Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sbs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vkpISxcTEhHRHAACIVBb/N18Q/o5F8jO8Tz75WKWr35UjYVC4R8F5crsOqOLXmUpO7voM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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16117dd1f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['Survived'].value_counts().plot(kind='bar', title='Death and Survival Counts')\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHdq+fbtiY2M1e/ZsjRgxorvnBwAgKoQM8ttvvy2Xy6WtW7dq//79ev755+U4joqLi5WT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\nMgAABhBkAAAMIMgAABhAkAEAMOD/AR6+aJcWSXh8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16117f83da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['Pclass'].value_counts().plot(kind='bar', title='Passenger Class Counts')\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1611844a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['Sex'].value_counts().plot(kind='bar', title='Sex Counts')\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nOn6Ul156SdXV1br//vuDy44dO6bo6Gi5XNw3x4mrr6/XLbfconXr1mnMmDGS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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16118052860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['Embarked'].value_counts().plot(kind='bar', title='Embarkation Counts')\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x161180e34e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yySfldDrV0NCgiooKJScnKy8vT/PmzYt2vJD5fD7NmjVLhw4dktfr1YQJE3TZ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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16117da8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['Age'].hist()\n",
    "plt.title('Age Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S        1           2.0   \n",
       "1      0          PC 17599  71.2833   C85        C        0           0.0   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S        0           2.0   \n",
       "3      0            113803  53.1000  C123        S        0           2.0   \n",
       "4      0            373450   8.0500   NaN        S        1           2.0   \n",
       "\n",
       "   FamilySize  \n",
       "0           1  \n",
       "1           1  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genders_mapping = dict([('female', 0), ('male', 1)])\n",
    "df_train['Sex_Val'] = df_train['Sex'].map(genders_mapping).astype(int)\n",
    "embarked_locs_mapping = dict([('C', 0),('Q', 1), ('S', 2), ('NaN', 3)])\n",
    "df_train['Embarked_Val'] = df_train['Embarked'].map(embarked_locs_mapping)\n",
    "df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch']\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train = df_train.drop(['PassengerId', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked', 'SibSp', 'Parch'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train['Embarked_Val'] = df_train['Embarked_Val'].fillna(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train['Age'] = df_train['Age'].fillna(df_train['Age'].mean()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age     Fare  Sex_Val  Embarked_Val  FamilySize\n",
       "0         0       3  22.0   7.2500        1           2.0           1\n",
       "1         1       1  38.0  71.2833        0           0.0           1\n",
       "2         1       3  26.0   7.9250        0           2.0           0\n",
       "3         1       1  35.0  53.1000        0           2.0           1\n",
       "4         0       3  35.0   8.0500        1           2.0           0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.        ,   3.        ,  22.        , ...,   1.        ,\n",
       "          2.        ,   1.        ],\n",
       "       [  1.        ,   1.        ,  38.        , ...,   0.        ,\n",
       "          0.        ,   1.        ],\n",
       "       [  1.        ,   3.        ,  26.        , ...,   0.        ,\n",
       "          2.        ,   0.        ],\n",
       "       ..., \n",
       "       [  0.        ,   3.        ,  29.69911765, ...,   0.        ,\n",
       "          2.        ,   3.        ],\n",
       "       [  1.        ,   1.        ,  26.        , ...,   1.        ,\n",
       "          0.        ,   0.        ],\n",
       "       [  0.        ,   3.        ,  32.        , ...,   1.        ,\n",
       "          1.        ,   0.        ]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = df_train.values\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_features = train_data[:,1:]\n",
    "train_target = train_data[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mean accuracy of Random Forest: 0.9820426487093153'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = clf.fit(train_features, train_target)\n",
    "score = clf.score(train_features, train_target)\n",
    "\"Mean accuracy of Random Forest: {0}\".format(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predict_target = clf.predict(train_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98204264870931535"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(predict_target, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test.csv')\n",
    "df_test.info()\n",
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilieff, Mr. Ylio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>903</td>\n",
       "      <td>1</td>\n",
       "      <td>Jones, Mr. Charles Cresson</td>\n",
       "      <td>male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>694</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>905</td>\n",
       "      <td>2</td>\n",
       "      <td>Howard, Mr. Benjamin</td>\n",
       "      <td>male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24065</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>907</td>\n",
       "      <td>2</td>\n",
       "      <td>del Carlo, Mrs. Sebastiano (Argenia Genovesi)</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2167</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>908</td>\n",
       "      <td>2</td>\n",
       "      <td>Keane, Mr. Daniel</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233734</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>909</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf, Mr. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2692</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>910</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilmakangas, Miss. Ida Livija</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101270</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf Khalil, Mrs. Mariana (Miriam\")\"</td>\n",
       "      <td>female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2696</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>912</td>\n",
       "      <td>1</td>\n",
       "      <td>Rothschild, Mr. Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17603</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>913</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Master. Artur Karl</td>\n",
       "      <td>male</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>C 17368</td>\n",
       "      <td>3.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>914</td>\n",
       "      <td>1</td>\n",
       "      <td>Flegenheim, Mrs. Alfred (Antoinette)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17598</td>\n",
       "      <td>31.6833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>915</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Richard Norris II</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Mrs. Arthur Larned (Emily Maria Borie)</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>917</td>\n",
       "      <td>3</td>\n",
       "      <td>Robins, Mr. Alexander A</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 3337</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Miss. Helene Ragnhild</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B36</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>919</td>\n",
       "      <td>3</td>\n",
       "      <td>Daher, Mr. Shedid</td>\n",
       "      <td>male</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2698</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>920</td>\n",
       "      <td>1</td>\n",
       "      <td>Brady, Mr. John Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113054</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>A21</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>921</td>\n",
       "      <td>3</td>\n",
       "      <td>Samaan, Mr. Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>1280</td>\n",
       "      <td>3</td>\n",
       "      <td>Canavan, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364858</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>1281</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "      <td>male</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1282</td>\n",
       "      <td>1</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12749</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>B24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>1283</td>\n",
       "      <td>1</td>\n",
       "      <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
       "      <td>female</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17592</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>D28</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>1284</td>\n",
       "      <td>3</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>1285</td>\n",
       "      <td>2</td>\n",
       "      <td>Gilbert, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 30769</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1286</td>\n",
       "      <td>3</td>\n",
       "      <td>Kink-Heilmann, Mr. Anton</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>315153</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>1287</td>\n",
       "      <td>1</td>\n",
       "      <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13695</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>C31</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1288</td>\n",
       "      <td>3</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>371109</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13567</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1290</td>\n",
       "      <td>3</td>\n",
       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347065</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1291</td>\n",
       "      <td>3</td>\n",
       "      <td>Conlon, Mr. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21332</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1292</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Caroline</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36928</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>C7</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1293</td>\n",
       "      <td>2</td>\n",
       "      <td>Gale, Mr. Harry</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28664</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1294</td>\n",
       "      <td>1</td>\n",
       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112378</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1295</td>\n",
       "      <td>1</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "      <td>male</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113059</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
       "      <td>1</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17765</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>D40</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>1297</td>\n",
       "      <td>2</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2166</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>D38</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1298</td>\n",
       "      <td>2</td>\n",
       "      <td>Ware, Mr. William Jeffery</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28666</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1299</td>\n",
       "      <td>1</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113503</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>334915</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1301</td>\n",
       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                               Name  \\\n",
       "0            892       3                                   Kelly, Mr. James   \n",
       "1            893       3                   Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                          Myles, Mr. Thomas Francis   \n",
       "3            895       3                                   Wirz, Mr. Albert   \n",
       "4            896       3       Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "5            897       3                         Svensson, Mr. Johan Cervin   \n",
       "6            898       3                               Connolly, Miss. Kate   \n",
       "7            899       2                       Caldwell, Mr. Albert Francis   \n",
       "8            900       3          Abrahim, Mrs. Joseph (Sophie Halaut Easu)   \n",
       "9            901       3                            Davies, Mr. John Samuel   \n",
       "10           902       3                                   Ilieff, Mr. Ylio   \n",
       "11           903       1                         Jones, Mr. Charles Cresson   \n",
       "12           904       1      Snyder, Mrs. John Pillsbury (Nelle Stevenson)   \n",
       "13           905       2                               Howard, Mr. Benjamin   \n",
       "14           906       1  Chaffee, Mrs. Herbert Fuller (Carrie Constance...   \n",
       "15           907       2      del Carlo, Mrs. Sebastiano (Argenia Genovesi)   \n",
       "16           908       2                                  Keane, Mr. Daniel   \n",
       "17           909       3                                  Assaf, Mr. Gerios   \n",
       "18           910       3                       Ilmakangas, Miss. Ida Livija   \n",
       "19           911       3              Assaf Khalil, Mrs. Mariana (Miriam\")\"   \n",
       "20           912       1                             Rothschild, Mr. Martin   \n",
       "21           913       3                          Olsen, Master. Artur Karl   \n",
       "22           914       1               Flegenheim, Mrs. Alfred (Antoinette)   \n",
       "23           915       1                    Williams, Mr. Richard Norris II   \n",
       "24           916       1    Ryerson, Mrs. Arthur Larned (Emily Maria Borie)   \n",
       "25           917       3                            Robins, Mr. Alexander A   \n",
       "26           918       1                       Ostby, Miss. Helene Ragnhild   \n",
       "27           919       3                                  Daher, Mr. Shedid   \n",
       "28           920       1                            Brady, Mr. John Bertram   \n",
       "29           921       3                                  Samaan, Mr. Elias   \n",
       "..           ...     ...                                                ...   \n",
       "388         1280       3                               Canavan, Mr. Patrick   \n",
       "389         1281       3                        Palsson, Master. Paul Folke   \n",
       "390         1282       1                         Payne, Mr. Vivian Ponsonby   \n",
       "391         1283       1     Lines, Mrs. Ernest H (Elizabeth Lindsey James)   \n",
       "392         1284       3                      Abbott, Master. Eugene Joseph   \n",
       "393         1285       2                               Gilbert, Mr. William   \n",
       "394         1286       3                           Kink-Heilmann, Mr. Anton   \n",
       "395         1287       1     Smith, Mrs. Lucien Philip (Mary Eloise Hughes)   \n",
       "396         1288       3                               Colbert, Mr. Patrick   \n",
       "397         1289       1  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...   \n",
       "398         1290       3                     Larsson-Rondberg, Mr. Edvard A   \n",
       "399         1291       3                           Conlon, Mr. Thomas Henry   \n",
       "400         1292       1                            Bonnell, Miss. Caroline   \n",
       "401         1293       2                                    Gale, Mr. Harry   \n",
       "402         1294       1                     Gibson, Miss. Dorothy Winifred   \n",
       "403         1295       1                             Carrau, Mr. Jose Pedro   \n",
       "404         1296       1                       Frauenthal, Mr. Isaac Gerald   \n",
       "405         1297       2       Nourney, Mr. Alfred (Baron von Drachstedt\")\"   \n",
       "406         1298       2                          Ware, Mr. William Jeffery   \n",
       "407         1299       1                         Widener, Mr. George Dunton   \n",
       "408         1300       3                    Riordan, Miss. Johanna Hannah\"\"   \n",
       "409         1301       3                          Peacock, Miss. Treasteall   \n",
       "410         1302       3                             Naughton, Miss. Hannah   \n",
       "411         1303       1    Minahan, Mrs. William Edward (Lillian E Thorpe)   \n",
       "412         1304       3                     Henriksson, Miss. Jenny Lovisa   \n",
       "413         1305       3                                 Spector, Mr. Woolf   \n",
       "414         1306       1                       Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                       Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                                Ware, Mr. Frederick   \n",
       "417         1309       3                           Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare  \\\n",
       "0      male  34.5      0      0              330911    7.8292   \n",
       "1    female  47.0      1      0              363272    7.0000   \n",
       "2      male  62.0      0      0              240276    9.6875   \n",
       "3      male  27.0      0      0              315154    8.6625   \n",
       "4    female  22.0      1      1             3101298   12.2875   \n",
       "5      male  14.0      0      0                7538    9.2250   \n",
       "6    female  30.0      0      0              330972    7.6292   \n",
       "7      male  26.0      1      1              248738   29.0000   \n",
       "8    female  18.0      0      0                2657    7.2292   \n",
       "9      male  21.0      2      0           A/4 48871   24.1500   \n",
       "10     male   NaN      0      0              349220    7.8958   \n",
       "11     male  46.0      0      0                 694   26.0000   \n",
       "12   female  23.0      1      0               21228   82.2667   \n",
       "13     male  63.0      1      0               24065   26.0000   \n",
       "14   female  47.0      1      0         W.E.P. 5734   61.1750   \n",
       "15   female  24.0      1      0       SC/PARIS 2167   27.7208   \n",
       "16     male  35.0      0      0              233734   12.3500   \n",
       "17     male  21.0      0      0                2692    7.2250   \n",
       "18   female  27.0      1      0    STON/O2. 3101270    7.9250   \n",
       "19   female  45.0      0      0                2696    7.2250   \n",
       "20     male  55.0      1      0            PC 17603   59.4000   \n",
       "21     male   9.0      0      1             C 17368    3.1708   \n",
       "22   female   NaN      0      0            PC 17598   31.6833   \n",
       "23     male  21.0      0      1            PC 17597   61.3792   \n",
       "24   female  48.0      1      3            PC 17608  262.3750   \n",
       "25     male  50.0      1      0           A/5. 3337   14.5000   \n",
       "26   female  22.0      0      1              113509   61.9792   \n",
       "27     male  22.5      0      0                2698    7.2250   \n",
       "28     male  41.0      0      0              113054   30.5000   \n",
       "29     male   NaN      2      0                2662   21.6792   \n",
       "..      ...   ...    ...    ...                 ...       ...   \n",
       "388    male  21.0      0      0              364858    7.7500   \n",
       "389    male   6.0      3      1              349909   21.0750   \n",
       "390    male  23.0      0      0               12749   93.5000   \n",
       "391  female  51.0      0      1            PC 17592   39.4000   \n",
       "392    male  13.0      0      2           C.A. 2673   20.2500   \n",
       "393    male  47.0      0      0          C.A. 30769   10.5000   \n",
       "394    male  29.0      3      1              315153   22.0250   \n",
       "395  female  18.0      1      0               13695   60.0000   \n",
       "396    male  24.0      0      0              371109    7.2500   \n",
       "397  female  48.0      1      1               13567   79.2000   \n",
       "398    male  22.0      0      0              347065    7.7750   \n",
       "399    male  31.0      0      0               21332    7.7333   \n",
       "400  female  30.0      0      0               36928  164.8667   \n",
       "401    male  38.0      1      0               28664   21.0000   \n",
       "402  female  22.0      0      1              112378   59.4000   \n",
       "403    male  17.0      0      0              113059   47.1000   \n",
       "404    male  43.0      1      0               17765   27.7208   \n",
       "405    male  20.0      0      0       SC/PARIS 2166   13.8625   \n",
       "406    male  23.0      1      0               28666   10.5000   \n",
       "407    male  50.0      1      1              113503  211.5000   \n",
       "408  female   NaN      0      0              334915    7.7208   \n",
       "409  female   3.0      1      1  SOTON/O.Q. 3101315   13.7750   \n",
       "410  female   NaN      0      0              365237    7.7500   \n",
       "411  female  37.0      1      0               19928   90.0000   \n",
       "412  female  28.0      0      0              347086    7.7750   \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   \n",
       "414  female  39.0      0      0            PC 17758  108.9000   \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   \n",
       "416    male   NaN      0      0              359309    8.0500   \n",
       "417    male   NaN      1      1                2668   22.3583   \n",
       "\n",
       "               Cabin Embarked  \n",
       "0                NaN        Q  \n",
       "1                NaN        S  \n",
       "2                NaN        Q  \n",
       "3                NaN        S  \n",
       "4                NaN        S  \n",
       "5                NaN        S  \n",
       "6                NaN        Q  \n",
       "7                NaN        S  \n",
       "8                NaN        C  \n",
       "9                NaN        S  \n",
       "10               NaN        S  \n",
       "11               NaN        S  \n",
       "12               B45        S  \n",
       "13               NaN        S  \n",
       "14               E31        S  \n",
       "15               NaN        C  \n",
       "16               NaN        Q  \n",
       "17               NaN        C  \n",
       "18               NaN        S  \n",
       "19               NaN        C  \n",
       "20               NaN        C  \n",
       "21               NaN        S  \n",
       "22               NaN        S  \n",
       "23               NaN        C  \n",
       "24   B57 B59 B63 B66        C  \n",
       "25               NaN        S  \n",
       "26               B36        C  \n",
       "27               NaN        C  \n",
       "28               A21        S  \n",
       "29               NaN        C  \n",
       "..               ...      ...  \n",
       "388              NaN        Q  \n",
       "389              NaN        S  \n",
       "390              B24        S  \n",
       "391              D28        S  \n",
       "392              NaN        S  \n",
       "393              NaN        S  \n",
       "394              NaN        S  \n",
       "395              C31        S  \n",
       "396              NaN        Q  \n",
       "397              B41        C  \n",
       "398              NaN        S  \n",
       "399              NaN        Q  \n",
       "400               C7        S  \n",
       "401              NaN        S  \n",
       "402              NaN        C  \n",
       "403              NaN        S  \n",
       "404              D40        C  \n",
       "405              D38        C  \n",
       "406              NaN        S  \n",
       "407              C80        C  \n",
       "408              NaN        Q  \n",
       "409              NaN        S  \n",
       "410              NaN        Q  \n",
       "411              C78        Q  \n",
       "412              NaN        S  \n",
       "413              NaN        S  \n",
       "414             C105        C  \n",
       "415              NaN        S  \n",
       "416              NaN        S  \n",
       "417              NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_test['Sex_Val'] = df_test['Sex'].map(genders_mapping)\n",
    "df_test['Embarked_Val'] = df_test['Embarked'].map(embarked_locs_mapping)\n",
    "df_test['Family_Size'] = df_test['SibSp'] + df_test['Parch']\n",
    "df_test = df_test.drop(['Name','Sex','SibSp','Parch','Ticket','Cabin','Embarked'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "      <th>Family_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age     Fare  Sex_Val  Embarked_Val  Family_Size\n",
       "0          892       3  34.5   7.8292        1             1            0\n",
       "1          893       3  47.0   7.0000        0             2            1\n",
       "2          894       2  62.0   9.6875        1             1            0\n",
       "3          895       3  27.0   8.6625        1             2            0\n",
       "4          896       3  22.0  12.2875        0             2            2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['Age'] = df_test['Age'].fillna(df_test['Age'].mean())\n",
    "df_test['Fare'] = df_test['Fare'].fillna(df_test['Fare'].mean())\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predict_test = clf.predict(df_test.values[:,1:]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_test['Survived'] = predict_test\n",
    "df_test[['PassengerId', 'Survived']].to_csv('results.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 6) (891,)\n",
      "(712, 6) (712,)\n",
      "(179, 6) (179,)\n"
     ]
    }
   ],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(train_features, train_target, test_size=0.20, random_state=0)\n",
    "print (train_features.shape, train_target.shape)\n",
    "print (train_x.shape, train_y.shape)\n",
    "print (test_x.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.83\n"
     ]
    }
   ],
   "source": [
    "clf = clf.fit(train_x, train_y)\n",
    "predict_y = clf.predict(test_x)\n",
    "print (\"Accuracy = %.2f\" % (accuracy_score(test_y, predict_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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