoBLZvt4kiRJkqSDa/WZ\nO0mSJEnSaLDcSZIkSVIPWO4kSZIkqQcsd5IkSZLUA5Y7SZIkSeoBy50kSZIk9YDlTpIkSZJ6wHIn\nSZIkST1guZMkSZKkHrDcSZIkSVIPWO4kSZIkqQcsd5IkSZLUA5Y7SZIkSeoBy50kSZIk9YDlTpIk\nSZJ6wHInSZIkST1guZMkSZKkHrDcSZIkSVIPWO4kSZIkqQcmug6g+bHsis+03sblZ+zjohbb2X7l\nea0zSJIkSZqZZ+4kSZIkqQcsd5IkSZLUAw7LlObZlp17Wg1vHQaHyEqSJPXPvJe7JOcCHwCOAa6u\nqivnO4O6MYzP/R3O4T4XaKmRJElSX81ruUtyDPAh4JXADuCrSTZW1T3zmUOSRsl8vPFxOB87d0HX\nESRJUkvzfebuLGBbVd0LkGQ9sAqw3Enz6EjKRNurox6OZ1ElSZKGK1U1fw+WvAE4t6p+u5l+E/Di\nqnrrtGVWA6ubyecBf9XyYU8GHm65jbk2DhlhPHKacXjGIacZh6dtzudU1TOHFUaSJB29kbugSlWt\nAdYMa3tJbq+qyWFtby6MQ0YYj5xmHJ5xyGnG4RmXnJIk6eDm+6sQdgJLp00vaeZJkiRJklqY73L3\nVeC0JMuTHAecD2yc5wySJEmS1DvzOiyzqvYleSvwBQZfhfCRqrp7jh92aEM859A4ZITxyGnG4RmH\nnGYcnnHJKUmSDmJeL6giSZIkSZob8z0sU5IkSZI0Byx3kiRJktQDvS13Sc5N8ldJtiW5ous8M0ny\nkSS7ktzVdZaDSbI0yS1J7klyd5JLu840kyRPSfKXSf5fk/M/d53pYJIck+RrSW7sOstMkmxPsiXJ\nnUlu7zrPwSRZlGRDkm8k2ZrkJV1nmi7J85rXcP/Po0ne3nWuAyX53eZ35q4k1yZ5SteZJEnS7PTy\nM3dJjgG+CbwS2MHgKp1vrKp7Og12gCQvA/YCH6+q07vOM5MkpwCnVNUdSZ4KbAZeP4KvZYAFVbU3\nybHAV4BLq+rWjqM9QZLLgEngaVX12q7zHCjJdmCyqkb6i7eTXAP836q6urn67glV9YOuc82k+Tdp\nJ/DiqvpO13n2S3Iqg9+V51fV40muAz5bVR/rNpkkSZqNvp65OwvYVlX3VtWPgfXAqo4zPUFVfRnY\n3XWOQ6mqB6rqjub2D4GtwKndpnqiGtjbTB7b/IzcOxdJlgDnAVd3nWWcJTkReBmwFqCqfjyqxa6x\nEvjWKBW7aSaA45NMACcA3+04jyRJmqW+lrtTgfunTe9gBAvJuEmyDHghcFu3SWbWDHe8E9gFfLGq\nRjHn+4HfA/6u6yCHUMBNSTYnWd11mINYDjwEfLQZ4np1kgVdhzqE84Fruw5xoKraCfwxcB/wALCn\nqm7qNpUkSZqtvpY7DVmShcCngLdX1aNd55lJVf20ql4ALAHOSjJSQ12TvBbYVVWbu85yGC+tqjOB\nVwOXNMOHR80EcCbw4ap6IfAYMKqfrT0OeB3wZ11nOVCSkxiMalgOPAtYkOS3uk0lSZJmq6/lbiew\ndNr0kmaeZqH5DNungHVVdX3XeQ6nGZ53C3Bu11kOcA7wuuYzbeuBlyf5024jPVFzNoeq2gV8msEw\n51GzA9gx7ezsBgZlbxS9Grijqh7sOsgMXgF8u6oeqqqfANcD/7TjTJIkaZb6Wu6+CpyWZHnzrvn5\nwMaOM42l5kIla4GtVfXervMcTJJnJlnU3D6ewcV0vtFtql9UVe+sqiVVtYzBPvmlqhqpsyRJFjQX\nzqEZ5vgqYOSu5lpV3wPuT/K8ZtZKYKQu8jPNGxnBIZmN+4Czk5zQ/K6vZPC5WkmSNIYmug4wF6pq\nX5K3Al8AjgE+UlV3dxzrCZJcC0wBJyfZAby7qtZ2m+oJzgHeBGxpPs8G8K6q+myHmWZyCnBNc1XC\nvwdcV1Uj+VUDI24x8OnBcT4TwCeq6vPdRjqotwHrmjdw7gXe0nGeJ2gK8iuBf911lplU1W1JNgB3\nAPuArwFruk0lSZJmq5dfhSBJkiRJTzZ9HZYpSZIkSU8qljtJkiRJ6gHLnSRJkiT1gOVOkiRJknrA\ncidJkiRJPWC5kyRJkqQesNxJkiRJUg/8f4r1CpSiGVAhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108ca9208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_test.hist(bins=10, column=['Age','Fare','Pclass','SibSp','Parch'], figsize=(15,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation between features\n",
    "-------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jJyeHdbGraNG6XamWDa8byeXLl7h40XxQtGfnNsIKDTSyh1oRt5N07iTJiWfI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\nnCphFTD83gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1028ae400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,8])\n",
    "g = sns.heatmap(train_test[[\"Survived\",\"SibSp\",\"Parch\",\"Age\",\"Fare\",\"Pclass\",\"Female\"]].corr(),annot=True, cmap = \"coolwarm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rates of survival by feature values\n",
    "---------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SeuG2zpIkSdLocWqbJKk3bussSZIkjZZORyQlWZbkliTbklw4yf3nJ9ma5MYk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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d5c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20,5])\n",
    "plt.subplot(131)\n",
    "sns.barplot('Sex', 'Survived', data=train)\n",
    "plt.subplot(132)\n",
    "sns.barplot('Pclass', 'Survived', data=train)\n",
    "plt.subplot(133)\n",
    "sns.barplot('Embarked', 'Survived', data=train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---------\n",
    "## Including 'Age', 'Name' and 'Cabin'\n",
    "---------\n",
    "\n",
    "* ### 'Age' and 'Cabin' both suffer from a significant amount of missing values, lets think of a way to include/impute that data...\n",
    "* ### Before we impute age values, lets engineer a new feature 'Title' based on the 'Name' feature, and see if there is any correlation between title and age, that may allow us to better predict/impute missing 'Age' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1                                 Braund, Mr. Owen Harris\n",
       "2       Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "3                                  Heikkinen, Miss. Laina\n",
       "4            Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "5                                Allen, Mr. William Henry\n",
       "6                                        Moran, Mr. James\n",
       "7                                 McCarthy, Mr. Timothy J\n",
       "8                          Palsson, Master. Gosta Leonard\n",
       "9       Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
       "10                    Nasser, Mrs. Nicholas (Adele Achem)\n",
       "11                        Sandstrom, Miss. Marguerite Rut\n",
       "12                               Bonnell, Miss. Elizabeth\n",
       "13                         Saundercock, Mr. William Henry\n",
       "14                            Andersson, Mr. Anders Johan\n",
       "15                   Vestrom, Miss. Hulda Amanda Adolfina\n",
       "16                       Hewlett, Mrs. (Mary D Kingcome) \n",
       "17                                   Rice, Master. Eugene\n",
       "18                           Williams, Mr. Charles Eugene\n",
       "19      Vander Planke, Mrs. Julius (Emelia Maria Vande...\n",
       "20                                Masselmani, Mrs. Fatima\n",
       "21                                   Fynney, Mr. Joseph J\n",
       "22                                  Beesley, Mr. Lawrence\n",
       "23                            McGowan, Miss. Anna \"Annie\"\n",
       "24                           Sloper, Mr. William Thompson\n",
       "25                          Palsson, Miss. Torborg Danira\n",
       "26      Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...\n",
       "27                                Emir, Mr. Farred Chehab\n",
       "28                         Fortune, Mr. Charles Alexander\n",
       "29                          O'Dwyer, Miss. Ellen \"Nellie\"\n",
       "30                                    Todoroff, Mr. Lalio\n",
       "                              ...                        \n",
       "1280                                 Canavan, Mr. Patrick\n",
       "1281                          Palsson, Master. Paul Folke\n",
       "1282                           Payne, Mr. Vivian Ponsonby\n",
       "1283       Lines, Mrs. Ernest H (Elizabeth Lindsey James)\n",
       "1284                        Abbott, Master. Eugene Joseph\n",
       "1285                                 Gilbert, Mr. William\n",
       "1286                             Kink-Heilmann, Mr. Anton\n",
       "1287       Smith, Mrs. Lucien Philip (Mary Eloise Hughes)\n",
       "1288                                 Colbert, Mr. Patrick\n",
       "1289    Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...\n",
       "1290                       Larsson-Rondberg, Mr. Edvard A\n",
       "1291                             Conlon, Mr. Thomas Henry\n",
       "1292                              Bonnell, Miss. Caroline\n",
       "1293                                      Gale, Mr. Harry\n",
       "1294                       Gibson, Miss. Dorothy Winifred\n",
       "1295                               Carrau, Mr. Jose Pedro\n",
       "1296                         Frauenthal, Mr. Isaac Gerald\n",
       "1297         Nourney, Mr. Alfred (Baron von Drachstedt\")\"\n",
       "1298                            Ware, Mr. William Jeffery\n",
       "1299                           Widener, Mr. George Dunton\n",
       "1300                      Riordan, Miss. Johanna Hannah\"\"\n",
       "1301                            Peacock, Miss. Treasteall\n",
       "1302                               Naughton, Miss. Hannah\n",
       "1303      Minahan, Mrs. William Edward (Lillian E Thorpe)\n",
       "1304                       Henriksson, Miss. Jenny Lovisa\n",
       "1305                                   Spector, Mr. Woolf\n",
       "1306                         Oliva y Ocana, Dona. Fermina\n",
       "1307                         Saether, Mr. Simon Sivertsen\n",
       "1308                                  Ware, Mr. Frederick\n",
       "1309                             Peter, Master. Michael J\n",
       "Name: Name, Length: 1309, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capt</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Col</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Countess</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Don</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dona</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dr</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jonkheer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lady</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Major</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Master</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Miss</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mlle</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mme</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mr</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ms</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rev</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sir</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0    1\n",
       "0       Capt    1\n",
       "1        Col    4\n",
       "2   Countess    1\n",
       "3        Don    1\n",
       "4       Dona    1\n",
       "5         Dr    8\n",
       "6   Jonkheer    1\n",
       "7       Lady    1\n",
       "8      Major    2\n",
       "9     Master   61\n",
       "10      Miss  260\n",
       "11      Mlle    2\n",
       "12       Mme    1\n",
       "13        Mr  757\n",
       "14       Mrs  197\n",
       "15        Ms    2\n",
       "16       Rev    8\n",
       "17       Sir    1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define function to extract titles from passenger names\n",
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "train_test['Title'] = train_test['Name'].apply(get_title)\n",
    "\n",
    "titles = pd.Series(np.unique(train_test['Title'],return_counts=True)[0])\n",
    "counts = pd.Series(np.unique(train_test['Title'],return_counts=True)[1])\n",
    "pd.concat([titles,counts], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looks like the major titles are 'Mr','Master','Mrs','Miss'. After some research, it appears some of the other title values (e.g. 'Ms') may contain the same information as other values (e.g. 'Miss'), in this case \"an unmarried woman\". Since we can infer that those two superifically different titles actually confer the same basic information, lets standardize them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Titles and Meanings\n",
    "---------------\n",
    "* #### Capt: Profession (Male)\n",
    "* #### Col: Profession (Male)\n",
    "* #### Countess: Nobility (Female)\n",
    "* #### Don: Nobility (Male)\n",
    "* #### Dr: Profession (Male)\n",
    "* #### Jonkheer: Nobility (Male)\n",
    "* #### Lady: Nobility (Female)\n",
    "* #### Major: Profession (Male)\t\n",
    "* #### Master: Generic young (Male)\n",
    "* #### Miss: Generic unmarried(Female)\n",
    "* #### Mlle: Generic unmarried (Female)\n",
    "* #### Mme: Generic unmarried (Female)\n",
    "* #### Mr:\tGeneric (Male)\n",
    "* #### Mrs:\tGeneric married (Female)\n",
    "* #### Ms: Geneirc unmarried (Female)\n",
    "* #### Rev:\tProfession (Male)\n",
    "* #### Sir: Nobility (Male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test['Title'] = train_test['Title'].replace(['Mme', 'Mlle','Ms'], 'Miss')\n",
    "train_test['Title'] = train_test['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Distinguished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distinguished</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Master</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miss</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mr</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0    1\n",
       "0  Distinguished   29\n",
       "1         Master   61\n",
       "2           Miss  265\n",
       "3             Mr  757\n",
       "4            Mrs  197"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.Series(np.unique(train_test['Title'],return_counts=True)[0])\n",
    "counts = pd.Series(np.unique(train_test['Title'],return_counts=True)[1])\n",
    "pd.concat([titles,counts], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets make dummy features for the Title categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Embarked_Q</th>\n",
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       "      <th>Age</th>\n",
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       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "1             7.2500      0       3      1       0.0           0           0   \n",
       "2            71.2833      0       1      1       1.0           1           0   \n",
       "3             7.9250      0       3      0       1.0           0           0   \n",
       "4            53.1000      0       1      1       1.0           0           0   \n",
       "5             8.0500      0       3      0       0.0           0           0   \n",
       "\n",
       "             Embarked_S  Female   Age Cabin  \\\n",
       "PassengerId                                   \n",
       "1                     1       0  22.0   NaN   \n",
       "2                     0       1  38.0   C85   \n",
       "3                     1       1  26.0   NaN   \n",
       "4                     1       1  35.0  C123   \n",
       "5                     1       0  35.0   NaN   \n",
       "\n",
       "                                                          Name Title  \\\n",
       "PassengerId                                                            \n",
       "1                                      Braund, Mr. Owen Harris    Mr   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...   Mrs   \n",
       "3                                       Heikkinen, Miss. Laina  Miss   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)   Mrs   \n",
       "5                                     Allen, Mr. William Henry    Mr   \n",
       "\n",
       "             Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                        \n",
       "1                        0       0     0   1    0  \n",
       "2                        0       0     0   0    1  \n",
       "3                        0       0     1   0    0  \n",
       "4                        0       0     0   0    1  \n",
       "5                        0       0     0   1    0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.get_dummies(train_test['Title'])\n",
    "train_test = train_test.join(titles)\n",
    "train_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>E46</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.1333</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16.7000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>G6</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>C103</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31.2750</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>29.1250</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>D56</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31.3875</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>263.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canavan, Mr. Patrick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>21.0750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>93.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B24</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>39.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>D28</td>\n",
       "      <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>20.2500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gilbert, Mr. William</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>22.0250</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kink-Heilmann, Mr. Anton</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>60.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>C31</td>\n",
       "      <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>79.2000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>B41</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>7.7333</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Conlon, Mr. Thomas Henry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>164.8667</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>C7</td>\n",
       "      <td>Bonnell, Miss. Caroline</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>21.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gale, Mr. Harry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>59.4000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>47.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>27.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>D40</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>13.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>D38</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>10.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ware, Mr. William Jeffery</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>211.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>C80</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>7.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>13.7750</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>90.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>C105</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>Distinguished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                     \n",
       "1              7.2500      0       3      1       0.0           0           0   \n",
       "2             71.2833      0       1      1       1.0           1           0   \n",
       "3              7.9250      0       3      0       1.0           0           0   \n",
       "4             53.1000      0       1      1       1.0           0           0   \n",
       "5              8.0500      0       3      0       0.0           0           0   \n",
       "6              8.4583      0       3      0       0.0           0           1   \n",
       "7             51.8625      0       1      0       0.0           0           0   \n",
       "8             21.0750      1       3      3       0.0           0           0   \n",
       "9             11.1333      2       3      0       1.0           0           0   \n",
       "10            30.0708      0       2      1       1.0           1           0   \n",
       "11            16.7000      1       3      1       1.0           0           0   \n",
       "12            26.5500      0       1      0       1.0           0           0   \n",
       "13             8.0500      0       3      0       0.0           0           0   \n",
       "14            31.2750      5       3      1       0.0           0           0   \n",
       "15             7.8542      0       3      0       0.0           0           0   \n",
       "16            16.0000      0       2      0       1.0           0           0   \n",
       "17            29.1250      1       3      4       0.0           0           1   \n",
       "18            13.0000      0       2      0       1.0           0           0   \n",
       "19            18.0000      0       3      1       0.0           0           0   \n",
       "20             7.2250      0       3      0       1.0           1           0   \n",
       "21            26.0000      0       2      0       0.0           0           0   \n",
       "22            13.0000      0       2      0       1.0           0           0   \n",
       "23             8.0292      0       3      0       1.0           0           1   \n",
       "24            35.5000      0       1      0       1.0           0           0   \n",
       "25            21.0750      1       3      3       0.0           0           0   \n",
       "26            31.3875      5       3      1       1.0           0           0   \n",
       "27             7.2250      0       3      0       0.0           1           0   \n",
       "28           263.0000      2       1      3       0.0           0           0   \n",
       "29             7.8792      0       3      0       1.0           0           1   \n",
       "30             7.8958      0       3      0       0.0           0           0   \n",
       "...               ...    ...     ...    ...       ...         ...         ...   \n",
       "1280           7.7500      0       3      0       NaN           0           1   \n",
       "1281          21.0750      1       3      3       NaN           0           0   \n",
       "1282          93.5000      0       1      0       NaN           0           0   \n",
       "1283          39.4000      1       1      0       NaN           0           0   \n",
       "1284          20.2500      2       3      0       NaN           0           0   \n",
       "1285          10.5000      0       2      0       NaN           0           0   \n",
       "1286          22.0250      1       3      3       NaN           0           0   \n",
       "1287          60.0000      0       1      1       NaN           0           0   \n",
       "1288           7.2500      0       3      0       NaN           0           1   \n",
       "1289          79.2000      1       1      1       NaN           1           0   \n",
       "1290           7.7750      0       3      0       NaN           0           0   \n",
       "1291           7.7333      0       3      0       NaN           0           1   \n",
       "1292         164.8667      0       1      0       NaN           0           0   \n",
       "1293          21.0000      0       2      1       NaN           0           0   \n",
       "1294          59.4000      1       1      0       NaN           1           0   \n",
       "1295          47.1000      0       1      0       NaN           0           0   \n",
       "1296          27.7208      0       1      1       NaN           1           0   \n",
       "1297          13.8625      0       2      0       NaN           1           0   \n",
       "1298          10.5000      0       2      1       NaN           0           0   \n",
       "1299         211.5000      1       1      1       NaN           1           0   \n",
       "1300           7.7208      0       3      0       NaN           0           1   \n",
       "1301          13.7750      1       3      1       NaN           0           0   \n",
       "1302           7.7500      0       3      0       NaN           0           1   \n",
       "1303          90.0000      0       1      1       NaN           0           1   \n",
       "1304           7.7750      0       3      0       NaN           0           0   \n",
       "1305           8.0500      0       3      0       NaN           0           0   \n",
       "1306         108.9000      0       1      0       NaN           1           0   \n",
       "1307           7.2500      0       3      0       NaN           0           0   \n",
       "1308           8.0500      0       3      0       NaN           0           0   \n",
       "1309          22.3583      1       3      1       NaN           1           0   \n",
       "\n",
       "             Embarked_S  Female   Age        Cabin  \\\n",
       "PassengerId                                          \n",
       "1                     1       0  22.0          NaN   \n",
       "2                     0       1  38.0          C85   \n",
       "3                     1       1  26.0          NaN   \n",
       "4                     1       1  35.0         C123   \n",
       "5                     1       0  35.0          NaN   \n",
       "6                     0       0   NaN          NaN   \n",
       "7                     1       0  54.0          E46   \n",
       "8                     1       0   2.0          NaN   \n",
       "9                     1       1  27.0          NaN   \n",
       "10                    0       1  14.0          NaN   \n",
       "11                    1       1   4.0           G6   \n",
       "12                    1       1  58.0         C103   \n",
       "13                    1       0  20.0          NaN   \n",
       "14                    1       0  39.0          NaN   \n",
       "15                    1       1  14.0          NaN   \n",
       "16                    1       1  55.0          NaN   \n",
       "17                    0       0   2.0          NaN   \n",
       "18                    1       0   NaN          NaN   \n",
       "19                    1       1  31.0          NaN   \n",
       "20                    0       1   NaN          NaN   \n",
       "21                    1       0  35.0          NaN   \n",
       "22                    1       0  34.0          D56   \n",
       "23                    0       1  15.0          NaN   \n",
       "24                    1       0  28.0           A6   \n",
       "25                    1       1   8.0          NaN   \n",
       "26                    1       1  38.0          NaN   \n",
       "27                    0       0   NaN          NaN   \n",
       "28                    1       0  19.0  C23 C25 C27   \n",
       "29                    0       1   NaN          NaN   \n",
       "30                    1       0   NaN          NaN   \n",
       "...                 ...     ...   ...          ...   \n",
       "1280                  0       0  21.0          NaN   \n",
       "1281                  1       0   6.0          NaN   \n",
       "1282                  1       0  23.0          B24   \n",
       "1283                  1       1  51.0          D28   \n",
       "1284                  1       0  13.0          NaN   \n",
       "1285                  1       0  47.0          NaN   \n",
       "1286                  1       0  29.0          NaN   \n",
       "1287                  1       1  18.0          C31   \n",
       "1288                  0       0  24.0          NaN   \n",
       "1289                  0       1  48.0          B41   \n",
       "1290                  1       0  22.0          NaN   \n",
       "1291                  0       0  31.0          NaN   \n",
       "1292                  1       1  30.0           C7   \n",
       "1293                  1       0  38.0          NaN   \n",
       "1294                  0       1  22.0          NaN   \n",
       "1295                  1       0  17.0          NaN   \n",
       "1296                  0       0  43.0          D40   \n",
       "1297                  0       0  20.0          D38   \n",
       "1298                  1       0  23.0          NaN   \n",
       "1299                  0       0  50.0          C80   \n",
       "1300                  0       1   NaN          NaN   \n",
       "1301                  1       1   3.0          NaN   \n",
       "1302                  0       1   NaN          NaN   \n",
       "1303                  0       1  37.0          C78   \n",
       "1304                  1       1  28.0          NaN   \n",
       "1305                  1       0   NaN          NaN   \n",
       "1306                  0       1  39.0         C105   \n",
       "1307                  1       0  38.5          NaN   \n",
       "1308                  1       0   NaN          NaN   \n",
       "1309                  0       0   NaN          NaN   \n",
       "\n",
       "                                                          Name          Title  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris             Mr   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...            Mrs   \n",
       "3                                       Heikkinen, Miss. Laina           Miss   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)            Mrs   \n",
       "5                                     Allen, Mr. William Henry             Mr   \n",
       "6                                             Moran, Mr. James             Mr   \n",
       "7                                      McCarthy, Mr. Timothy J             Mr   \n",
       "8                               Palsson, Master. Gosta Leonard         Master   \n",
       "9            Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)            Mrs   \n",
       "10                         Nasser, Mrs. Nicholas (Adele Achem)            Mrs   \n",
       "11                             Sandstrom, Miss. Marguerite Rut           Miss   \n",
       "12                                    Bonnell, Miss. Elizabeth           Miss   \n",
       "13                              Saundercock, Mr. William Henry             Mr   \n",
       "14                                 Andersson, Mr. Anders Johan             Mr   \n",
       "15                        Vestrom, Miss. Hulda Amanda Adolfina           Miss   \n",
       "16                            Hewlett, Mrs. (Mary D Kingcome)             Mrs   \n",
       "17                                        Rice, Master. Eugene         Master   \n",
       "18                                Williams, Mr. Charles Eugene             Mr   \n",
       "19           Vander Planke, Mrs. Julius (Emelia Maria Vande...            Mrs   \n",
       "20                                     Masselmani, Mrs. Fatima            Mrs   \n",
       "21                                        Fynney, Mr. Joseph J             Mr   \n",
       "22                                       Beesley, Mr. Lawrence             Mr   \n",
       "23                                 McGowan, Miss. Anna \"Annie\"           Miss   \n",
       "24                                Sloper, Mr. William Thompson             Mr   \n",
       "25                               Palsson, Miss. Torborg Danira           Miss   \n",
       "26           Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...            Mrs   \n",
       "27                                     Emir, Mr. Farred Chehab             Mr   \n",
       "28                              Fortune, Mr. Charles Alexander             Mr   \n",
       "29                               O'Dwyer, Miss. Ellen \"Nellie\"           Miss   \n",
       "30                                         Todoroff, Mr. Lalio             Mr   \n",
       "...                                                        ...            ...   \n",
       "1280                                      Canavan, Mr. Patrick             Mr   \n",
       "1281                               Palsson, Master. Paul Folke         Master   \n",
       "1282                                Payne, Mr. Vivian Ponsonby             Mr   \n",
       "1283            Lines, Mrs. Ernest H (Elizabeth Lindsey James)            Mrs   \n",
       "1284                             Abbott, Master. Eugene Joseph         Master   \n",
       "1285                                      Gilbert, Mr. William             Mr   \n",
       "1286                                  Kink-Heilmann, Mr. Anton             Mr   \n",
       "1287            Smith, Mrs. Lucien Philip (Mary Eloise Hughes)            Mrs   \n",
       "1288                                      Colbert, Mr. Patrick             Mr   \n",
       "1289         Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...            Mrs   \n",
       "1290                            Larsson-Rondberg, Mr. Edvard A             Mr   \n",
       "1291                                  Conlon, Mr. Thomas Henry             Mr   \n",
       "1292                                   Bonnell, Miss. Caroline           Miss   \n",
       "1293                                           Gale, Mr. Harry             Mr   \n",
       "1294                            Gibson, Miss. Dorothy Winifred           Miss   \n",
       "1295                                    Carrau, Mr. Jose Pedro             Mr   \n",
       "1296                              Frauenthal, Mr. Isaac Gerald             Mr   \n",
       "1297              Nourney, Mr. Alfred (Baron von Drachstedt\")\"             Mr   \n",
       "1298                                 Ware, Mr. William Jeffery             Mr   \n",
       "1299                                Widener, Mr. George Dunton             Mr   \n",
       "1300                           Riordan, Miss. Johanna Hannah\"\"           Miss   \n",
       "1301                                 Peacock, Miss. Treasteall           Miss   \n",
       "1302                                    Naughton, Miss. Hannah           Miss   \n",
       "1303           Minahan, Mrs. William Edward (Lillian E Thorpe)            Mrs   \n",
       "1304                            Henriksson, Miss. Jenny Lovisa           Miss   \n",
       "1305                                        Spector, Mr. Woolf             Mr   \n",
       "1306                              Oliva y Ocana, Dona. Fermina  Distinguished   \n",
       "1307                              Saether, Mr. Simon Sivertsen             Mr   \n",
       "1308                                       Ware, Mr. Frederick             Mr   \n",
       "1309                                  Peter, Master. Michael J         Master   \n",
       "\n",
       "             Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                        \n",
       "1                        0       0     0   1    0  \n",
       "2                        0       0     0   0    1  \n",
       "3                        0       0     1   0    0  \n",
       "4                        0       0     0   0    1  \n",
       "5                        0       0     0   1    0  \n",
       "6                        0       0     0   1    0  \n",
       "7                        0       0     0   1    0  \n",
       "8                        0       1     0   0    0  \n",
       "9                        0       0     0   0    1  \n",
       "10                       0       0     0   0    1  \n",
       "11                       0       0     1   0    0  \n",
       "12                       0       0     1   0    0  \n",
       "13                       0       0     0   1    0  \n",
       "14                       0       0     0   1    0  \n",
       "15                       0       0     1   0    0  \n",
       "16                       0       0     0   0    1  \n",
       "17                       0       1     0   0    0  \n",
       "18                       0       0     0   1    0  \n",
       "19                       0       0     0   0    1  \n",
       "20                       0       0     0   0    1  \n",
       "21                       0       0     0   1    0  \n",
       "22                       0       0     0   1    0  \n",
       "23                       0       0     1   0    0  \n",
       "24                       0       0     0   1    0  \n",
       "25                       0       0     1   0    0  \n",
       "26                       0       0     0   0    1  \n",
       "27                       0       0     0   1    0  \n",
       "28                       0       0     0   1    0  \n",
       "29                       0       0     1   0    0  \n",
       "30                       0       0     0   1    0  \n",
       "...                    ...     ...   ...  ..  ...  \n",
       "1280                     0       0     0   1    0  \n",
       "1281                     0       1     0   0    0  \n",
       "1282                     0       0     0   1    0  \n",
       "1283                     0       0     0   0    1  \n",
       "1284                     0       1     0   0    0  \n",
       "1285                     0       0     0   1    0  \n",
       "1286                     0       0     0   1    0  \n",
       "1287                     0       0     0   0    1  \n",
       "1288                     0       0     0   1    0  \n",
       "1289                     0       0     0   0    1  \n",
       "1290                     0       0     0   1    0  \n",
       "1291                     0       0     0   1    0  \n",
       "1292                     0       0     1   0    0  \n",
       "1293                     0       0     0   1    0  \n",
       "1294                     0       0     1   0    0  \n",
       "1295                     0       0     0   1    0  \n",
       "1296                     0       0     0   1    0  \n",
       "1297                     0       0     0   1    0  \n",
       "1298                     0       0     0   1    0  \n",
       "1299                     0       0     0   1    0  \n",
       "1300                     0       0     1   0    0  \n",
       "1301                     0       0     1   0    0  \n",
       "1302                     0       0     1   0    0  \n",
       "1303                     0       0     0   0    1  \n",
       "1304                     0       0     1   0    0  \n",
       "1305                     0       0     0   1    0  \n",
       "1306                     1       0     0   0    0  \n",
       "1307                     0       0     0   1    0  \n",
       "1308                     0       0     0   1    0  \n",
       "1309                     0       1     0   0    0  \n",
       "\n",
       "[1309 rows x 18 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets see if there is any difference in avergae Age for those titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109db5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20,15])\n",
    "plt.subplot(331)\n",
    "sns.barplot('Title', 'Age', data=train_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sure enough, we see a significant difference in avergae age based on title. Especially for Mrs vs Miss, and Mr vs Master. Distinguished titles are, as expected, associated with a higher Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* ### From our correlation map we can see that 'Age' is correlated to some degree with 'PClass' and 'SibSp'\n",
    "* ### We can observe a difference in 'mean' age by 'PClass'and 'Sibsp' by plotting the variables against each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BrwPHJNmU5BzgfOAlSb4PvLiZlqQ98iIrkvRoBXwpSQH/d1Wt3fXFJKuB1QAr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\nSZIkSR1hgydJkiRJHfH/AzvsTUlVDCW8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e43208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,5])\n",
    "plt.subplot(131)\n",
    "sns.barplot('Pclass', 'Age', data=train_test)\n",
    "plt.subplot(132)\n",
    "sns.barplot('SibSp', 'Age', data=train_test)\n",
    "plt.subplot(133)\n",
    "sns.barplot('Parch', 'Age', data=train_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With these apparent differences in age based on the above factors, we should be able to better predict missing age values. We can use other variables/features for that passenger to predict their likely age.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>6</th>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>146.5208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B78</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kraeff, Mr. Theodor</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rogers, Mr. William John</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>15.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lennon, Mr. Denis</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Driscoll, Miss. Bridget</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>21.6792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samaan, Mr. Youssef</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>35.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C52</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>27.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stewart, Mr. Albert A</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Staneff, Mr. Ivan</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moutal, Mr. Rahamin Haim</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>7.7875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McDermott, Miss. Brigdet Delia</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Slocovski, Mr. Selman Francis</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shorney, Mr. Charles Joseph</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Petroff, Mr. Pastcho (\"Pentcho\")</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moss, Mr. Albert Johan</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>24.1500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moran, Miss. Bertha</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moore, Mr. Leonard Charles</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McMahon, Mr. Martin</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F E69</td>\n",
       "      <td>Peter, Miss. Anna</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>15.2458</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>7.3125</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Howard, Miss. May Elizabeth</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fox, Mr. Patrick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>15.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lennon, Miss. Mary</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saade, Mr. Jean Nassr</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fleming, Miss. Honora</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1178</th>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Franklin, Mr. Charles (Charles Fardon)</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F E46</td>\n",
       "      <td>Mardirosian, Mr. Sarkis</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ford, Mr. Arthur</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>39.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rheims, Mr. George Alexander Lucien</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nasr, Mr. Mustafa</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>21.6792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samaan, Mr. Hanna</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>15.0458</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>Malachard, Mr. Noel</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McCarthy, Miss. Catherine Katie\"\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>7.5750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sadowitz, Mr. Harry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thomas, Mr. Tannous</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betros, Master. Seman</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sage, Mr. John George</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>van Billiard, Master. James William</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lockyer, Mr. Edward</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Keefe, Mr. Patrick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sage, Mrs. John (Annie Bullen)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>14.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Caram, Mr. Joseph</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Connor, Mr. Patrick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Risien, Mrs. Samuel (Emma)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>12.8750</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wheeler, Mr. Edwin Frederick\"\"</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>7.7208</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                     \n",
       "6              8.4583      0       3      0       0.0           0           1   \n",
       "18            13.0000      0       2      0       1.0           0           0   \n",
       "20             7.2250      0       3      0       1.0           1           0   \n",
       "27             7.2250      0       3      0       0.0           1           0   \n",
       "29             7.8792      0       3      0       1.0           0           1   \n",
       "30             7.8958      0       3      0       0.0           0           0   \n",
       "32           146.5208      0       1      1       1.0           1           0   \n",
       "33             7.7500      0       3      0       1.0           0           1   \n",
       "37             7.2292      0       3      0       1.0           1           0   \n",
       "43             7.8958      0       3      0       0.0           1           0   \n",
       "46             8.0500      0       3      0       0.0           0           0   \n",
       "47            15.5000      0       3      1       0.0           0           1   \n",
       "48             7.7500      0       3      0       1.0           0           1   \n",
       "49            21.6792      0       3      2       0.0           1           0   \n",
       "56            35.5000      0       1      0       1.0           0           0   \n",
       "65            27.7208      0       1      0       0.0           1           0   \n",
       "66            15.2458      1       3      1       1.0           1           0   \n",
       "77             7.8958      0       3      0       0.0           0           0   \n",
       "78             8.0500      0       3      0       0.0           0           0   \n",
       "83             7.7875      0       3      0       1.0           0           1   \n",
       "88             8.0500      0       3      0       0.0           0           0   \n",
       "96             8.0500      0       3      0       0.0           0           0   \n",
       "102            7.8958      0       3      0       0.0           0           0   \n",
       "108            7.7750      0       3      0       1.0           0           0   \n",
       "110           24.1500      0       3      1       1.0           0           1   \n",
       "122            8.0500      0       3      0       0.0           0           0   \n",
       "127            7.7500      0       3      0       0.0           0           1   \n",
       "129           22.3583      1       3      1       1.0           1           0   \n",
       "141           15.2458      2       3      0       0.0           1           0   \n",
       "155            7.3125      0       3      0       0.0           0           0   \n",
       "...               ...    ...     ...    ...       ...         ...         ...   \n",
       "1160           8.0500      0       3      0       NaN           0           0   \n",
       "1163           7.7500      0       3      0       NaN           0           1   \n",
       "1165          15.5000      0       3      1       NaN           0           1   \n",
       "1166           7.2250      0       3      0       NaN           1           0   \n",
       "1174           7.7500      0       3      0       NaN           0           1   \n",
       "1178           7.2500      0       3      0       NaN           0           0   \n",
       "1180           7.2292      0       3      0       NaN           1           0   \n",
       "1181           8.0500      0       3      0       NaN           0           0   \n",
       "1182          39.6000      0       1      0       NaN           0           0   \n",
       "1184           7.2292      0       3      0       NaN           1           0   \n",
       "1189          21.6792      0       3      2       NaN           1           0   \n",
       "1193          15.0458      0       2      0       NaN           1           0   \n",
       "1196           7.7500      0       3      0       NaN           0           1   \n",
       "1204           7.5750      0       3      0       NaN           0           0   \n",
       "1224           7.2250      0       3      0       NaN           1           0   \n",
       "1231           7.2292      0       3      0       NaN           1           0   \n",
       "1234          69.5500      9       3      1       NaN           0           0   \n",
       "1236          14.5000      1       3      1       NaN           0           0   \n",
       "1249           7.8792      0       3      0       NaN           0           0   \n",
       "1250           7.7500      0       3      0       NaN           0           1   \n",
       "1257          69.5500      9       3      1       NaN           0           0   \n",
       "1258          14.4583      0       3      1       NaN           1           0   \n",
       "1272           7.7500      0       3      0       NaN           0           1   \n",
       "1274          14.5000      0       3      0       NaN           0           0   \n",
       "1276          12.8750      0       2      0       NaN           0           0   \n",
       "1300           7.7208      0       3      0       NaN           0           1   \n",
       "1302           7.7500      0       3      0       NaN           0           1   \n",
       "1305           8.0500      0       3      0       NaN           0           0   \n",
       "1308           8.0500      0       3      0       NaN           0           0   \n",
       "1309          22.3583      1       3      1       NaN           1           0   \n",
       "\n",
       "             Embarked_S  Female  Age  Cabin  \\\n",
       "PassengerId                                   \n",
       "6                     0       0  NaN    NaN   \n",
       "18                    1       0  NaN    NaN   \n",
       "20                    0       1  NaN    NaN   \n",
       "27                    0       0  NaN    NaN   \n",
       "29                    0       1  NaN    NaN   \n",
       "30                    1       0  NaN    NaN   \n",
       "32                    0       1  NaN    B78   \n",
       "33                    0       1  NaN    NaN   \n",
       "37                    0       0  NaN    NaN   \n",
       "43                    0       0  NaN    NaN   \n",
       "46                    1       0  NaN    NaN   \n",
       "47                    0       0  NaN    NaN   \n",
       "48                    0       1  NaN    NaN   \n",
       "49                    0       0  NaN    NaN   \n",
       "56                    1       0  NaN    C52   \n",
       "65                    0       0  NaN    NaN   \n",
       "66                    0       0  NaN    NaN   \n",
       "77                    1       0  NaN    NaN   \n",
       "78                    1       0  NaN    NaN   \n",
       "83                    0       1  NaN    NaN   \n",
       "88                    1       0  NaN    NaN   \n",
       "96                    1       0  NaN    NaN   \n",
       "102                   1       0  NaN    NaN   \n",
       "108                   1       0  NaN    NaN   \n",
       "110                   0       1  NaN    NaN   \n",
       "122                   1       0  NaN    NaN   \n",
       "127                   0       0  NaN    NaN   \n",
       "129                   0       1  NaN  F E69   \n",
       "141                   0       1  NaN    NaN   \n",
       "155                   1       0  NaN    NaN   \n",
       "...                 ...     ...  ...    ...   \n",
       "1160                  1       1  NaN    NaN   \n",
       "1163                  0       0  NaN    NaN   \n",
       "1165                  0       1  NaN    NaN   \n",
       "1166                  0       0  NaN    NaN   \n",
       "1174                  0       1  NaN    NaN   \n",
       "1178                  1       0  NaN    NaN   \n",
       "1180                  0       0  NaN  F E46   \n",
       "1181                  1       0  NaN    NaN   \n",
       "1182                  1       0  NaN    NaN   \n",
       "1184                  0       0  NaN    NaN   \n",
       "1189                  0       0  NaN    NaN   \n",
       "1193                  0       0  NaN      D   \n",
       "1196                  0       1  NaN    NaN   \n",
       "1204                  1       0  NaN    NaN   \n",
       "1224                  0       0  NaN    NaN   \n",
       "1231                  0       0  NaN    NaN   \n",
       "1234                  1       0  NaN    NaN   \n",
       "1236                  1       0  NaN    NaN   \n",
       "1249                  1       0  NaN    NaN   \n",
       "1250                  0       0  NaN    NaN   \n",
       "1257                  1       1  NaN    NaN   \n",
       "1258                  0       0  NaN    NaN   \n",
       "1272                  0       0  NaN    NaN   \n",
       "1274                  1       1  NaN    NaN   \n",
       "1276                  1       0  NaN    NaN   \n",
       "1300                  0       1  NaN    NaN   \n",
       "1302                  0       1  NaN    NaN   \n",
       "1305                  1       0  NaN    NaN   \n",
       "1308                  1       0  NaN    NaN   \n",
       "1309                  0       0  NaN    NaN   \n",
       "\n",
       "                                                       Name   Title  \\\n",
       "PassengerId                                                           \n",
       "6                                          Moran, Mr. James      Mr   \n",
       "18                             Williams, Mr. Charles Eugene      Mr   \n",
       "20                                  Masselmani, Mrs. Fatima     Mrs   \n",
       "27                                  Emir, Mr. Farred Chehab      Mr   \n",
       "29                            O'Dwyer, Miss. Ellen \"Nellie\"    Miss   \n",
       "30                                      Todoroff, Mr. Lalio      Mr   \n",
       "32           Spencer, Mrs. William Augustus (Marie Eugenie)     Mrs   \n",
       "33                                 Glynn, Miss. Mary Agatha    Miss   \n",
       "37                                         Mamee, Mr. Hanna      Mr   \n",
       "43                                      Kraeff, Mr. Theodor      Mr   \n",
       "46                                 Rogers, Mr. William John      Mr   \n",
       "47                                        Lennon, Mr. Denis      Mr   \n",
       "48                                O'Driscoll, Miss. Bridget    Miss   \n",
       "49                                      Samaan, Mr. Youssef      Mr   \n",
       "56                                        Woolner, Mr. Hugh      Mr   \n",
       "65                                    Stewart, Mr. Albert A      Mr   \n",
       "66                                 Moubarek, Master. Gerios  Master   \n",
       "77                                        Staneff, Mr. Ivan      Mr   \n",
       "78                                 Moutal, Mr. Rahamin Haim      Mr   \n",
       "83                           McDermott, Miss. Brigdet Delia    Miss   \n",
       "88                            Slocovski, Mr. Selman Francis      Mr   \n",
       "96                              Shorney, Mr. Charles Joseph      Mr   \n",
       "102                        Petroff, Mr. Pastcho (\"Pentcho\")      Mr   \n",
       "108                                  Moss, Mr. Albert Johan      Mr   \n",
       "110                                     Moran, Miss. Bertha    Miss   \n",
       "122                              Moore, Mr. Leonard Charles      Mr   \n",
       "127                                     McMahon, Mr. Martin      Mr   \n",
       "129                                       Peter, Miss. Anna    Miss   \n",
       "141                           Boulos, Mrs. Joseph (Sultana)     Mrs   \n",
       "155                                   Olsen, Mr. Ole Martin      Mr   \n",
       "...                                                     ...     ...   \n",
       "1160                            Howard, Miss. May Elizabeth    Miss   \n",
       "1163                                       Fox, Mr. Patrick      Mr   \n",
       "1165                                     Lennon, Miss. Mary    Miss   \n",
       "1166                                  Saade, Mr. Jean Nassr      Mr   \n",
       "1174                                  Fleming, Miss. Honora    Miss   \n",
       "1178                 Franklin, Mr. Charles (Charles Fardon)      Mr   \n",
       "1180                                Mardirosian, Mr. Sarkis      Mr   \n",
       "1181                                       Ford, Mr. Arthur      Mr   \n",
       "1182                    Rheims, Mr. George Alexander Lucien      Mr   \n",
       "1184                                      Nasr, Mr. Mustafa      Mr   \n",
       "1189                                      Samaan, Mr. Hanna      Mr   \n",
       "1193                                    Malachard, Mr. Noel      Mr   \n",
       "1196                      McCarthy, Miss. Catherine Katie\"\"    Miss   \n",
       "1204                                    Sadowitz, Mr. Harry      Mr   \n",
       "1224                                    Thomas, Mr. Tannous      Mr   \n",
       "1231                                  Betros, Master. Seman  Master   \n",
       "1234                                  Sage, Mr. John George      Mr   \n",
       "1236                    van Billiard, Master. James William  Master   \n",
       "1249                                    Lockyer, Mr. Edward      Mr   \n",
       "1250                                   O'Keefe, Mr. Patrick      Mr   \n",
       "1257                         Sage, Mrs. John (Annie Bullen)     Mrs   \n",
       "1258                                      Caram, Mr. Joseph      Mr   \n",
       "1272                                  O'Connor, Mr. Patrick      Mr   \n",
       "1274                             Risien, Mrs. Samuel (Emma)     Mrs   \n",
       "1276                         Wheeler, Mr. Edwin Frederick\"\"      Mr   \n",
       "1300                        Riordan, Miss. Johanna Hannah\"\"    Miss   \n",
       "1302                                 Naughton, Miss. Hannah    Miss   \n",
       "1305                                     Spector, Mr. Woolf      Mr   \n",
       "1308                                    Ware, Mr. Frederick      Mr   \n",
       "1309                               Peter, Master. Michael J  Master   \n",
       "\n",
       "             Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                        \n",
       "6                        0       0     0   1    0  \n",
       "18                       0       0     0   1    0  \n",
       "20                       0       0     0   0    1  \n",
       "27                       0       0     0   1    0  \n",
       "29                       0       0     1   0    0  \n",
       "30                       0       0     0   1    0  \n",
       "32                       0       0     0   0    1  \n",
       "33                       0       0     1   0    0  \n",
       "37                       0       0     0   1    0  \n",
       "43                       0       0     0   1    0  \n",
       "46                       0       0     0   1    0  \n",
       "47                       0       0     0   1    0  \n",
       "48                       0       0     1   0    0  \n",
       "49                       0       0     0   1    0  \n",
       "56                       0       0     0   1    0  \n",
       "65                       0       0     0   1    0  \n",
       "66                       0       1     0   0    0  \n",
       "77                       0       0     0   1    0  \n",
       "78                       0       0     0   1    0  \n",
       "83                       0       0     1   0    0  \n",
       "88                       0       0     0   1    0  \n",
       "96                       0       0     0   1    0  \n",
       "102                      0       0     0   1    0  \n",
       "108                      0       0     0   1    0  \n",
       "110                      0       0     1   0    0  \n",
       "122                      0       0     0   1    0  \n",
       "127                      0       0     0   1    0  \n",
       "129                      0       0     1   0    0  \n",
       "141                      0       0     0   0    1  \n",
       "155                      0       0     0   1    0  \n",
       "...                    ...     ...   ...  ..  ...  \n",
       "1160                     0       0     1   0    0  \n",
       "1163                     0       0     0   1    0  \n",
       "1165                     0       0     1   0    0  \n",
       "1166                     0       0     0   1    0  \n",
       "1174                     0       0     1   0    0  \n",
       "1178                     0       0     0   1    0  \n",
       "1180                     0       0     0   1    0  \n",
       "1181                     0       0     0   1    0  \n",
       "1182                     0       0     0   1    0  \n",
       "1184                     0       0     0   1    0  \n",
       "1189                     0       0     0   1    0  \n",
       "1193                     0       0     0   1    0  \n",
       "1196                     0       0     1   0    0  \n",
       "1204                     0       0     0   1    0  \n",
       "1224                     0       0     0   1    0  \n",
       "1231                     0       1     0   0    0  \n",
       "1234                     0       0     0   1    0  \n",
       "1236                     0       1     0   0    0  \n",
       "1249                     0       0     0   1    0  \n",
       "1250                     0       0     0   1    0  \n",
       "1257                     0       0     0   0    1  \n",
       "1258                     0       0     0   1    0  \n",
       "1272                     0       0     0   1    0  \n",
       "1274                     0       0     0   0    1  \n",
       "1276                     0       0     0   1    0  \n",
       "1300                     0       0     1   0    0  \n",
       "1302                     0       0     1   0    0  \n",
       "1305                     0       0     0   1    0  \n",
       "1308                     0       0     0   1    0  \n",
       "1309                     0       1     0   0    0  \n",
       "\n",
       "[263 rows x 18 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing = train_test[train_test['Age'].isnull()]\n",
    "train_test = train_test[train_test['Age'].notnull()]\n",
    "age_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets temporarily split off the entries with missing age values from the larger data set so we can fill in those values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since 'Master' was so predictive of a younger age, set all missing Master ages to the mean for Master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>66</th>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>25.4667</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lefebre, Master. Henry Forbes</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>15.2458</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moubarek, Master. Halim Gonios (\"William George\")</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>23.4500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnston, Master. William Arthur Willie\"\"</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Betros, Master. Seman</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>van Billiard, Master. James William</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>22.3583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.482642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "66           15.2458      1       3      1       1.0           1           0   \n",
       "160          69.5500      2       3      8       0.0           0           0   \n",
       "177          25.4667      1       3      3       0.0           0           0   \n",
       "710          15.2458      1       3      1       1.0           1           0   \n",
       "1136         23.4500      2       3      1       NaN           0           0   \n",
       "1231          7.2292      0       3      0       NaN           1           0   \n",
       "1236         14.5000      1       3      1       NaN           0           0   \n",
       "1309         22.3583      1       3      1       NaN           1           0   \n",
       "\n",
       "             Embarked_S  Female       Age Cabin  \\\n",
       "PassengerId                                       \n",
       "66                    0       0  5.482642   NaN   \n",
       "160                   1       0  5.482642   NaN   \n",
       "177                   1       0  5.482642   NaN   \n",
       "710                   0       0  5.482642   NaN   \n",
       "1136                  1       0  5.482642   NaN   \n",
       "1231                  0       0  5.482642   NaN   \n",
       "1236                  1       0  5.482642   NaN   \n",
       "1309                  0       0  5.482642   NaN   \n",
       "\n",
       "                                                          Name   Title  \\\n",
       "PassengerId                                                              \n",
       "66                                    Moubarek, Master. Gerios  Master   \n",
       "160                                 Sage, Master. Thomas Henry  Master   \n",
       "177                              Lefebre, Master. Henry Forbes  Master   \n",
       "710          Moubarek, Master. Halim Gonios (\"William George\")  Master   \n",
       "1136                 Johnston, Master. William Arthur Willie\"\"  Master   \n",
       "1231                                     Betros, Master. Seman  Master   \n",
       "1236                       van Billiard, Master. James William  Master   \n",
       "1309                                  Peter, Master. Michael J  Master   \n",
       "\n",
       "             Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                        \n",
       "66                       0       1     0   0    0  \n",
       "160                      0       1     0   0    0  \n",
       "177                      0       1     0   0    0  \n",
       "710                      0       1     0   0    0  \n",
       "1136                     0       1     0   0    0  \n",
       "1231                     0       1     0   0    0  \n",
       "1236                     0       1     0   0    0  \n",
       "1309                     0       1     0   0    0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[age_missing['Master']== 1, \"Age\"]=train_test.loc[train_test['Master']==1, \"Age\"].mean()\n",
    "age_missing.loc[age_missing['Master']== 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets look at the distribution of Titles for missing ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Distinguished</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Master</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miss</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mr</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0    1\n",
       "0  Distinguished    1\n",
       "1         Master    8\n",
       "2           Miss   51\n",
       "3             Mr  176\n",
       "4            Mrs   27"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titles = pd.Series(np.unique(age_missing['Title'],return_counts=True)[0])\n",
    "counts = pd.Series(np.unique(age_missing['Title'],return_counts=True)[1])\n",
    "pd.concat([titles,counts], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets fill ages for Distinguished using the same method as we did for Master, since there are only a few missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>767</th>\n",
       "      <td>39.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.178571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brewe, Dr. Arthur Jackson</td>\n",
       "      <td>Distinguished</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                 \n",
       "767          39.6      0       1      0       0.0           1           0   \n",
       "\n",
       "             Embarked_S  Female        Age Cabin                       Name  \\\n",
       "PassengerId                                                                   \n",
       "767                   0       0  45.178571   NaN  Brewe, Dr. Arthur Jackson   \n",
       "\n",
       "                     Title  Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                                       \n",
       "767          Distinguished              1       0     0   0    0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[age_missing['Distinguished']== 1, \"Age\"]=train_test.loc[train_test['Distinguished']==1, \"Age\"].mean()\n",
    "age_missing.loc[age_missing['Distinguished']== 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Before we fill the rest of the missing ages using the same method, lets see if we can spot any meaningful patterns in the distrubtion of ages for each specific title vs the the other important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mr = train_test.loc[train_test['Mr']== 1]\n",
    "miss = train_test.loc[train_test['Miss']== 1]\n",
    "mrs = train_test.loc[train_test['Mrs']== 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y+CpwblX9Y8vRtA9JFgH/BDwf2EzvjnmnD9v//8Ne70aljozKv9MkbwDGgUdW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\nJKkTBjxJkiRJ6sQHCUr3GLYlbiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109a99fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[15,15])\n",
    "\n",
    "ax1 = fig.add_subplot(331)\n",
    "sns.barplot('Pclass', 'Age', data=miss)\n",
    "ax1.set_title(\"Miss\")\n",
    "\n",
    "ax2 = fig.add_subplot(332)\n",
    "sns.barplot('SibSp', 'Age', data=miss)\n",
    "ax2.set_title(\"Miss\")\n",
    "\n",
    "ax3 = fig.add_subplot(333)\n",
    "sns.barplot('Parch', 'Age', data=miss)\n",
    "ax3.set_title(\"Miss\")\n",
    "\n",
    "ax4 = fig.add_subplot(334)\n",
    "sns.barplot('Pclass', 'Age', data=mrs)\n",
    "ax4.set_title(\"Mrs\")\n",
    "\n",
    "ax5 = fig.add_subplot(335)\n",
    "sns.barplot('SibSp', 'Age', data=mrs)\n",
    "ax5.set_title(\"Mrs\")\n",
    "\n",
    "ax6 = fig.add_subplot(336)\n",
    "sns.barplot('Parch', 'Age', data=mrs)\n",
    "ax6.set_title(\"Mrs\")\n",
    "\n",
    "ax7 = fig.add_subplot(337)\n",
    "sns.barplot('Pclass', 'Age', data=mr)\n",
    "ax7.set_title(\"Mr\")\n",
    "\n",
    "ax8 = fig.add_subplot(338)\n",
    "sns.barplot('SibSp', 'Age', data=mr)\n",
    "ax8.set_title(\"Mr\")\n",
    "\n",
    "ax9 = fig.add_subplot(339)\n",
    "sns.barplot('Parch', 'Age', data=mr)\n",
    "ax9.set_title(\"Mr\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For 'Ms' there appears to be a strong pattern for the associated 'Parch' value. For 'Parch' = 0, Miss tended to be much older.\n",
    "* #### Reflecting on this intuitively, this makes sense. 'Parch' specifies the number of parents + children\n",
    "* #### In that era, it would be extremely unusual for a 'Miss' (unmarried) to have any kids, so we can reasonably assume that 'Parch' in the case of 'Miss', more simply represents the number of \"Parents\" there were traveling with\n",
    "* #### In this case, the pattern represents that a 'Miss' traveling with parents ('Parch'>0) tended to be younger than a 'Miss' that was traveling alone (Parch=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For 'Mrs' there is not much variation between associated features. As such, we can probably get away with just using a simple average of all 'Mrs' ages we do have\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For 'Mr' the strongest pattern appears to be with its associated 'SibSp' feature\n",
    "* #### 'SibSp' specifies the number of siblings + spouses\n",
    "* #### In that era (and now) it is customary for a Mr to have 0 or 1 spouses, so for 'SibSp'>1 we can assume that means they were travling with 1 or more siblings.\n",
    "* #### Whether a man was traveling with a sibling seems like a reasonably important feature and a point to split the distribution on"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set all 'Misses' with 'Parch'=0 to the average for that exact group in the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>29</th>\n",
       "      <td>7.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.152174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.152174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.152174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Driscoll, Miss. Bridget</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>7.7875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.152174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McDermott, Miss. Brigdet Delia</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>24.1500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.152174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moran, Miss. Bertha</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "29            7.8792      0       3      0       1.0           0           1   \n",
       "33            7.7500      0       3      0       1.0           0           1   \n",
       "48            7.7500      0       3      0       1.0           0           1   \n",
       "83            7.7875      0       3      0       1.0           0           1   \n",
       "110          24.1500      0       3      1       1.0           0           1   \n",
       "\n",
       "             Embarked_S  Female        Age Cabin  \\\n",
       "PassengerId                                        \n",
       "29                    0       1  27.152174   NaN   \n",
       "33                    0       1  27.152174   NaN   \n",
       "48                    0       1  27.152174   NaN   \n",
       "83                    0       1  27.152174   NaN   \n",
       "110                   0       1  27.152174   NaN   \n",
       "\n",
       "                                       Name Title  Distinguished  Master  \\\n",
       "PassengerId                                                                \n",
       "29            O'Dwyer, Miss. Ellen \"Nellie\"  Miss              0       0   \n",
       "33                 Glynn, Miss. Mary Agatha  Miss              0       0   \n",
       "48                O'Driscoll, Miss. Bridget  Miss              0       0   \n",
       "83           McDermott, Miss. Brigdet Delia  Miss              0       0   \n",
       "110                     Moran, Miss. Bertha  Miss              0       0   \n",
       "\n",
       "             Miss  Mr  Mrs  \n",
       "PassengerId                 \n",
       "29              1   0    0  \n",
       "33              1   0    0  \n",
       "48              1   0    0  \n",
       "83              1   0    0  \n",
       "110             1   0    0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[(age_missing['Miss']== 1) & (age_missing['Parch']== 0), \"Age\"]=train_test.loc[(train_test['Miss']== 1) & (train_test['Parch']== 0), \"Age\"].mean()\n",
    "age_missing.loc[(age_missing['Miss']== 1) & (age_missing['Parch']== 0)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set All 'Misses' with 'Parch'>0 to the average for that group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>129</th>\n",
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       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>2</td>\n",
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       "      <td>8</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Sage, Miss. Constance Gladys</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>25.4667</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.178816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lefebre, Miss. Mathilde</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>25.4667</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>12.178816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lefebre, Miss. Ida</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>25.4667</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>12.178816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lefebre, Miss. Jeannie</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "129          22.3583      1       3      1       1.0           1           0   \n",
       "181          69.5500      2       3      8       0.0           0           0   \n",
       "230          25.4667      1       3      3       0.0           0           0   \n",
       "410          25.4667      1       3      3       0.0           0           0   \n",
       "486          25.4667      1       3      3       0.0           0           0   \n",
       "\n",
       "             Embarked_S  Female        Age  Cabin  \\\n",
       "PassengerId                                         \n",
       "129                   0       1  12.178816  F E69   \n",
       "181                   1       1  12.178816    NaN   \n",
       "230                   1       1  12.178816    NaN   \n",
       "410                   1       1  12.178816    NaN   \n",
       "486                   1       1  12.178816    NaN   \n",
       "\n",
       "                                     Name Title  Distinguished  Master  Miss  \\\n",
       "PassengerId                                                                    \n",
       "129                     Peter, Miss. Anna  Miss              0       0     1   \n",
       "181          Sage, Miss. Constance Gladys  Miss              0       0     1   \n",
       "230               Lefebre, Miss. Mathilde  Miss              0       0     1   \n",
       "410                    Lefebre, Miss. Ida  Miss              0       0     1   \n",
       "486                Lefebre, Miss. Jeannie  Miss              0       0     1   \n",
       "\n",
       "             Mr  Mrs  \n",
       "PassengerId           \n",
       "129           0    0  \n",
       "181           0    0  \n",
       "230           0    0  \n",
       "410           0    0  \n",
       "486           0    0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[(age_missing['Miss']== 1) & (age_missing['Parch']>0), \"Age\"]=train_test.loc[(train_test['Miss']== 1) & (train_test['Parch']> 0), \"Age\"].mean()\n",
    "age_missing.loc[(age_missing['Miss']== 1) & (age_missing['Parch']>0)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set all 'Mrs' to avergae for 'Mrs' in training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.2250</td>\n",
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       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>146.5208</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>B78</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>Mrs</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>141</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36.994118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>55.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>36.994118</td>\n",
       "      <td>E33</td>\n",
       "      <td>Chibnall, Mrs. (Edith Martha Bowerman)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36.994118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>O'Brien, Mrs. Thomas (Johanna \"Hannah\" Godfrey)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                     \n",
       "20             7.2250      0       3      0       1.0           1           0   \n",
       "32           146.5208      0       1      1       1.0           1           0   \n",
       "141           15.2458      2       3      0       0.0           1           0   \n",
       "167           55.0000      1       1      0       1.0           0           0   \n",
       "187           15.5000      0       3      1       1.0           0           1   \n",
       "\n",
       "             Embarked_S  Female        Age Cabin  \\\n",
       "PassengerId                                        \n",
       "20                    0       1  36.994118   NaN   \n",
       "32                    0       1  36.994118   B78   \n",
       "141                   0       1  36.994118   NaN   \n",
       "167                   1       1  36.994118   E33   \n",
       "187                   0       1  36.994118   NaN   \n",
       "\n",
       "                                                        Name Title  \\\n",
       "PassengerId                                                          \n",
       "20                                   Masselmani, Mrs. Fatima   Mrs   \n",
       "32            Spencer, Mrs. William Augustus (Marie Eugenie)   Mrs   \n",
       "141                            Boulos, Mrs. Joseph (Sultana)   Mrs   \n",
       "167                   Chibnall, Mrs. (Edith Martha Bowerman)   Mrs   \n",
       "187          O'Brien, Mrs. Thomas (Johanna \"Hannah\" Godfrey)   Mrs   \n",
       "\n",
       "             Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                        \n",
       "20                       0       0     0   0    1  \n",
       "32                       0       0     0   0    1  \n",
       "141                      0       0     0   0    1  \n",
       "167                      0       0     0   0    1  \n",
       "187                      0       0     0   0    1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[(age_missing['Mrs']== 1) , \"Age\"]=train_test.loc[(train_test['Mrs']== 1), \"Age\"].mean()\n",
    "age_missing.loc[(age_missing['Mrs']== 1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set all 'Mr' with 'SibSp'>1 to its respective average, and 'SibSp' <2 to its average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>21.6792</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Samaan, Mr. Youssef</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sage, Mr. Frederick</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>23.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McCoy, Mr. Bernard</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>69.5500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "49           21.6792      0       3      2       0.0           1           0   \n",
       "202          69.5500      2       3      8       0.0           0           0   \n",
       "302          23.2500      0       3      2       1.0           0           1   \n",
       "325          69.5500      2       3      8       0.0           0           0   \n",
       "847          69.5500      2       3      8       0.0           0           0   \n",
       "\n",
       "             Embarked_S  Female        Age Cabin                      Name  \\\n",
       "PassengerId                                                                  \n",
       "49                    0       0  23.666667   NaN       Samaan, Mr. Youssef   \n",
       "202                   1       0  23.666667   NaN       Sage, Mr. Frederick   \n",
       "302                   0       0  23.666667   NaN        McCoy, Mr. Bernard   \n",
       "325                   1       0  23.666667   NaN  Sage, Mr. George John Jr   \n",
       "847                   1       0  23.666667   NaN  Sage, Mr. Douglas Bullen   \n",
       "\n",
       "            Title  Distinguished  Master  Miss  Mr  Mrs  \n",
       "PassengerId                                              \n",
       "49             Mr              0       0     0   1    0  \n",
       "202            Mr              0       0     0   1    0  \n",
       "302            Mr              0       0     0   1    0  \n",
       "325            Mr              0       0     0   1    0  \n",
       "847            Mr              0       0     0   1    0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[(age_missing['Mr']== 1) & (age_missing['SibSp']> 1), \"Age\"]=train_test.loc[(train_test['Mr']== 1) & (train_test['SibSp']> 1), \"Age\"].mean()\n",
    "age_missing.loc[(age_missing['Mr']== 1) & (age_missing['SibSp']>1)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Distinguished</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.4583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.574107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32.574107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.574107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>32.574107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7.2292</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.574107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fare  Parch  Pclass  SibSp  Survived  Embarked_C  Embarked_Q  \\\n",
       "PassengerId                                                                    \n",
       "6             8.4583      0       3      0       0.0           0           1   \n",
       "18           13.0000      0       2      0       1.0           0           0   \n",
       "27            7.2250      0       3      0       0.0           1           0   \n",
       "30            7.8958      0       3      0       0.0           0           0   \n",
       "37            7.2292      0       3      0       1.0           1           0   \n",
       "\n",
       "             Embarked_S  Female        Age Cabin  \\\n",
       "PassengerId                                        \n",
       "6                     0       0  32.574107   NaN   \n",
       "18                    1       0  32.574107   NaN   \n",
       "27                    0       0  32.574107   NaN   \n",
       "30                    1       0  32.574107   NaN   \n",
       "37                    0       0  32.574107   NaN   \n",
       "\n",
       "                                     Name Title  Distinguished  Master  Miss  \\\n",
       "PassengerId                                                                    \n",
       "6                        Moran, Mr. James    Mr              0       0     0   \n",
       "18           Williams, Mr. Charles Eugene    Mr              0       0     0   \n",
       "27                Emir, Mr. Farred Chehab    Mr              0       0     0   \n",
       "30                    Todoroff, Mr. Lalio    Mr              0       0     0   \n",
       "37                       Mamee, Mr. Hanna    Mr              0       0     0   \n",
       "\n",
       "             Mr  Mrs  \n",
       "PassengerId           \n",
       "6             1    0  \n",
       "18            1    0  \n",
       "27            1    0  \n",
       "30            1    0  \n",
       "37            1    0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_missing.loc[(age_missing['Mr']== 1) & (age_missing['SibSp']< 2), \"Age\"]=train_test.loc[(train_test['Mr']== 1) & (train_test['SibSp']< 2), \"Age\"].mean()\n",
    "age_missing.loc[(age_missing['Mr']== 1) & (age_missing['SibSp']<2)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets add these imputed 'Age' values back to our main data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test = pd.concat([train_test, age_missing])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_test.drop(['Name','Title','Distinguished','Master','Mr','Miss','Mrs','Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now lets test what impact adding 'Ages' in had on our accuracy\n",
    "----------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ytrain = train_test[train_test['Survived'].notnull()]\n",
    "ytrain = ytrain['Survived']\n",
    "xtrain = train_test[train_test['Survived'].notnull()].drop(['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=100)\n",
    "model.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xtest = train_test[train_test['Survived'].isnull()].drop(['Survived'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IDtest = pd.Series(xtest.index.values, name=\"PassengerId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testy = pd.Series(model.predict(xtest).astype(int), name=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = pd.concat([IDtest, testy], axis=1)\n",
    "results.to_csv(\"basic_age.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Uk0joR0ytq0HWuYOR7Vkl3jemL6N3IrYyku4H7qI9GLFxKgYBO9rerlRt0X0J\n/YiIiuREbkRERRL6EREVSehHRFQkoR8RUZH/D5txX7ssiyXdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10974aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = model.feature_importances_\n",
    "names=xtrain.columns.values\n",
    "pd.Series(importances, index=names).plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}