Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a view into the dataset itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<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>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",
       "    <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Kink-Heilmann, Miss. Luise Gretchen</td>\n",
       "      <td>female</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>315153</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Renouf, Mrs. Peter Henry (Lillian Jefferys)</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31027</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Clarke, Mrs. Charles V (Ada Maria Winfield)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Weir, Col. John</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113800</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Wick, Miss. Mary Natalie</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>36928</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>C7</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Holm, Mr. John Fredrik Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C 7075</td>\n",
       "      <td>6.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Klasen, Mr. Klas Albin</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>350404</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>250655</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass                                            Name  \\\n",
       "PassengerId                                                                     \n",
       "185                 1       3             Kink-Heilmann, Miss. Luise Gretchen   \n",
       "727                 1       2     Renouf, Mrs. Peter Henry (Lillian Jefferys)   \n",
       "427                 1       2     Clarke, Mrs. Charles V (Ada Maria Winfield)   \n",
       "695                 0       1                                 Weir, Col. John   \n",
       "884                 0       2                   Banfield, Mr. Frederick James   \n",
       "319                 1       1                        Wick, Miss. Mary Natalie   \n",
       "819                 0       3                Holm, Mr. John Fredrik Alexander   \n",
       "176                 0       3                          Klasen, Mr. Klas Albin   \n",
       "706                 0       2  Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")   \n",
       "160                 0       3                      Sage, Master. Thomas Henry   \n",
       "\n",
       "                Sex   Age  SibSp  Parch            Ticket      Fare Cabin  \\\n",
       "PassengerId                                                                 \n",
       "185          female   4.0      0      2            315153   22.0250   NaN   \n",
       "727          female  30.0      3      0             31027   21.0000   NaN   \n",
       "427          female  28.0      1      0              2003   26.0000   NaN   \n",
       "695            male  60.0      0      0            113800   26.5500   NaN   \n",
       "884            male  28.0      0      0  C.A./SOTON 34068   10.5000   NaN   \n",
       "319          female  31.0      0      2             36928  164.8667    C7   \n",
       "819            male  43.0      0      0            C 7075    6.4500   NaN   \n",
       "176            male  18.0      1      1            350404    7.8542   NaN   \n",
       "706            male  39.0      0      0            250655   26.0000   NaN   \n",
       "160            male   NaN      8      2          CA. 2343   69.5500   NaN   \n",
       "\n",
       "            Embarked  \n",
       "PassengerId           \n",
       "185                S  \n",
       "727                S  \n",
       "427                S  \n",
       "695                S  \n",
       "884                S  \n",
       "319                S  \n",
       "819                S  \n",
       "176                S  \n",
       "706                S  \n",
       "160                S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = pd.read_csv(\"datasets/titanic_train.csv\", index_col='PassengerId')\n",
    "data_validate = pd.read_csv(\"datasets/titanic_test.csv\", index_col='PassengerId')\n",
    "data_raw.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\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(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Douglas, Mr. Walter Donald</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived      Pclass                        Name   Sex         Age  \\\n",
       "count   891.000000  891.000000                         891   891  714.000000   \n",
       "unique         NaN         NaN                         891     2         NaN   \n",
       "top            NaN         NaN  Douglas, Mr. Walter Donald  male         NaN   \n",
       "freq           NaN         NaN                           1   577         NaN   \n",
       "mean      0.383838    2.308642                         NaN   NaN   29.699118   \n",
       "std       0.486592    0.836071                         NaN   NaN   14.526497   \n",
       "min       0.000000    1.000000                         NaN   NaN    0.420000   \n",
       "25%       0.000000    2.000000                         NaN   NaN   20.125000   \n",
       "50%       0.000000    3.000000                         NaN   NaN   28.000000   \n",
       "75%       1.000000    3.000000                         NaN   NaN   38.000000   \n",
       "max       1.000000    3.000000                         NaN   NaN   80.000000   \n",
       "\n",
       "             SibSp       Parch    Ticket        Fare    Cabin Embarked  \n",
       "count   891.000000  891.000000       891  891.000000      204      889  \n",
       "unique         NaN         NaN       681         NaN      147        3  \n",
       "top            NaN         NaN  CA. 2343         NaN  B96 B98        S  \n",
       "freq           NaN         NaN         7         NaN        4      644  \n",
       "mean      0.523008    0.381594       NaN   32.204208      NaN      NaN  \n",
       "std       1.102743    0.806057       NaN   49.693429      NaN      NaN  \n",
       "min       0.000000    0.000000       NaN    0.000000      NaN      NaN  \n",
       "25%       0.000000    0.000000       NaN    7.910400      NaN      NaN  \n",
       "50%       0.000000    0.000000       NaN   14.454200      NaN      NaN  \n",
       "75%       1.000000    0.000000       NaN   31.000000      NaN      NaN  \n",
       "max       8.000000    6.000000       NaN  512.329200      NaN      NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning and Wrangling the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll make a copy of the raw data and put it in a list along with the validation set. We can later separate it into training and testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_copy = data_raw.copy(deep=True)\n",
    "data_cleaner = [data_copy, data_validate]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are 891 entries in the dataset and 12 columns including the PassengerId as the index.\n",
    "\n",
    "Of the 891 entries for Cabin 687 entries in total are null. This means that there isn't much we can do with the information about the cabin.\n",
    "\n",
    "In addition, both the Ticket and Fare columns are more or less random. Furthermore, PassengerId is only a unique identifier and will not affect our model.\n",
    "\n",
    "While it is possible to separate the Name into titles alone, I believe it is not needed.\n",
    "\n",
    "So all of them are dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We note that there 177 entries for Age do not exist. Instead of deleting these entries completely, we shall instead fill these age columns with the median age. We choose median over mean because there are both babies(Age is a fraction less than one) and very old people as well which might skew the value of mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the case of the port of Embarkation, we see that only 2 values are null. We will use the mode of this column to fill in these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for dataset in data_cleaner:\n",
    "    dataset['Age'].fillna(dataset['Age'].median(), inplace = True)\n",
    "    dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace = True)\n",
    "    dataset.drop(['Cabin', 'Ticket', 'Fare', 'Name'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now SibSp and Parch are described as follows:\n",
    "\n",
    "    sibsp: The dataset defines family relations in this way...\n",
    "    Sibling = brother, sister, stepbrother, stepsister\n",
    "    Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "    parch: The dataset defines family relations in this way...\n",
    "    Parent = mother, father\n",
    "    Child = daughter, son, stepdaughter, stepson\n",
    "    Some children travelled only with a nanny, therefore parch=0 for them.\n",
    "    \n",
    "We can instead create a new feature 'FamilySize' by adding SibSp and Parch and 1(For the person themself). We will also create another feature 'IsAlone' for the people who travelled alone. And then we may remove the SibSp and Parch columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akash\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:179: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "for dataset in data_cleaner:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "    # We set IsAlone to 1/True for everyone and then change it to 0/False depending on their FamilySize.\n",
    "    dataset['IsAlone'] = 1\n",
    "    dataset['IsAlone'].loc[dataset['FamilySize'] > 1] = 0\n",
    "    dataset.drop(['SibSp', 'Parch'], axis=1, inplace = True)"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass     Sex   Age Embarked  FamilySize  IsAlone\n",
       "PassengerId                                                              \n",
       "1                   0       3    male  22.0        S           2        0\n",
       "2                   1       1  female  38.0        C           2        0\n",
       "3                   1       3  female  26.0        S           1        1\n",
       "4                   1       1  female  35.0        S           2        0\n",
       "5                   0       3    male  35.0        S           1        1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaner[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know from our preliminary analysis that the Sex is either male or female. We also know the age and port of embarkation for all passengers. Let us set male = 0 and female = 1. Also, we can set the port such that C = 0, Q = 1, S = 2. We shall leave the age as it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akash\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:179: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "for dataset in data_cleaner:\n",
    "    dataset['Sex'].loc[dataset['Sex'] == 'male'] = 0\n",
    "    dataset['Sex'].loc[dataset['Sex'] == 'female'] = 1\n",
    "    dataset['Embarked'].loc[dataset['Embarked'] == 'C'] = 0\n",
    "    dataset['Embarked'].loc[dataset['Embarked'] == 'Q'] = 1\n",
    "    dataset['Embarked'].loc[dataset['Embarked'] == 'S'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass Sex   Age Embarked  FamilySize  IsAlone\n",
       "PassengerId                                                          \n",
       "1                   0       3   0  22.0        2           2        0\n",
       "2                   1       1   1  38.0        0           2        0\n",
       "3                   1       3   1  26.0        2           1        1\n",
       "4                   1       1   1  35.0        2           2        0\n",
       "5                   0       3   0  35.0        2           1        1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cleaner[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Splitting up the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can now split the data into the labels and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_clean, data_validate = data_cleaner\n",
    "data_labels = data_clean['Survived']\n",
    "data_features = data_clean.drop('Survived', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting up the labels and features into training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_train, features_test, labels_train, labels_test = train_test_split(data_features, data_labels,\n",
    "                                                                            test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a look at our testing, training and validating data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass Sex   Age Embarked  FamilySize  IsAlone\n",
       "PassengerId                                                \n",
       "332               1   0  45.5        2           1        1\n",
       "734               2   0  23.0        2           1        1\n",
       "383               3   0  32.0        2           1        1\n",
       "705               3   0  26.0        2           2        0\n",
       "814               3   1   6.0        2           7        0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "332    0\n",
       "734    0\n",
       "383    0\n",
       "705    0\n",
       "814    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>IsAlone</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass Sex   Age Embarked  FamilySize  IsAlone\n",
       "PassengerId                                                \n",
       "710               3   0  28.0        0           3        0\n",
       "440               2   0  31.0        2           1        1\n",
       "841               3   0  20.0        2           1        1\n",
       "721               2   1   6.0        2           2        0\n",
       "40                3   1  14.0        0           2        0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "710    1\n",
       "440    0\n",
       "841    0\n",
       "721    1\n",
       "40     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass Sex   Age Embarked  FamilySize  IsAlone\n",
       "PassengerId                                                \n",
       "892               3   0  34.5        1           1        1\n",
       "893               3   1  47.0        2           2        0\n",
       "894               2   0  62.0        1           1        1\n",
       "895               3   0  27.0        2           1        1\n",
       "896               3   1  22.0        2           3        0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_validate.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_classifier = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time: 0.011408567428588867s.\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "nb_classifier.fit(features_train, labels_train)\n",
    "print(\"Training Time: \", time()-t0, \"s.\", sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Time: 0.0057599544525146484s.\n"
     ]
    }
   ],
   "source": [
    "t1 = time()\n",
    "nb_pred = nb_classifier.predict(features_test)\n",
    "print(\"Testing Time: \", time()-t1, \"s.\", sep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.787709497207.\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \", accuracy_score(labels_test, nb_pred), \".\", sep='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~79% accuracy on our testing data. Now we just predict for the given data and save it to a file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt_classifier = tree.DecisionTreeClassifier(min_samples_split=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time:  0 s\n"
     ]
    }
   ],
   "source": [
    "t0 = time()\n",
    "dt_classifier.fit(features_train, labels_train)\n",
    "print(\"Training Time: \", round(time() - t0), \"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Time:  0 s\n"
     ]
    }
   ],
   "source": [
    "t1 = time()\n",
    "dt_prediction = dt_classifier.predict(features_test)\n",
    "print(\"Prediction Time: \", round(time() - t1), \"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.826815642458\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(labels_test, dt_prediction))"
   ]
  },
  {
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>721</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Pclass Sex   Age Embarked  FamilySize  IsAlone\n",
       "PassengerId                                                \n",
       "710               3   0  28.0        0           3        0\n",
       "440               2   0  31.0        2           1        1\n",
       "841               3   0  20.0        2           1        1\n",
       "721               2   1   6.0        2           2        0\n",
       "40                3   1  14.0        0           2        0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_classifier.predict(features_test.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "710    1\n",
       "440    0\n",
       "841    0\n",
       "721    1\n",
       "40     1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the algorithms on the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final = dt_classifier.predict(data_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample = pd.read_csv(\"datasets/titanic_sample.csv\", index_col='PassengerId')\n",
    "sample['Survived'] = final\n",
    "sample.to_csv(\"datasets/titanic_output.csv\", )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Score: 0.75119"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"result.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's about it. I know I didn't do any of the fancy graphs and explore the data further, but seeing as I started learning about 2 days ago, I'll take it. I might update this in the future but most likely it'll remain the same. If I do revisit this, I will probably go more indepth and try out different models."
   ]
  }
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