TITANIC DISASTER PROBLEM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TITANIC DISASTER PROBLEM FROM KAGGLE\n",
    "\n",
    "    A personal solution to this problem.\n",
    "    \n",
    "    PROJECT WORK FOR ALGORITHM DESIGN - CSE 3131"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Getting the training and test set\n",
    "train = pd.read_csv('titanic/train.csv')\n",
    "test = pd.read_csv('titanic/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <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": [
    "# Taking a look at the training set\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": [
    "# Getting some detailed info about the training set\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that the 'Age' column, 'Cabin' column and 'Embarked' column have some missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual Analysis of some feature columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kIzbYEaMkH05yf5Ivzxk7Ncmnknytf57S40nyviS7k9ye5IVD1SVptthrJE3SkKfSrgIu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\nSVIzGEmSJDWDkSRJUjMYSZIktf8LvklLuHzuqPwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c9599080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x = 'Survived', col = 'Sex', kind = 'count', data = train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly females have a higher likelihood of survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "8      1\n",
       "9      1\n",
       "10     1\n",
       "11     1\n",
       "14     0\n",
       "15     1\n",
       "18     0\n",
       "19     1\n",
       "22     1\n",
       "24     0\n",
       "25     1\n",
       "28     1\n",
       "31     1\n",
       "32     1\n",
       "38     0\n",
       "39     1\n",
       "40     0\n",
       "41     0\n",
       "43     1\n",
       "44     1\n",
       "47     1\n",
       "49     0\n",
       "52     1\n",
       "53     1\n",
       "56     1\n",
       "58     1\n",
       "61     1\n",
       "      ..\n",
       "807    0\n",
       "809    1\n",
       "813    0\n",
       "816    0\n",
       "820    1\n",
       "823    1\n",
       "829    1\n",
       "830    1\n",
       "835    1\n",
       "842    1\n",
       "849    1\n",
       "852    0\n",
       "853    1\n",
       "854    0\n",
       "855    1\n",
       "856    1\n",
       "858    1\n",
       "862    1\n",
       "863    0\n",
       "865    1\n",
       "866    1\n",
       "871    1\n",
       "874    1\n",
       "875    1\n",
       "879    1\n",
       "880    1\n",
       "882    0\n",
       "885    0\n",
       "887    1\n",
       "888    0\n",
       "Name: Survived, Length: 314, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the percentage of female survival\n",
    "train[train.Sex == 'female'].Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74\n"
     ]
    }
   ],
   "source": [
    "# Since length is 314, we have to divide the number of survived females by 314\n",
    "females_survived = train[train.Sex == 'female'].Survived.sum()/314\n",
    "print(round(females_survived * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "4      0\n",
       "5      0\n",
       "6      0\n",
       "7      0\n",
       "12     0\n",
       "13     0\n",
       "16     0\n",
       "17     1\n",
       "20     0\n",
       "21     1\n",
       "23     1\n",
       "26     0\n",
       "27     0\n",
       "29     0\n",
       "30     0\n",
       "33     0\n",
       "34     0\n",
       "35     0\n",
       "36     1\n",
       "37     0\n",
       "42     0\n",
       "45     0\n",
       "46     0\n",
       "48     0\n",
       "50     0\n",
       "51     0\n",
       "54     0\n",
       "55     1\n",
       "57     0\n",
       "      ..\n",
       "840    0\n",
       "841    0\n",
       "843    0\n",
       "844    0\n",
       "845    0\n",
       "846    0\n",
       "847    0\n",
       "848    0\n",
       "850    0\n",
       "851    0\n",
       "857    1\n",
       "859    0\n",
       "860    0\n",
       "861    0\n",
       "864    0\n",
       "867    0\n",
       "868    0\n",
       "869    1\n",
       "870    0\n",
       "872    0\n",
       "873    0\n",
       "876    0\n",
       "877    0\n",
       "878    0\n",
       "881    0\n",
       "883    0\n",
       "884    0\n",
       "886    0\n",
       "889    1\n",
       "890    0\n",
       "Name: Survived, Length: 577, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the percentage of males survived\n",
    "train[train.Sex == 'male'].Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "# Since length is 577, we have to divide the number of survived females by 577\n",
    "males_survived = train[train.Sex == 'male'].Survived.sum()\n",
    "print(round(males_survived/577 * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Approximately 74% females and 19% males survived. Male survival percentage is much lower than females!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c16c9c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using seaborn to build bar plots of the feature 'Survived' over 'Pclass'\n",
    "# Gives us an idea of survival rates of 'Pclass' feature column\n",
    "\n",
    "sns.factorplot(x = 'Survived', col = 'Pclass', kind = 'count', data = train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the passengers on the first class were the most likely to survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VjZkmqqoeYD6ENgBzwGVJXrGfzbZU1T91y5cDp3fLLwXet8D8y4Cf75bP6J7j\nKOCHgfcl2Qb8KfPvbAE8D7ikW37PAb0gSYuWeSNpSGaO+rasdQGaPlW1C7gauDrJ9cDZwMM88kbA\nkXts8vWRbf8hyZeTPJv5YHrVAk+xBfiPSY5hPiA/Cnw78JWqWrO3sg7y5UhaxMwbSUMyc9Qnj5hp\nopI8I8nqkaE1wN8DtzEfMAA/t5/dXAr8OvDEqrp+zwe7d6yuYf7w/YeqaldVfRX4UpLTuzqS5Pu7\nTf6a+XedAF524K9K0mJk3kgakpmjvtmYadKOAjYnuSnJdcCJwBuANwJvT/JXwK797OP9zIfM5fuY\ncxnw8u5+t5cB5yT5PHAjsL4bfw1wbpLPAE88sJcjaREzbyQNycxRr7xcviRJkiQ15hEzSZIkSWrM\nxkySJEmSGrMxkyRJkqTGbMwkSZIkqTEbM0mSJElqzMZMkiRJkhqzMZMkSZKkxv4f+SLahZLO0aAA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c16c97f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using seaborn to build bar plots of the feature 'Survived' over 'Embarked'\n",
    "# Gives us an idea of survival rates of 'Pclass' feature column\n",
    "sns.factorplot(x = 'Survived', col = 'Embarked', kind = 'count', data = train);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Image size of 71424x288 pixels is too large. It must be less than 2^16 in each direction.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-1975ee694f57>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Using seaborn to build bar plots of the feature 'Survived' over 'Fare'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactorplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Survived'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Fare'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'count'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mfactorplot\u001b[0;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, size, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   3506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3507\u001b[0m     \u001b[0;31m# Initialize the facets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3508\u001b[0;31m     \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mfacet_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3510\u001b[0m     \u001b[0;31m# Draw the plot onto the facets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, row, col, hue, col_wrap, sharex, sharey, size, aspect, palette, row_order, col_order, hue_order, hue_kws, dropna, legend_out, despine, margin_titles, xlim, ylim, subplot_kws, gridspec_kws)\u001b[0m\n\u001b[1;32m    372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[0;31m# Make the axes look good\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m         \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    375\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdespine\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdespine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mtight_layout\u001b[0;34m(self, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   2001\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2002\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2003\u001b[0;31m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2005\u001b[0m         kwargs = get_tight_layout_figure(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/tight_layout.py\u001b[0m in \u001b[0;36mget_renderer\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcanvas\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"get_renderer\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 219\u001b[0;31m             \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    220\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0;31m# not sure if this can happen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[0;34m(self, cleared)\u001b[0m\n\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mneed_new_renderer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    446\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Image size of 71424x288 pixels is too large. It must be less than 2^16 in each direction."
     ]
    }
   ],
   "source": [
    "# Using seaborn to build bar plots of the feature 'Survived' over 'Fare'\n",
    "sns.factorplot(x = 'Survived', col = 'Fare', kind = 'count', data = train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that the image size is too large. We therefore have to use another method to visualize the 'Fare' feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XJOk0luSIvqpeS/J+4M8ZnF55X1UdXIrnkiSd3pKdR19VjwCPLNXjDxl7+meFOZfGey6N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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20be086748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a histogram of 'Fare' column from the training set\n",
    "sns.distplot(train.Fare, kde = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20bdfd5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting 'Fare' and 'Survived' together to get an idea of how survival is related to the fare value.\n",
    "train.groupby('Survived').Fare.hist(alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Those who paid more, had higher chances of survival!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing missing values and creating my Machine Learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A final check on the 'Age' feature column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously 'Age' feature column has an impact on the survival. But, it has got some missing values. I can fill those with either the mean of the median of the same column. But before doing that, I need to check the distribution of the 'Age' feature column in order to decide which one would be suitable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eZ3dJykfccvZ4QgL9+c07upfgzfoMBBHxBx4BlgFTgVUi0v04w01AtTEmE3gI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\nCoD/D3aL7sNE0GLvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20bde92128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using seaborn to plot a histogram of 'Age' column and check the distribution of the values.\n",
    "\n",
    "sns.distplot(train.dropna().Age)    # Missing values have to be dropped else error will pop out.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the 'Age' feature column is clearly right skewed. So mean won't be an appropriate choice. Hence, I shall fill the missing values with the median of the column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1046 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 122.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Storing the response data for later use\n",
    "train_survived = train.Survived\n",
    "\n",
    "# Concatenating the training and the test sets to impute the missing values and work on a combined data\n",
    "train_test_data = pd.concat([train.drop(['Survived'], axis = 1), test])\n",
    "\n",
    "# Checking the info of the new concatenated data\n",
    "train_test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 122.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Filling the 'Age' and 'Fare' feature columns with the median values\n",
    "\n",
    "train_test_data['Age'] = train_test_data.Age.fillna(train_test_data.Age.median())\n",
    "train_test_data['Fare'] = train_test_data.Fare.fillna(train_test_data.Fare.median())\n",
    "\n",
    "# Checking the info again to see if the missing values have been filled up.\n",
    "train_test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values of 'Age' and 'Fare' columns have now been filled up. Now we just have to take care of the 'Cabin' and 'Embarked' feature columns.\n",
    "\n",
    "Since, the 'Cabin' feature column has got only 295 filled in columns, I am gonna ignore that column for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Embarked       1307 non-null object\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 112.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Removing the 'Cabin' feature column\n",
    "train_test_data.drop(['Cabin'], axis = 1, inplace = True)\n",
    "\n",
    "train_test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1309 non-null float64\n",
      "Embarked       1309 non-null object\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 112.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Filling the 'Embarked' feature column with the most frequent value\n",
    "\n",
    "train_test_data = train_test_data.fillna(train_test_data['Embarked'].value_counts().index[0])\n",
    "\n",
    "train_test_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ALL THE THINGS HAVE BEEN TAKEN CARE OF NOW!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>29.503186</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.281086</td>\n",
       "      <td>0.644003</td>\n",
       "      <td>0.093965</td>\n",
       "      <td>0.699771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>12.905241</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.741500</td>\n",
       "      <td>0.478997</td>\n",
       "      <td>0.291891</td>\n",
       "      <td>0.458533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>655.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId       Pclass          Age        SibSp        Parch  \\\n",
       "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean    655.000000     2.294882    29.503186     0.498854     0.385027   \n",
       "std     378.020061     0.837836    12.905241     1.041658     0.865560   \n",
       "min       1.000000     1.000000     0.170000     0.000000     0.000000   \n",
       "25%     328.000000     2.000000    22.000000     0.000000     0.000000   \n",
       "50%     655.000000     3.000000    28.000000     0.000000     0.000000   \n",
       "75%     982.000000     3.000000    35.000000     1.000000     0.000000   \n",
       "max    1309.000000     3.000000    80.000000     8.000000     9.000000   \n",
       "\n",
       "              Fare     Sex_male   Embarked_Q   Embarked_S  \n",
       "count  1309.000000  1309.000000  1309.000000  1309.000000  \n",
       "mean     33.281086     0.644003     0.093965     0.699771  \n",
       "std      51.741500     0.478997     0.291891     0.458533  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       7.895800     0.000000     0.000000     0.000000  \n",
       "50%      14.454200     1.000000     0.000000     1.000000  \n",
       "75%      31.275000     1.000000     0.000000     1.000000  \n",
       "max     512.329200     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we encode the data by making dummy variables of the required feature columns as Categorical values might not be \n",
    "# accepted by the machine learning model\n",
    "\n",
    "# 'Embarked' and 'Sex' are the two columns am focusing on.\n",
    "\n",
    "train_test_data = pd.get_dummies(train_test_data, columns = ['Sex'], drop_first=True)\n",
    "train_test_data = pd.get_dummies(train_test_data, columns = ['Embarked'], drop_first=True)\n",
    "\n",
    "# Dropping one column from each of these as we might get caught in the 'DUMMY VARIABLE TRAP'. So to avoid that,\n",
    "# we have to take n - 1 columns.\n",
    "\n",
    "train_test_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the 'Sex' feature column and the 'Embarked' feature column have been divided into 2 and 3 separate dummy variable column each and have been sliced off of one column each to avoid the DUMMY VARIABLE TRAP.\n",
    "\n",
    "Using get_dummies() which is a pandas function, the 'Sex' column was divided into 'Sex_Male' and 'Sex_Female' and the later was discarded. Similarly, 'Embarked'column was divided into 'Embarked_Q', 'Embarked_S' and 'Embarked_C' and 'Embarked_C' was sliced off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  Sex_male  Embarked_Q  Embarked_S\n",
       "0       3  22.0      1      0   7.2500         1           0           1\n",
       "1       1  38.0      1      0  71.2833         0           0           0\n",
       "2       3  26.0      0      0   7.9250         0           0           1\n",
       "3       1  35.0      1      0  53.1000         0           0           1\n",
       "4       3  35.0      0      0   8.0500         1           0           1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, I am selecting the columns on which I want to build my Machine Learning model.\n",
    "# Since am a beginner, I thought PassengerId might not have much effect on the prediction, althought i might be wrong.\n",
    "\n",
    "train_test_data = train_test_data[['Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_male', 'Embarked_Q', 'Embarked_S']]\n",
    "\n",
    "# Checking if all is good!\n",
    "train_test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All good!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "Pclass        1309 non-null int64\n",
      "Age           1309 non-null float64\n",
      "SibSp         1309 non-null int64\n",
      "Parch         1309 non-null int64\n",
      "Fare          1309 non-null float64\n",
      "Sex_male      1309 non-null uint8\n",
      "Embarked_Q    1309 non-null uint8\n",
      "Embarked_S    1309 non-null uint8\n",
      "dtypes: float64(2), int64(3), uint8(3)\n",
      "memory usage: 65.2 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.294882</td>\n",
       "      <td>29.503186</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.281086</td>\n",
       "      <td>0.644003</td>\n",
       "      <td>0.093965</td>\n",
       "      <td>0.699771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.837836</td>\n",
       "      <td>12.905241</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.741500</td>\n",
       "      <td>0.478997</td>\n",
       "      <td>0.291891</td>\n",
       "      <td>0.458533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Pclass          Age        SibSp        Parch         Fare  \\\n",
       "count  1309.000000  1309.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean      2.294882    29.503186     0.498854     0.385027    33.281086   \n",
       "std       0.837836    12.905241     1.041658     0.865560    51.741500   \n",
       "min       1.000000     0.170000     0.000000     0.000000     0.000000   \n",
       "25%       2.000000    22.000000     0.000000     0.000000     7.895800   \n",
       "50%       3.000000    28.000000     0.000000     0.000000    14.454200   \n",
       "75%       3.000000    35.000000     1.000000     0.000000    31.275000   \n",
       "max       3.000000    80.000000     8.000000     9.000000   512.329200   \n",
       "\n",
       "          Sex_male   Embarked_Q   Embarked_S  \n",
       "count  1309.000000  1309.000000  1309.000000  \n",
       "mean      0.644003     0.093965     0.699771  \n",
       "std       0.478997     0.291891     0.458533  \n",
       "min       0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000  \n",
       "50%       1.000000     0.000000     1.000000  \n",
       "75%       1.000000     0.000000     1.000000  \n",
       "max       1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final check on the info and getting the description\n",
    "\n",
    "train_test_data.info()\n",
    "train_test_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the 'Support Vector Machine Classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "        1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "        0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "        0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "        1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1,\n",
       "        0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1,\n",
       "        0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
       "        1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "        0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0,\n",
       "        1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0]),\n",
       " array([[  3. ,  34.5,   0. , ...,   1. ,   1. ,   0. ],\n",
       "        [  3. ,  47. ,   1. , ...,   0. ,   0. ,   1. ],\n",
       "        [  2. ,  62. ,   0. , ...,   1. ,   1. ,   0. ],\n",
       "        ..., \n",
       "        [  3. ,  38.5,   0. , ...,   1. ,   0. ,   1. ],\n",
       "        [  3. ,  28. ,   0. , ...,   1. ,   0. ,   1. ],\n",
       "        [  3. ,  28. ,   1. , ...,   1. ,   0. ,   0. ]]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset back into the training and test sets and then using scikit learn library to convert them into arrays\n",
    "X = train_test_data.iloc[:891].values\n",
    "y = train_survived.values\n",
    "X_test = train_test_data.iloc[891:].values\n",
    "\n",
    "X, y, X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the classifier and fitting it to the training set\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the test set results and then formatting the test set to hold only the required two columns.\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "test['Survived'] = y_pred\n",
    "test = test[['PassengerId', 'Survived']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Converting the result to a csv file.\n",
    "test.to_csv('titanic_submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final comments\n",
    "\n",
    "This project's generated csv file was submitted to kaggle.com and it generated a score of 0.76555 which is equavelent to nearly 76.55%."
   ]
  }
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