Titanic Dataset Kaggle Competition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic Dataset Kaggle Competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question/Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
    "\n",
    "One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
    "\n",
    "In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The necessary packages needed to build the analysis is imported into the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# Importing visualization packages\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Importing machine learning packages\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acquire training and testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training and test data are contained in the root folder of the notebook and imported into the notebook as Pandas dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('titanic/train.csv')\n",
    "df_test = pd.read_csv('titanic/test.csv')\n",
    "combine = [df_train, df_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <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",
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       "   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  "
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df_train.head()"
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  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df_test.head()"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
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       "      <th>std</th>\n",
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       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
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       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>top</th>\n",
       "      <td>Salkjelsvik, Miss. Anna Kristine</td>\n",
       "      <td>male</td>\n",
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      "text/plain": [
       "                                    Name   Sex  Ticket    Cabin Embarked\n",
       "count                                891   891     891      204      889\n",
       "unique                               891     2     681      147        3\n",
       "top     Salkjelsvik, Miss. Anna Kristine  male  347082  B96 B98        S\n",
       "freq                                   1   577       7        4      644"
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   ],
   "source": [
    "df_train.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze, identify patterns, and explore the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables available to us have the following descriptors which explain to us what they mean.\n",
    "\n",
    "- Survived: Survived (1) or died (0)\n",
    "- Pclass: Passenger's class\n",
    "- Name: Passenger's name\n",
    "- Sex: Passenger's sex\n",
    "- Age: Passenger's age\n",
    "- SibSp: Number of siblings/spouses aboard\n",
    "- Parch: Number of parents/children aboard\n",
    "- Ticket: Ticket number\n",
    "- Fare: Fare\n",
    "- Cabin: Cabin\n",
    "- Embarked: Port of embarkation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expected survival for different features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Effect of different features on survivablitiy is first tested between different columns and the Survived column which indicates the survival of the passenger. The factors likely to be involved in the survival of the passenger:\n",
    "- Passenger Class (Pclass)\n",
    "- Sex of the passenger (Sex)\n",
    "- Family members on the ship (SibSp and Parch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[['Parch', 'Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing data by visualizing it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For us to understand who would have survived this disaster better, we should visualize the patients who survived based on age, passenger class and etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of people who survived distributed over age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a1fdc7080>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Np7ODRyRJWgoLmySpV5wEWSO3PxPD2k0pPcI7bA/GFpskqVcsbJKkXrErUhPLbhlJc7HF\nJknqlalosY1q8IIkdYm9GA1bbJKkXrGwSZJ6ZSq6IqVH8/q40bDrvtumpavSFpskqVcsbJKkXrGw\nSZJ6xcImSeoVB4+otxzIIE0nW2ySpF4ZS4styenARcCBwLurytvaS0tgLmmSrNTlBiveYktyIPAO\n4EeBZwHnJnnWSschTTpzSZrbOLoiXwDcXlV3VNU3gUuAM8YQhzTpzCVpDuPoijwG+PKs5V3A9z96\noyQbgY3t4gNJbp3n874DuGdZI1xeXY8Puh/jisaXC4feZZD4PlJVpy8poPktmktD5BH472B/dT0+\nWCTGJfzbX1a5cNFjOFAejaOwZY519ZgVVZuBzYt+WLK9qmaWI7BR6Hp80P0YjW/+r55j3bfl0qB5\nBB7n/dX1+KD7MS5XfOPoitwFHDdr+VjgzjHEIU06c0mawzgK2w3ACUmOT3IwcA6wdQxxSJPOXJLm\nsOJdkVW1N8kvAh+lGaL8nqrasR8fOVA3yxh1PT7ofozGNwdzqXO6Hh90P8ZliS9Vjzm9JUnSxHLm\nEUlSr1jYJEm9MtGFLcnpSW5NcnuSTR2I57gk1yS5JcmOJOe361cnuSrJbe3zEWOO88Akn0qyrV0+\nPsl1bXyXtgMRxhnf4UmuSPL59li+sEvHMMmvtv99P5vk75I8vmvHcBjm0X7F2tlcmuY8mtjC1tHp\nhPYCb6yqZwInA69vY9oEXF1VJwBXt8vjdD5wy6zlC4G3t/HdC2wYS1SPuIjmQsxnAM+libUTxzDJ\nMcAvAzNV9WyaQRvn0L1jOBDzaL91OZemN4+qaiIfwAuBj85afjPw5nHH9agYrwReDtwKrG3XrQVu\nHWNMx9L8gz4V2EZzke89wKq5jusY4nsS8EXagU2z1nfiGPLIbB+raUYVbwN+pEvHcMi/xzxaelyd\nzaVpz6OJbbEx93RCx4wplsdIsg54HnAdcHRV3QXQPh81vsj4E+BNwMPt8pHAfVW1t10e93F8GrAH\n+Ku2i+fdSQ6hI8ewqv4L+EPgS8BdwNeAG+nWMRyGebR0Xc6lqc6jSS5sA03NNQ5JDgXeD/xKVd0/\n7nj2SfIqYHdV3Th79RybjvM4rgKeD7yrqp4HPEg3upwAaM9JnAEcDzwFOISmG+/ROvFvcQBd++//\nLV3NI5iIXJrqPJrkwtbJ6YSSHESTjH9TVR9oV9+dZG37/lpg95jCOwV4dZKdNDPBn0rzq/PwJPsu\n1h/3cdwF7Kqq69rlK2gStCvH8GXAF6tqT1X9H/AB4EV06xgOwzxamq7n0lTn0SQXts5NJ5QkwMXA\nLVX1x7Pe2gqsb1+vpzlnsOKq6s1VdWxVraM5Xh+vqvOAa4Czxx0fQFV9Bfhykqe3q04DPkdHjiFN\n18nJSZ7Y/vfeF19njuGQzKMl6HouTX0ejePE4TKegHwF8AXgP4Df7EA8P0DTdL4Z+HT7eAVN3/vV\nwG3t8+oOxPpDwLb29dOA64HbgcuBx405thOB7e1x/BBwRJeOIfBW4PPAZ4G/Bh7XtWM45N9jHu1f\nvJ3MpWnOI6fUkiT1yiR3RUqS9BgWNklSr1jYJEm9YmGTJPWKhU2S1CsWtimR5MeTVJJnjDsWaVKZ\nR5PBwjY9zgX+leZiUklLYx5NAAvbFGjn3DuF5hYQ57TrDkjyzvZ+SNuSfDjJ2e17JyX5RJIbk3x0\n3xQ80jQzjyaHhW06nElzX6YvAF9N8nzgLGAd8Bzg52luEbFvjr4/A86uqpOA9wC/N46gpY4xjybE\nqsU3UQ+cSzNBKzQTtp4LHARcXlUPA19Jck37/tOBZwNXNVO4cSDNbSWkaWceTQgLW88lOZJm5vFn\nJymaBCvgg/PtAuyoqheuUIhS55lHk8WuyP47G3hfVX1nVa2rquNo7qx7D/AT7TmCo2kmcoXmDrtr\nknyrSyXJ94wjcKlDzKMJYmHrv3N57K/K99Pc3G8Xzczaf0lzh+KvVdU3aZL4wiQ30cys/qKVC1fq\nJPNogji7/xRLcmhVPdB2s1wPnFLNfZwkDcg86h7PsU23bUkOBw4G3mYySktiHnWMLTZJUq94jk2S\n1CsWNklSr1jYJEm9YmGTJPWKhU2S1Cv/D+jVtVwxLPEWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f797c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_hist = sns.FacetGrid(df_train, col='Survived')\n",
    "age_hist.map(plt.hist, 'Age', bins=20)\n",
    "age_hist.set_ylabels('Number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can tell that amongst those who survived (Survived = 1), the number of infants (Age: 0-4) is higher than passengers below the age of 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of people who survived over age and passenger class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a211e6c50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GzHSHKl7a8/oe4Dfb1+uBJ/YlIkmSNLK2WjhU1esHFYgkSRp9Xa+q2Bv4b8Be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\nmYWDJEnqzMJBkiR1ZuEgSZI6+/8BccO3h6VVh4YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a211fac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_age_grid = sns.FacetGrid(df_train, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "pclass_age_grid.map(plt.hist, 'Age', alpha=0.5, bins=20)\n",
    "pclass_age_grid.add_legend()\n",
    "pclass_age_grid.set_ylabels('Number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The casualties of passenger who were in Passenger Class 3 are significantly more than the casualties in other passenger classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Survival rate of passengers amongst each sex for each embarking zone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rate of survival of passengers in each embarking zone distributed over sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2145aeb8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7zj3p6Lt6R2R1iQRqZ3X1ikiTo/BsYrp1Tq/2tSWo2dVbWlbBqvzd1Vqn+4qq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2124bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Embarked', size=2.8, aspect=2)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The survivors of female passengers was more amongst those passengers who embarked on S and Q but not in C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare Analysis of passengers who survived based on Embarking Zone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of the fare paid by passengers who survived or did not, based on their embarking zone and sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a21877668>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTHIccCfwmhZjkDRc5rU0JlorAKrqUmB9JwYPbGu7ktpjXkvjwzsBSpLUQRYAkiR1kAWA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20bd8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df_train, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Diff of proportions is significant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrangle, prepare, cleanse the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping irrelevant columns in data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see through describing the data that the 'Ticket' column and the 'Cabin' column are alphanumeric, have duplicates and do not have much information about the survival rates of the passenger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = df_train.drop(['Ticket', 'Cabin'], axis=1)\n",
    "df_test = df_test.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [df_train, df_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting a feature from given column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is done to extract the title of the passenger from the name column so we have an idea who the passenger will be in society"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(df_train['Title'], df_train['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By combining different titles that mean the same and splitting the title of each person by their role and status\n",
    "- Mlle means 'Mademoiselle' which is the honorific used to describe someone who would be called 'Miss' in France\n",
    "- Mme means 'Madame' which is the honorific used to describe someone who would be called 'Mrs' in France\n",
    "- Ms is the short form for 'Miss'\n",
    "- Lady, Countess, Dona are female honorifics of nobility\n",
    "- Don, Sir, Jonkheer are male honorifics of nobility\n",
    "- Capt refers to the Captain of the Titanic; Col, Major are military positions; Dr are doctors, Rev is a Reverend who all have special roles in society"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Royalty</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Special</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Title  Survived\n",
       "0   Master  0.575000\n",
       "1     Miss  0.702703\n",
       "2       Mr  0.156673\n",
       "3      Mrs  0.793651\n",
       "4  Royalty  0.600000\n",
       "5  Special  0.277778"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Don', 'Sir', 'Jonkheer', 'Dona'],'Royalty')\n",
    "    dataset['Title'] = dataset['Title'].replace(['Capt', 'Col','Dr','Major','Rev'],'Special')\n",
    "\n",
    "df_train[['Title','Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For feature mapping each title with the likelyhood of the passenger's survival, we can map these titles as numbers for our learning algorithms to be able to use these columns to predict whether the passenger survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      4  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      4  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Master\": 1, \"Miss\": 2, \"Mrs\": 3, \"Mr\": 4, \"Royalty\": 5, \"Special\": 6}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have extracted the features out of the name, we do not have the need to uniquely identify the passengers with their name and passengerId, so we could drop those columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = df_train.drop(['Name', 'PassengerId'], axis=1)\n",
    "df_test = df_test.drop(['Name'], axis=1)\n",
    "combine = [df_train, df_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting a simple category into a feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our learning algorithms to train better on this data, let us map female to 0 and male to 1 for the 'Sex' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      4\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a21ed4eb8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BzwZ2AIdX1T0waE7gsAM8ZkuSnUl2zs7Ojlat1DD7S+rH0MGW5EnAR4A3VNXX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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21ed4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_sex_age_grid = sns.FacetGrid(df_train, row='Pclass', col='Sex')\n",
    "pclass_sex_age_grid.map(plt.hist, 'Age', alpha=0.5, bins=20)\n",
    "pclass_sex_age_grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Populating null values with predicted data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the number of values in the training data with null values in the Age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Age.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coming up with median age based on passenger class and sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 42.,  28.,  24.],\n",
       "       [ 41.,  24.,  22.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age = np.zeros((2,3))\n",
    "for dataset in combine:\n",
    "    for sex in range(0,2):\n",
    "        for pclass in range(0,3):\n",
    "            guess_df = dataset[(dataset['Sex'] == sex) & \\\n",
    "                                  (dataset['Pclass'] == pclass+1)]['Age'].dropna()\n",
    "            age_guess = guess_df.median()\n",
    "            median_age[sex,pclass] = age_guess\n",
    "median_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing null values of Age with median age for the passenger's class and sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      4\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      4"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = median_age[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a feature out of a column with continuous numerical value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the ages of the training data into 5 parts to split a continuous numerical value (age) into bands (i.e age bands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['AgeBand'] = pd.cut(df_train['Age'], 5)\n",
    "df_train[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode the age band that we have calculated as AgeG which indicates age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0   22      1      0   7.2500        S      4   \n",
       "1         1       1    1   38      1      0  71.2833        C      3   \n",
       "2         1       3    1   26      0      0   7.9250        S      2   \n",
       "3         1       1    1   35      1      0  53.1000        S      3   \n",
       "4         0       3    0   35      0      0   8.0500        S      4   \n",
       "\n",
       "        AgeBand  AgeG  \n",
       "0  (16.0, 32.0]   1.0  \n",
       "1  (32.0, 48.0]   2.0  \n",
       "2  (16.0, 32.0]   1.0  \n",
       "3  (32.0, 48.0]   2.0  \n",
       "4  (32.0, 48.0]   2.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'AgeG'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'AgeG'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'AgeG'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'AgeG'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'AgeG'] = 4\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One we are done with the making the age feature into the groups we can lose those features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeG</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  SibSp  Parch     Fare Embarked  Title  AgeG\n",
       "0         0       3    0      1      0   7.2500        S      4   1.0\n",
       "1         1       1    1      1      0  71.2833        C      3   2.0\n",
       "2         1       3    1      0      0   7.9250        S      2   1.0\n",
       "3         1       1    1      1      0  53.1000        S      3   2.0\n",
       "4         0       3    0      0      0   8.0500        S      4   2.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.drop(['AgeBand','Age'], axis=1)\n",
    "combine = [df_train, df_test]\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining features to create a new feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us create a feature FamilySize by combining the SibSp and Parch columns which indicate the number of siblings and parent/children that the passenger had on the Titanic to form the FamilySize feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>5</th>\n",
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       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "df_train[['FamilySize','Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us drop the SibSp and Parch columns since we have included them into our FamilySize feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex     Fare Embarked  Title  AgeG  FamilySize\n",
       "0         0       3    0   7.2500        S      4   1.0           2\n",
       "1         1       1    1  71.2833        C      3   2.0           2\n",
       "2         1       3    1   7.9250        S      2   1.0           1\n",
       "3         1       1    1  53.1000        S      3   2.0           2\n",
       "4         0       3    0   8.0500        S      4   2.0           1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.drop(['SibSp', 'Parch'], axis=1)\n",
    "df_test = df_test.drop(['SibSp','Parch'], axis=1)\n",
    "combine = [df_train, df_test]\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting a categorical feature into a numeric feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to convert the 'Embarked' feature into a numeric feature we need to fill in the null values of the column with actual data, so we are finding the most common value in this column by generating the mode of the data. The "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = df_train.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing the value of the 'Embarked' column in the data with the most common embarked port, is denoted by 'S'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "\n",
    "df_train[['Embarked','Survived']].groupby(['Embarked'], as_index = False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now map each of the 'Embarked' ports into numerical values to convert it into a numeric feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeG</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex     Fare  Embarked  Title  AgeG  FamilySize\n",
       "0         0       3    0   7.2500         0      4   1.0           2\n",
       "1         1       1    1  71.2833         1      3   2.0           2\n",
       "2         1       3    1   7.9250         0      2   1.0           1\n",
       "3         1       1    1  53.1000         0      3   2.0           2\n",
       "4         0       3    0   8.0500         0      4   2.0           1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are dropping the 'Fare' column because we are assuming that the passenger class column is indicatory of the price of the ticket. This is done to simplify the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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",
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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>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeG</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Embarked  Title  AgeG  FamilySize\n",
       "0         0       3    0         0      4   1.0           2\n",
       "1         1       1    1         1      3   2.0           2\n",
       "2         1       3    1         0      2   1.0           1\n",
       "3         1       1    1         0      3   2.0           2\n",
       "4         0       3    0         0      4   2.0           1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.drop(['Fare'], axis=1)\n",
    "df_test = df_test.drop(['Fare'], axis=1)\n",
    "combine = [df_train, df_test]\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixing the age group column so all the values are int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeG</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Embarked  Title  AgeG  FamilySize\n",
       "0         0       3    0         0      4     1           2\n",
       "1         1       1    1         1      3     2           2\n",
       "2         1       3    1         0      2     1           1\n",
       "3         1       1    1         0      3     2           2\n",
       "4         0       3    0         0      4     2           1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.AgeG = dataset.AgeG.astype(int)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeG</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Embarked  Title  AgeG  FamilySize\n",
       "0          892       3    0         2      4     2           1\n",
       "1          893       3    1         0      3     2           2\n",
       "2          894       2    0         2      4     3           1\n",
       "3          895       3    0         0      4     1           1\n",
       "4          896       3    1         0      3     1           3"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = df_test.drop(['Age'], axis=1)\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model, predict and solve the problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem that we are left to solve is to predict whether the passenger survives the Titanic or not, which is a classification problem. We are also given the columns that can be correlated to the survival of the passenger. We intend to do supervised learning plus classification and regression on a given set of features that we have extracted from the data. We will use the following methods in order to build our analysis\n",
    "\n",
    "- Logistic Regression\n",
    "- Support Vector Machines\n",
    "- KNN or K-Nearest Neighbors\n",
    "- Decision Trees\n",
    "- Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we implement our models and predict the survivors, we should clean our train and test data in order to be able to fit in the various classifiers available to us in the sci-kit learn package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = df_train.drop(\"Survived\", axis=1)\n",
    "Y_train = df_train['Survived']\n",
    "X_test = df_test.drop('PassengerId', axis=1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.260000000000005"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, Y_train)\n",
    "Y_pred = log_reg.predict(X_test)\n",
    "acc_log = round(log_reg.score(X_train,Y_train)*100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the logistic regressor we can find out the correlations between different columns within the data towards increasing the survival of a passenger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.064339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.230931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FamilySize</td>\n",
       "      <td>-0.207689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AgeG</td>\n",
       "      <td>-0.298590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Title</td>\n",
       "      <td>-0.473545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-1.079056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature  Correlation\n",
       "1         Sex     2.064339\n",
       "2    Embarked     0.230931\n",
       "5  FamilySize    -0.207689\n",
       "4        AgeG    -0.298590\n",
       "3       Title    -0.473545\n",
       "0      Pclass    -1.079056"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(df_train.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df['Correlation'] = pd.Series(log_reg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can hence concluded Sex being female (Sex=1) is a major determining factor in the survial of the passenger. We can also conclude that as the passenger class number increase, it reduces the chance of survival of the passenger which means (Pclass = 3 < Pclass = 2 < Pclass = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support vector machines are supervised learning models that are used for classification and regression analysis and is mainly used to differentiate between two categories, for which it builds a model and assigns new test samples to either category, making it a non-probabilitistic linear classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.390000000000001"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svm = round(svc.score(X_train, Y_train) *100 ,2)\n",
    "acc_svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors or KNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Nearest Neighbors classifier classifies samples based on the number of nearest neighbor samples k which are under one category, when there are samples which are close to the the value which defines the problem space they are classified under that value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.950000000000003"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) *100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Trees are classifiers which map features as tree branches to finally come to a conclusive value which is denoted by the leaf of the tree. Tree models generally take a finite set of values and these trees are known as classification trees. The branches in the tree represent the decision based branches based on features available and the leaves of the tree represent the values that are derived from the conclusion of decisions made on those features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.200000000000003"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, Y_train)\n",
    "Y_pred = tree.predict(X_test)\n",
    "acc_tree = round(tree.score(X_train, Y_train)*100, 2)\n",
    "acc_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest classifiers are classifiers which create multiple decision trees for the problem space in training and outputting the class that is mostly predicted by each individual tree, or the mean prediction amongst the individual trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.079999999999998"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_train, Y_train)\n",
    "Y_pred = forest.predict(X_test)\n",
    "acc_forest = round(forest.score(X_train, Y_train) *100 , 2)\n",
    "acc_forest\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem solving report and final solution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Method</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>86.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>83.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>83.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>81.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Method  Score\n",
       "3       Decision Trees  86.20\n",
       "4        Random Forest  86.08\n",
       "2                  KNN  83.95\n",
       "1                  SVM  83.39\n",
       "0  Logistic Regression  81.26"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = pd.DataFrame({'Method': ['Logistic Regression','SVM','KNN','Decision Trees','Random Forest'], 'Score': [acc_log, acc_svm, acc_knn, acc_tree, acc_forest]})\n",
    "model.sort_values(by='Score', ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the decision tree classifer outputs a 86.2% accuracy on the test set and is the most successful model for the set of features and the model that we have built."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supply or submit the results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To submit the results, build a dataframe to contain your values and use the to_csv function to output a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": df_test[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "submission.to_csv('titanic/submission.csv', index=False)"
   ]
  }
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