Predicting Titanic Survivors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plot\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data\n",
    "data_train = pd.read_csv('train.csv')\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Viewing the data type of each column.\n",
    "data_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we need to convert objects(which are strings in pandas), to numbers, so that we can use these as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 66.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Get some basic information on the dataframe.\n",
    "data_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Age,Cabin,and Embarked have some missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating Descriptive statistics on the dataframe.\n",
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7192c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a few features.\n",
    "\n",
    "# Setting the global default size of matplotlib figures.\n",
    "plot.rc('figure',figsize=(10,5))\n",
    "\n",
    "# Set up a grid of plots.\n",
    "fig = plot.figure(figsize = (10,10))\n",
    "fig_dims=(3,3)      # 3X2->3 rows,2columns\n",
    "\n",
    "#data_train['Survived'].value_counts()\n",
    "#0    549\n",
    "#1    342\n",
    "#Returns object containing counts of unique values.\n",
    "\n",
    "# Plot Death and survival counts.\n",
    "plot.subplot2grid(fig_dims,(0,0))\n",
    "data_train['Survived'].value_counts().plot(kind='bar',title = 'Death and Survival Counts')\n",
    "plot.xticks(rotation=0)\n",
    "\n",
    "# Plot Pclass counts.\n",
    "plot.subplot2grid(fig_dims,(0,1))\n",
    "data_train['Pclass'].value_counts().plot(kind='bar',title = 'Passenger Class Counts')\n",
    "plot.xticks(rotation=0)\n",
    "\n",
    "# Plot Sex counts.\n",
    "plot.subplot2grid(fig_dims,(1,0))\n",
    "data_train['Sex'].value_counts().plot(kind='bar',title = 'Gender Counts')\n",
    "plot.xticks(rotation=0)\n",
    "\n",
    "# Plot Embarked counts.\n",
    "plot.subplot2grid(fig_dims,(1,1))\n",
    "data_train['Embarked'].value_counts().plot(kind='bar',title = 'Ports of Embarkation Counts')\n",
    "plot.xticks(rotation=0)\n",
    "\n",
    "# Plot Age counts.\n",
    "plot.subplot2grid(fig_dims,(2,0))\n",
    "data_train['Age'].hist(edgecolor='black')\n",
    "plot.title('Age Counts')\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now explore features to view the impact on survival rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: Passenger Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>372</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Pclass            \n",
       "1          80  136\n",
       "2          97   87\n",
       "3         372  119"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will determine which proportion of passenger survive based on their passenger data.\n",
    "\n",
    "pclass_ct = pd.crosstab(data_train['Pclass'],data_train['Survived'])\n",
    "pclass_ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x71924d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalizing croos tab to sum to 1.\n",
    "pclass_ct = pclass_ct.div(pclass_ct.sum(1).astype(float),axis=0)\n",
    "pclass_ct.plot(kind='bar', stacked=True,  title='Survival Rate By Passenger Classes')\n",
    "\n",
    "plot.xlabel('Passenger Class')\n",
    "plot.ylabel('Survival Rate')\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that passenger class have significant impact on whether the passenger survived or not. Also, those in third class have higher chance for death, while those in first class have higher chance for survival.\n",
    "So, we will consider this feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  \n",
       "0      0         A/5 21171   7.2500   NaN        S        1  \n",
       "1      0          PC 17599  71.2833   C85        C        0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S        0  \n",
       "3      0            113803  53.1000  C123        S        0  \n",
       "4      0            373450   8.0500   NaN        S        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here. the column sex is in string, we have to map it to number representation.\n",
    "\n",
    "dictionary = {'male':1,'female':0}\n",
    "data_train['Sex_Val'] = data_train['Sex'].map(dictionary).astype(int)\n",
    "data_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_Val</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>468</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Sex_Val           \n",
       "0          81  233\n",
       "1         468  109"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will determine which proportion of passenger survive based on their gender.\n",
    "sex_ct = pd.crosstab(data_train['Sex_Val'],data_train['Survived'])\n",
    "sex_ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7473ab0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalizing croos tab to sum to 1.\n",
    "sex_ct = sex_ct.div(sex_ct.sum(1).astype(float),axis=0)\n",
    "sex_ct.plot(kind='bar', stacked=True,  title='Survival Rate By Gender')\n",
    "\n",
    "plot.xlabel('Sex_Val')\n",
    "plot.ylabel('Survival Rate')\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of female survived, but majority of males did not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features: Sex And Passenger Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex: Pclass Num\n",
      " M:   1     122\n",
      " F:   1     94\n",
      " M:   2     108\n",
      " F:   2     76\n",
      " M:   3     347\n",
      " F:   3     144\n"
     ]
    }
   ],
   "source": [
    "# Now, we will see if we can gain any insight on survival rate by looking at both Sex and Pclasses i.e, to count male and \n",
    "# female in each classes.\n",
    "\n",
    "passengers_classes = data_train['Pclass'].unique()\n",
    "passengers_classes = sorted(passengers_classes)\n",
    "\n",
    "print('Sex: Pclass Num')\n",
    "for p_class in passengers_classes:\n",
    "    print(' M:  ',p_class,'   ',len(data_train[(data_train['Sex']=='male') & (data_train['Pclass']==p_class)   ]))\n",
    "    print(' F:  ',p_class,'   ', len(data_train[(data_train['Sex']=='female') & (data_train['Pclass']==p_class)   ]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female\n",
      "Survived   0   1\n",
      "Pclass          \n",
      "1          3  91\n",
      "2          6  70\n",
      "3         72  72 \n",
      "\n",
      "Male\n",
      "Survived    0   1\n",
      "Pclass           \n",
      "1          77  45\n",
      "2          91  17\n",
      "3         300  47\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75cb150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75f9670>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# After seeing the values of Pclass of both the genders, we will plot the survival rate by Sex and Pclass.\n",
    "# Plot survival rate by Passenger class for female\n",
    "female_data_train = data_train[data_train['Sex']=='female']\n",
    "female_ct = pd.crosstab(female_data_train['Pclass'],data_train['Survived'])\n",
    "print('Female')\n",
    "print(female_ct,'\\n')\n",
    "female_ct = female_ct.div(female_ct.sum(1).astype(float),axis=0)\n",
    "female_ct.plot(kind='bar', stacked=True,  title='Female Survival Rate By Pclass')\n",
    "plot.xlabel('Passenger Class')\n",
    "plot.ylabel('Survival Rate')\n",
    "plot.xticks(rotation=0)\n",
    "\n",
    "# Plot survival rate by Passenger class for male\n",
    "male_data_train = data_train[data_train['Sex']=='male']\n",
    "male_ct = pd.crosstab(male_data_train['Pclass'],data_train['Survived'])\n",
    "print('Male')\n",
    "print(male_ct)\n",
    "male_ct = male_ct.div(male_ct.sum(1).astype(float),axis=0)\n",
    "male_ct.plot(kind='bar', stacked=True,  title='Male Survival Rate By Pclass')\n",
    "plot.xlabel('Passenger Class')\n",
    "plot.ylabel('Survival Rate')\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of females in 1st and 2nd class survived.\n",
    "Whereas, men have the highest chances of survival in 1st class only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['Embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  Sex_Val  \n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN        0  \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN        0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here, we have two values which are null.\n",
    "# We need to fill it, inaddition, we have to map these strings to number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'Q': 2, 'S': 0, nan: 3}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the unique values of Embarked\n",
    "embarked_locs = data_train['Embarked'].unique()\n",
    "\n",
    "dictionary = dict(zip(embarked_locs,range(0, len(embarked_locs) + 1)))\n",
    "dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  Sex_Val  \\\n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN        0   \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN        0   \n",
       "\n",
       "     Embarked_Val  \n",
       "61              3  \n",
       "829             3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dictionary = {nan: 0, 'C' : 1, 'Q' : 2, 'S' : 3 }\n",
    "data_train['Embarked_Val']=data_train['Embarked'].map(dictionary).astype(int)\n",
    "data_train[data_train['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \n",
       "0      0         A/5 21171   7.2500   NaN        S        1             0  \n",
       "1      0          PC 17599  71.2833   C85        C        0             1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S        0             0  \n",
       "3      0            113803  53.1000  C123        S        0             0  \n",
       "4      0            373450   8.0500   NaN        S        1             0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot the Historam for Embarked_Val."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x715f830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train['Embarked_Val'].hist(bins=len(embarked_locs),range=(0,3))\n",
    "plot.title('Port of Embarkation Historam')\n",
    "plot.xlabel('Port Of Embarkation')\n",
    "plot.ylabel('Count')\n",
    "plot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "since the vast majority of passengers embarked in 'S':3, we assign the missing values in  Embarked to 'S'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{nan: 3, 'C': 1, 'Q': 2, 'S': 0}\n"
     ]
    }
   ],
   "source": [
    "from numpy import nan\n",
    "#dictionarys={'C': 1, 'Q': 2, 'S': 0, 'nan': 3}\n",
    "embarked_locs = data_train['Embarked'].unique()\n",
    "\n",
    "dictionary = dict(zip(embarked_locs,range(0, len(embarked_locs) + 1)))\n",
    "print(dictionary)\n",
    "\n",
    "if(len(data_train[data_train['Embarked'].isnull()] > 0)):\n",
    "    data_train.replace({'Embarked_Val' : { dictionary[nan] : dictionary['S']}},inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked_locs = data_train['Embarked_Val'].unique()\n",
    "embarked_locs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  Sex_Val  \\\n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN        0   \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN        0   \n",
       "\n",
       "     Embarked_Val  \n",
       "61              0  \n",
       "829             0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[data_train['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we do not have any NaN for Embarked_Val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Val</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>427</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived        0    1\n",
       "Embarked_Val          \n",
       "0             427  219\n",
       "1              75   93\n",
       "2              47   30"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#{'C': 1, 'Q': 2, 'S': 0}\n",
    "embarked_ct = pd.crosstab(data_train['Embarked_Val'],data_train['Survived'])\n",
    "embarked_ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7841d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalizing croos tab to sum to 1.\n",
    "embarked_ct = embarked_ct.div(embarked_ct.sum(1).astype(float),axis=0)\n",
    "embarked_ct.plot(kind='bar', stacked=True,  title='Survival Rate By Gender')\n",
    "\n",
    "plot.xlabel('Port Of Embarkation')\n",
    "plot.ylabel('Survival Rate')\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those who embarked in location 'C':1, have higher rate of survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f228d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, We will plot a graph to determine gender and passenger class makeup for each port.\n",
    "fig = plot.figure(figsize=(1,1))\n",
    "#{'C': 1, 'Q': 2, 'S': 0}\n",
    "#dictionary = {'male':1,'female':0}\n",
    "\n",
    "# Graph for gender and embarkation location at location 'S':0.\n",
    "embarkedLocation = 0\n",
    "embarked_0_data_train = data_train[data_train['Embarked_Val'] == embarkedLocation]\n",
    "embarked_0_ct = pd.crosstab(embarked_0_data_train['Embarked_Val'],data_train['Sex_Val'])\n",
    "\n",
    "#print(embared_0_ct,'\\n')\n",
    "#plot.subplot2grid((2,3),(0,0))\n",
    "embarked_0_ct.plot(kind='bar')\n",
    "plot.xlabel('By Gender')\n",
    "plot.ylabel('Embarked In Location %d' %(embarkedLocation))\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n",
    "embarkedLocation=embarkedLocation+1\n",
    "\n",
    "# Graph for gender and embarkation location at location 'C':1.\n",
    "embarked_1_data_train = data_train[data_train['Embarked_Val'] == embarkedLocation]\n",
    "embarked_1_ct = pd.crosstab(embarked_1_data_train['Embarked_Val'],data_train['Sex_Val'])\n",
    "\n",
    "#print(embared_0_ct,'\\n')\n",
    "#plot.subplot2grid((2,3),(0,0))\n",
    "embarked_1_ct.plot(kind='bar')\n",
    "plot.xlabel('By Gender')\n",
    "plot.ylabel('Embarked In Location %d' %(embarkedLocation))\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n",
    "\n",
    "embarkedLocation=embarkedLocation+1\n",
    "\n",
    "\n",
    "# Graph for gender and embarkation location at location 'Q':2.\n",
    "embarked_2_data_train = data_train[data_train['Embarked_Val'] == embarkedLocation]\n",
    "embarked_2_ct = pd.crosstab(embarked_2_data_train['Embarked_Val'],data_train['Sex_Val'])\n",
    "\n",
    "#print(embared_0_ct,'\\n')\n",
    "#plot.subplot2grid((2,3),(0,0))\n",
    "embarked_2_ct.plot(kind='bar')\n",
    "plot.xlabel('By Gender')\n",
    "plot.ylabel('Emabarked In Location %d' %(embarkedLocation))\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f89ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x786dd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph for gender and embarkation location at location 'S':0.\n",
    "embarkedLocation = 0\n",
    "embarked_0_data_train = data_train[data_train['Embarked_Val'] == embarkedLocation]\n",
    "embarked_0_ct = pd.crosstab(embarked_0_data_train['Embarked_Val'],data_train['Pclass'])\n",
    "\n",
    "#print(embared_0_ct,'\\n')\n",
    "#plot.subplot2grid((2,3),(0,0))\n",
    "embarked_0_ct.plot(kind='bar')\n",
    "plot.xlabel('By Passenger Class')\n",
    "plot.ylabel('Emabarked In Location %d' %(embarkedLocation))\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n",
    "embarkedLocation=embarkedLocation+1\n",
    "\n",
    "\n",
    "embarked_1_data_train = data_train[data_train['Embarked_Val'] == embarkedLocation]\n",
    "embarked_1_ct = pd.crosstab(embarked_1_data_train['Embarked_Val'],data_train['Pclass'])\n",
    "\n",
    "#print(embared_0_ct,'\\n')\n",
    "#plot.subplot2grid((2,3),(0,0))\n",
    "embarked_1_ct.plot(kind='bar')\n",
    "plot.xlabel('By Passenger Class')\n",
    "plot.ylabel('Emabarked In Location %d' %(embarkedLocation))\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n",
    "embarkedLocation=embarkedLocation+1\n",
    "\n",
    "\n",
    "\n",
    "embarked_2_data_train = data_train[data_train['Embarked_Val'] == embarkedLocation]\n",
    "embarked_2_ct = pd.crosstab(embarked_2_data_train['Embarked_Val'],data_train['Pclass'])\n",
    "\n",
    "#print(embared_0_ct,'\\n')\n",
    "#plot.subplot2grid((2,3),(0,0))\n",
    "embarked_2_ct.plot(kind='bar')\n",
    "plot.xlabel('By Passenger Class')\n",
    "plot.ylabel('Emabarked In Location %d' %(embarkedLocation))\n",
    "plot.xticks(rotation=0)\n",
    "plot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_train=pd.concat([data_train,pd.get_dummies(data_train['Embarked_Val'],prefix='Embarked_Val')],axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sex  Pclass  Age\n",
       "5     male       3  NaN\n",
       "17    male       2  NaN\n",
       "19  female       3  NaN\n",
       "26    male       3  NaN\n",
       "28  female       3  NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to view missing values.\n",
    "data_train[data_train['Age'].isnull()][['Sex','Pclass','Age']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine age typical for each passenger class by Sex_Val.We will use the median intead of mean.\n",
    "data_train['AgeFill']=data_train['Age']\n",
    "\n",
    "data_train['AgeFill']=data_train['AgeFill'].groupby([data_train['Sex_Val'],data_train['Pclass']]).apply(lambda x:x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if AgeFill does not contain any missing values.\n",
    "data_train['AgeFill'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n",
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:748: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= mn)\n",
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:749: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= mx)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Age')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb3150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of agefill segmented by survivor.\n",
    "fi, ax = plot.subplots(2,1,figsize=(10,10))\n",
    "bin_size = 10\n",
    "data1 = data_train[data_train['Survived'] == 0]['Age']\n",
    "data2 = data_train[data_train['Survived'] == 1]['Age']\n",
    "max_age = max(data_train['AgeFill'])\n",
    "bins=(int)(max_age/bin_size)\n",
    "\n",
    "ax[0].hist([data1,data2],edgecolor='black', range = (1, max_age),bins=bins, stacked = True)\n",
    "ax[0].legend(('Died','Survived'),loc='best')\n",
    "ax[0].set_title('Survivors By Age Group Histograms')\n",
    "ax[0].set_xlabel('Age')\n",
    "ax[0].set_ylabel('Count')\n",
    "\n",
    "# Scatter plot survived and AgeFill\n",
    "ax[1].scatter(data_train['Survived'],data_train['AgeFill'],edgecolor='black',facecolor='blue')\n",
    "ax[1].set_title('Survivors by Age plot')\n",
    "ax[1].set_xlabel('Survived')\n",
    "ax[1].set_ylabel('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will keep on digging further, because, above graph do not seems to clearly show any insigts, it shows only survived(1) or not survived(0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7867770>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7867b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot AgeFill density by Pclass.\n",
    "for pclass in passengers_classes:\n",
    "    data_train.AgeFill[data_train.Pclass == pclass].plot(kind='kde')\n",
    "plot.title('Age Density Plot by Passenger Class')\n",
    "plot.xlabel('Age')\n",
    "plot.legend(('1st Class', '2nd Class', '3rd Class'), loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at AgeFill density by Pclass, we see the first class passengers were generally older then second class passengers, which in turn were older than third class passengers. We have determined that first class passengers had a higher survival rate than second class passengers, which in turn had a higher survival rate than third class passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8921090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot.figure(figsize=(10,10))\n",
    "\n",
    "# Plot the AgeFill histogram for Survivors\n",
    "plot.subplot2grid((3,1),(0,0))\n",
    "survived_data_train = data_train[data_train['Survived']==1]\n",
    "survived_data_train['AgeFill'].hist(range=(1,max_age),bins=bins,edgecolor='black')\n",
    "\n",
    "# Plot the AgeFill histogram for Females\n",
    "plot.subplot2grid((3,1),(1,0))\n",
    "survived_data_train = data_train[(data_train['Sex_Val']==0) & (data_train['Survived']==1)]\n",
    "survived_data_train['AgeFill'].hist(range=(1,max_age),bins=bins,edgecolor='black')\n",
    "\n",
    "# Plot the AgeFill histogram for first class passengers\n",
    "plot.subplot2grid((3,1),(2,0))\n",
    "survived_data_train = data_train[(data_train['Pclass']==1) & (data_train['Survived']==1)]\n",
    "survived_data_train['AgeFill'].hist(range=(1,max_age),bins=bins,edgecolor='black')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first graph, we see that most survivors come from the 20s to 30s age ranges and might be explained by the following two graphs. The second graph shows most females are within their 20s. The third graph shows most first class passengers are within their 30s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features: Family Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We wil now try to create new features or modify the existing features which might be advantageous to a machuine learning alorgithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "      <th>Embarked_Val_0</th>\n",
       "      <th>Embarked_Val_1</th>\n",
       "      <th>Embarked_Val_2</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S        1             0   \n",
       "1      0          PC 17599  71.2833   C85        C        0             1   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S        0             0   \n",
       "3      0            113803  53.1000  C123        S        0             0   \n",
       "4      0            373450   8.0500   NaN        S        1             0   \n",
       "\n",
       "   Embarked_Val_0  Embarked_Val_1  Embarked_Val_2  AgeFill  FamilySize  \n",
       "0               1               0               0     22.0           1  \n",
       "1               0               1               0     38.0           1  \n",
       "2               1               0               0     26.0           0  \n",
       "3               1               0               0     35.0           1  \n",
       "4               1               0               0     35.0           0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets create a new feature,FamilySize, that is a sum of parch(number of parents/cildren on board) and \n",
    "# sibsp(number of sibling or spouse).\n",
    "data_train['FamilySize'] = data_train['Parch'] + data_train['SibSp']\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x88f1dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train['FamilySize'].hist(edgecolor = 'black')\n",
    "plot.title('Family Size Histogram')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 10]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot unique values of Family and its maximum.\n",
    "family_size = sorted(data_train['FamilySize'].unique())\n",
    "sorted(family_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n",
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:748: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= mn)\n",
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:749: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= mx)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ce30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1 = data_train[data_train['Survived'] == 0]['FamilySize']\n",
    "data2 = data_train[data_train['Survived'] == 1]['FamilySize']\n",
    "family_size_max = max(family_size)\n",
    "bins=(int)(family_size_max+1)\n",
    "\n",
    "fig, ax = plot.subplots()\n",
    "plot.hist([data1,data2],edgecolor = 'black', range = (0, family_size_max),bins=bins, stacked = True)\n",
    "ax.legend(('Died','Survived'),loc = 'best')\n",
    "ax.set_title('Survivors By Family Size Histograms')\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Count')\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val</th>\n",
       "      <th>Embarked_Val_0</th>\n",
       "      <th>Embarked_Val_1</th>\n",
       "      <th>Embarked_Val_2</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Sex_Val  Embarked_Val  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S        1             0   \n",
       "1      0          PC 17599  71.2833   C85        C        0             1   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S        0             0   \n",
       "3      0            113803  53.1000  C123        S        0             0   \n",
       "4      0            373450   8.0500   NaN        S        1             0   \n",
       "\n",
       "   Embarked_Val_0  Embarked_Val_1  Embarked_Val_2  AgeFill  FamilySize  \n",
       "0               1               0               0     22.0           1  \n",
       "1               0               1               0     38.0           1  \n",
       "2               1               0               0     26.0           0  \n",
       "3               1               0               0     35.0           1  \n",
       "4               1               0               0     35.0           0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Capt',\n",
       " 'Col',\n",
       " 'Don',\n",
       " 'Dr',\n",
       " 'Jonkheer',\n",
       " 'Lady',\n",
       " 'Major',\n",
       " 'Master',\n",
       " 'Miss',\n",
       " 'Mlle',\n",
       " 'Mme',\n",
       " 'Mr',\n",
       " 'Mrs',\n",
       " 'Ms',\n",
       " 'Rev',\n",
       " 'Sir',\n",
       " 'the Countess'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will extract the titles from the names(for eg. Mr., Mrs., Royalty etc)\n",
    "titles=set()\n",
    "for names in data_train['Name']:\n",
    "    titles.add(names.split(',')[1].split('.')[0].strip())\n",
    "titles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create a dictionary so that we can map titles into the one in dictionary.\n",
    "\n",
    "Title_Dictionary = {\n",
    "    \"Capt\": \"Officer\",\n",
    "    \"Col\": \"Officer\",\n",
    "    \"Major\": \"Officer\",\n",
    "    \"Jonkheer\": \"Royalty\",\n",
    "    \"Don\": \"Royalty\",\n",
    "    \"Sir\" : \"Royalty\",\n",
    "    \"Dr\": \"Officer\",\n",
    "    \"Rev\": \"Officer\",\n",
    "    \"the Countess\":\"Royalty\",\n",
    "    \"Mme\": \"Mrs\",\n",
    "    \"Mlle\": \"Miss\",\n",
    "    \"Ms\": \"Mrs\",\n",
    "    \"Mr\" : \"Mr\",\n",
    "    \"Mrs\" : \"Mrs\",\n",
    "    \"Miss\" : \"Miss\",\n",
    "    \"Master\" : \"Master\",\n",
    "    \"Lady\" : \"Royalty\"\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_train[\"Title\"] = data_train[\"Name\"].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "data_train[\"Title\"] = data_train[\"Title\"].map(Title_Dictionary)\n",
    "data_train[\"Title\"].isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 26 columns):\n",
      "PassengerId        891 non-null int64\n",
      "Survived           891 non-null int64\n",
      "Pclass             891 non-null int64\n",
      "Name               891 non-null object\n",
      "Sex                891 non-null object\n",
      "Age                714 non-null float64\n",
      "SibSp              891 non-null int64\n",
      "Parch              891 non-null int64\n",
      "Ticket             891 non-null object\n",
      "Fare               891 non-null float64\n",
      "Cabin              204 non-null object\n",
      "Embarked           889 non-null object\n",
      "Sex_Val            891 non-null int32\n",
      "Embarked_Val       891 non-null int64\n",
      "Embarked_Val_0     891 non-null uint8\n",
      "Embarked_Val_1     891 non-null uint8\n",
      "Embarked_Val_2     891 non-null uint8\n",
      "AgeFill            891 non-null float64\n",
      "FamilySize         891 non-null int64\n",
      "Title              891 non-null object\n",
      "Titles__Master     891 non-null uint8\n",
      "Titles__Miss       891 non-null uint8\n",
      "Titles__Mr         891 non-null uint8\n",
      "Titles__Mrs        891 non-null uint8\n",
      "Titles__Officer    891 non-null uint8\n",
      "Titles__Royalty    891 non-null uint8\n",
      "dtypes: float64(3), int32(1), int64(7), object(6), uint8(9)\n",
      "memory usage: 101.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# So, now, we will create dummy variables of column titles.\n",
    "\n",
    "data_train=pd.concat([data_train,pd.get_dummies(data_train['Title'],prefix='Titles_')],axis=1)\n",
    "data_train.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>...</th>\n",
       "      <th>Embarked_Val_2</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "      <th>Titles__Master</th>\n",
       "      <th>Titles__Miss</th>\n",
       "      <th>Titles__Mr</th>\n",
       "      <th>Titles__Mrs</th>\n",
       "      <th>Titles__Officer</th>\n",
       "      <th>Titles__Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare       ...       Embarked_Val_2 AgeFill  \\\n",
       "0      0         A/5 21171   7.2500       ...                    0    22.0   \n",
       "1      0          PC 17599  71.2833       ...                    0    38.0   \n",
       "2      0  STON/O2. 3101282   7.9250       ...                    0    26.0   \n",
       "3      0            113803  53.1000       ...                    0    35.0   \n",
       "4      0            373450   8.0500       ...                    0    35.0   \n",
       "\n",
       "   FamilySize  Title  Titles__Master  Titles__Miss  Titles__Mr  Titles__Mrs  \\\n",
       "0           1     Mr               0             0           1            0   \n",
       "1           1    Mrs               0             0           0            1   \n",
       "2           0   Miss               0             1           0            0   \n",
       "3           1    Mrs               0             0           0            1   \n",
       "4           0     Mr               0             0           1            0   \n",
       "\n",
       "   Titles__Officer Titles__Royalty  \n",
       "0                0               0  \n",
       "1                0               0  \n",
       "2                0               0  \n",
       "3                0               0  \n",
       "4                0               0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature: Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_train['Cabin'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A cabin number looks like ‘C123’ and the letter refers to the deck. Therefore we’re going to extract these and \n",
    "# create a new feature, that contains a persons deck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_train['Cabin'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, need to fill in the NaN values.\n",
    "#data_train['Cabin'] = data_train['Cabin'].fillna('U0')\n",
    "#data_train['Cabin'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nimport re\\ndata_train['Deck']=0\\ndata_Frame_Len = data_train.shape[0]\\n\\nfor i in range(data_Frame_Len):\\n    data_train['Deck'][i]=re.sub(r'[0-9]+', '', data_train['Cabin'][i])\\ndata_train['Deck']\\n\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "import re\n",
    "data_train['Deck']=0\n",
    "data_Frame_Len = data_train.shape[0]\n",
    "\n",
    "for i in range(data_Frame_Len):\n",
    "    data_train['Deck'][i]=re.sub(r'[0-9]+', '', data_train['Cabin'][i])\n",
    "data_train['Deck']\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_train['Deck'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n# From above we see that there are some values with multiple alphabets.\\n# They are:\\ndata_Frame_Len = data_train.shape[0]\\nprint('In these positions, we have one or more values.')\\nprint('Pos','-','First',' ','Word')\\nfor i in range(data_Frame_Len):\\n    size=data_train['Deck'][i]\\n    if(len(size) >1):\\n        print(i,'-',size[0],' and ',size)\\n\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "# From above we see that there are some values with multiple alphabets.\n",
    "# They are:\n",
    "data_Frame_Len = data_train.shape[0]\n",
    "print('In these positions, we have one or more values.')\n",
    "print('Pos','-','First',' ','Word')\n",
    "for i in range(data_Frame_Len):\n",
    "    size=data_train['Deck'][i]\n",
    "    if(len(size) >1):\n",
    "        print(i,'-',size[0],' and ',size)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nDeck = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"U\": 8}\\n\\ndata_train[\\'Deck\\'] = data_train[\\'Cabin\\'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\\ndata_train[\\'Deck\\'] = data_train[\\'Deck\\'].map(Deck)\\ndata_train[\\'Deck\\'] = data_train[\\'Deck\\'].fillna(0)\\ndata_train[\\'Deck\\'] = data_train[\\'Deck\\'].astype(int)\\n\\n\\ndata_train[\\'Deck\\'].isnull().sum()\\n'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Deck = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"U\": 8}\n",
    "\n",
    "data_train['Deck'] = data_train['Cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
    "data_train['Deck'] = data_train['Deck'].map(Deck)\n",
    "data_train['Deck'] = data_train['Deck'].fillna(0)\n",
    "data_train['Deck'] = data_train['Deck'].astype(int)\n",
    "\n",
    "\n",
    "data_train['Deck'].isnull().sum()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_train['Deck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy decreases significantly after using Cabin column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Data Preparation For Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        object\n",
       "Sex         object\n",
       "Ticket      object\n",
       "Cabin       object\n",
       "Embarked    object\n",
       "Title       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Many Machine Learning algorithms do not work on strings and usually require data to be in an array,not dataFrame.\n",
    "# Drop only the column of type object.\n",
    "data_train.dtypes[data_train.dtypes.map(lambda x: x == 'object')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId          int64\n",
       "Survived             int64\n",
       "Pclass               int64\n",
       "Age                float64\n",
       "SibSp                int64\n",
       "Parch                int64\n",
       "Fare               float64\n",
       "Sex_Val              int32\n",
       "Embarked_Val         int64\n",
       "Embarked_Val_0       uint8\n",
       "Embarked_Val_1       uint8\n",
       "Embarked_Val_2       uint8\n",
       "AgeFill            float64\n",
       "FamilySize           int64\n",
       "Titles__Master       uint8\n",
       "Titles__Miss         uint8\n",
       "Titles__Mr           uint8\n",
       "Titles__Mrs          uint8\n",
       "Titles__Officer      uint8\n",
       "Titles__Royalty      uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the object we wont use.\n",
    "data_train = data_train.drop(['Name','Sex','Ticket','Cabin','Embarked','Title'],axis=1)\n",
    "data_train.dtypes\n",
    "# Drop Age column since we will be using AgeFill column.\n",
    "# Drop SibSp and Parch column. We will be using FamilySize column.\n",
    "# Drop passenerId column, since it will not be used as a feature.\n",
    "# Embarked_Val since,we will use dummy variables instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived             int64\n",
       "Pclass               int64\n",
       "Fare               float64\n",
       "Sex_Val              int32\n",
       "Embarked_Val_0       uint8\n",
       "Embarked_Val_1       uint8\n",
       "Embarked_Val_2       uint8\n",
       "AgeFill            float64\n",
       "FamilySize           int64\n",
       "Titles__Master       uint8\n",
       "Titles__Miss         uint8\n",
       "Titles__Mr           uint8\n",
       "Titles__Mrs          uint8\n",
       "Titles__Officer      uint8\n",
       "Titles__Royalty      uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = data_train.drop(['PassengerId','Age','SibSp','Parch','Embarked_Val'],axis=1)\n",
    "data_train.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Fare</th>\n",
       "      <th>Sex_Val</th>\n",
       "      <th>Embarked_Val_0</th>\n",
       "      <th>Embarked_Val_1</th>\n",
       "      <th>Embarked_Val_2</th>\n",
       "      <th>AgeFill</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Titles__Master</th>\n",
       "      <th>Titles__Miss</th>\n",
       "      <th>Titles__Mr</th>\n",
       "      <th>Titles__Mrs</th>\n",
       "      <th>Titles__Officer</th>\n",
       "      <th>Titles__Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Fare  Sex_Val  Embarked_Val_0  Embarked_Val_1  \\\n",
       "0         0       3   7.2500        1               1               0   \n",
       "1         1       1  71.2833        0               0               1   \n",
       "2         1       3   7.9250        0               1               0   \n",
       "3         1       1  53.1000        0               1               0   \n",
       "4         0       3   8.0500        1               1               0   \n",
       "\n",
       "   Embarked_Val_2  AgeFill  FamilySize  Titles__Master  Titles__Miss  \\\n",
       "0               0     22.0           1               0             0   \n",
       "1               0     38.0           1               0             0   \n",
       "2               0     26.0           0               0             1   \n",
       "3               0     35.0           1               0             0   \n",
       "4               0     35.0           0               0             0   \n",
       "\n",
       "   Titles__Mr  Titles__Mrs  Titles__Officer  Titles__Royalty  \n",
       "0           1            0                0                0  \n",
       "1           0            1                0                0  \n",
       "2           0            0                0                0  \n",
       "3           0            1                0                0  \n",
       "4           1            0                0                0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert the dataFrame to a numpy array.\n",
    "trainDataOnModel = data_train.values\n",
    "trainDataOnModel\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature Normalisation\n",
    "#x=data_train.iloc[:,1]\n",
    "\n",
    "#X_norm=x\n",
    "#mu=np.mean(x,axis=0)\n",
    "#sigma=np.std(x,axis=0,ddof=1)\n",
    "    \n",
    "#X_norm = (x - mu ) / sigma\n",
    "#X_norm_arr = X_norm.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Below, is a summary of the data wranling we we performed on training set. We will need to do the same operations to our trainig set.\n",
    "\n",
    "from numpy import nan\n",
    "\n",
    "def CleanData(data_Frame,drop_passenger_id):\n",
    "    \n",
    "      \n",
    "    \n",
    "    #Generate the mappin of sex from a string to a number representation.\n",
    "    dictionary = {'male':1,'female':0}\n",
    "    data_Frame['Sex_Val'] = data_Frame['Sex'].map(dictionary).astype(int)\n",
    "    \n",
    "    #Get the unique value of embarked.\n",
    "    embarked_locs = data_Frame['Embarked'].unique()\n",
    "    \n",
    "    #Generate the mapping of sex from a string to a number representation.\n",
    "    dictionary = dict(zip(embarked_locs,range(0, len(embarked_locs) + 1)))\n",
    "    data_Frame['Embarked_Val']=data_Frame['Embarked'].map(dictionary).astype(int)\n",
    "\n",
    "    #Transform embarked into dummy variables.\n",
    "    data_Frame=pd.concat([data_Frame,pd.get_dummies(data_Frame['Embarked_Val'],prefix='Embarked_Val')],axis=1)\n",
    "\n",
    "    #assign the missing values in Embarked to 'S'\n",
    "    if(len(data_Frame[data_Frame['Embarked'].isnull()] > 0)):\n",
    "        data_Frame.replace({'Embarked_Val' : { dictionary[nan] : dictionary['S']}},inplace=True )\n",
    "    \n",
    "    #Fill in missing values of fare with average fare.\n",
    "    if(len(data_Frame[data_Frame['Fare'].isnull()] > 0)):\n",
    "        av_Fare=data_Frame['Fare'].mean()\n",
    "        data_Frame.replace({None:av_Fare},inplace=True)\n",
    "        \n",
    "    # Determine age typical for each passenger class by Sex_Val.We will use the median intead of mean.\n",
    "    data_Frame['AgeFill']=data_Frame['Age']\n",
    "\n",
    "    data_Frame['AgeFill']=data_Frame['AgeFill'].groupby([data_train['Sex_Val'],data_train['Pclass']]).apply(lambda x:x.fillna(x.median()))    \n",
    "\n",
    "    # Lets create a new feature,FamilySize, that is a sum of parch(number of parents/cildren on board) and \n",
    "    # sibsp(number of sibling or spouse).\n",
    "    data_Frame['FamilySize']=data_Frame['Parch']+data_Frame['SibSp']\n",
    "    \n",
    "    # We will extract the titles from the names(for eg. Mr., Mrs., Royalty etc)\n",
    "    \n",
    "    title=set()\n",
    "    for names in data_Frame['Name']:\n",
    "        title.add(names.split(',')[1].split('.')[0].strip())\n",
    "    #print(title)\n",
    "    \n",
    "    data_Frame[\"Title\"] = data_Frame[\"Name\"].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "    data_Frame[\"Title\"] = data_Frame[\"Title\"].map(Title_Dictionary)\n",
    "    data_Frame = pd.concat([data_Frame,pd.get_dummies(data_Frame['Title'],prefix='Titles_')],axis=1)\n",
    "    data_Frame[\"Titles__Royalty\"]=0\n",
    "        \n",
    "    # Drop the object we wont use.\n",
    "    data_Frame = data_Frame.drop(['Name','Sex','Ticket','Cabin','Embarked','Embarked_Val','Title'],axis=1)\n",
    "    \n",
    "    # Drop Age column since we will be using AgeFill column.\n",
    "    # Drop SibSp and Parch column. We will be using FamilySize column.\n",
    "    # Drop passenerId column, since it will not be used as a feature.\n",
    "    data_Frame = data_Frame.drop(['Age','SibSp','Parch'],axis=1)\n",
    "\n",
    "    \n",
    "    if drop_passenger_id:\n",
    "        data_Frame = data_Frame.drop(['PassengerId'],axis=1)\n",
    "    \n",
    "    \n",
    "    \n",
    "    return data_Frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=50,max_features='sqrt')\n",
    "\n",
    "# Fit the training data and create the decision trees.\n",
    "train_features = data_train.iloc[:,1:]   # Excluding Survive column.\n",
    "train_target = data_train.iloc[:,0]       # Only survive column.\n",
    "\n",
    "# Fit the model to our training data.\n",
    "clf = clf.fit(train_features,train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "features=pd.DataFrame()\n",
    "features['feature']=train_features.columns\n",
    "features['importance']=clf.feature_importances_\n",
    "features.sort_values(by = ['importance'], ascending = True, inplace = True)\n",
    "features.set_index('feature',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c50b70>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAwAIE9AAAAAAAMQGAPAAAAAAADENgDAAAAAMAABPYAAAAAADAAgT0AAAAAAAxAYA8AAAAAAAMQ2AMAAAAAwAAE9gAAAAAAMACBPQAAAAAADEBgDwAAAAAAAxDYAwAAAADAAAT2AAAAAAAwAIE9AAAAAAAMQGAPAAAAAAADENgDAAAAAMAAViSwr6r7VNVl0+vrVXX9nO1PT+dsrKrnzmnzxKp6/16ua/tUw5VV9X+q6p57uP+NVXXl9P6oqvq5Pdk/AAAAAACr14oE9t19Y3cf1d1HJXl9klfObnf3Y6bTNiZ57k472TtunWr40STfTPLCvXito5II7AEAAAAASDLgkjhV9a3p7eYkj5tmvL9o3jk/WFVvqarPVdWlVfW0af/Dq+qiqc3Wqjp0GaVcmOQBU79VVa+YZt5fUVUnTvvfPnvtafudVXXCNJP+gqq6ZHo9Zm7HVXXXJC9PcuJU64lVdXVV3W86fpeq+nJV3XcHv59TqmpLVW3ZfstNy7g9AAAAAABGMlxgP8dpSS6YZry/ct6x05N8tLsfmeRJSV5RVT+Y5NQkr55m7m9Kct1SLlxV65Icm+TcadczMjMj/seSHDddb0OSP0/y/KnNQUkek+QDSf4pyU939yOSnJjkNXP77+5/TfJ7Sc6e7u/sJO9I8ovTKccluby7vzG/tu5+Y3dv6u5N6w44aCm3BwAAAADAgEYO7HflyUlOq6rLknw8yf5JHpiZWfG/W1X/NcmDuvvWRfZ7t6nPG5PcO8mHpv2PTXJWd2/v7huSfCLJI7v7E0keWlX3T/KcJO/t7tuT/ECSN1XVFUnOSXL4Aq79liS/PL3/lSRnLrJ2AAAAAABWsdUa2FeSZ85Z9/6B3f133f2/k5yQ5NYk51fVTy2y31un2fkPSnLXfH8N+9pFm7dnZmb88/P9kP1FSW7IzIz8TVNfu9TdX01yw1Tzo5J8cJG1AwAAAACwio0c2N+c5O47OXZ+kt+oqkqSqjp6+vngJNd292sys5zNkUu5cHfflOS/JHlJVf1Akv+bmfXm103rzD8+yUXT6W9N8ltTu6umfQcl+cfu/m6S/5hk3QLv788zszTOu7t7+1JqBwAAAABgdRo5sN+a5Paqunz+l84m+cPMLDuztaqunLaTmfXir5yWtTksyV8s9eLdfWmSy5OclOR9Uz2XJ/lokpd299en825I8ne54xI2f5rk5Kr6TJKHJfn2Di7xsSSHz37p7LTv3CQHxnI4AAAAAABrTnX3StewqlXVAUmuSPKIaWb+cvralOSV3f24hZy/34ZDe8PJr7rDvm2bj19OCQAAAAAA7GFVdXF3b9rdeSPPsB9eVR2X5AtJXrsHwvrTkrw3ye/sidoAAAAAAFhd1q90AXtTVd0nyUfm7Z5dT37+GvHHdveNi+m/uz+c5IFLLG9+X5uTbN4TfQEAAAAAsPrcqQP7KYA/aqXrAAAAAACA3bEkDgAAAAAADEBgDwAAAAAAAxDYAwAAAADAAAT2AAAAAAAwAIE9AAAAAAAMQGAPAAAAAAADENgDAAAAAMAABPYAAAAAADAAgT0AAAAAAAxAYA8AAAAAAAMQ2AMAAAAAwAAE9gAAAAAAMACBPQAAAAAADEBgDwAAAAAAAxDYAwAAAADAANavdAEs3REPOChbNh+/0mUAAAAAALAHmGEPAAAAAAADENgDAAAAAMAABPYAAAAAADAAgT0AAAAAAAxAYA8AAAAAAAMQ2AMAAAAAwAAE9gAAAAAAMACBPQAAAAAADEBgDwAAAAAAAxDYAwAAAADAAAT2AAAAAAAwAIE9AAAAAAAMQGAPAAAAAAADENgDAAAAAMAABPYAAAAAADAAgT0AAAAAAAxAYA8AAAAAAAMQ2AMAAAAAwAAE9gAAAAAAMACBPQAAAAAADEBgDwAAAAAAAxDYAwAAAADAAAT2AAAAAAAwgOrula6BJaqqm5N8caXrAPa5+yb5xkoXAexzxj6sPcY9rE3GPqxNxv6d34O6+367O2n9vqiEveaL3b1ppYsA9q2q2mLsw9pj7MPaY9zD2mTsw9pk7DPLkjgAAAAAADAAgT0AAAAAAAxAYL+6vXGlCwBWhLEPa5OxD2uPcQ9rk7EPa5OxTxJfOgsAAAAAAEMwwx4AAAAAAAYgsAcAAAAAgAEI7AdVVU+pqi9W1Zer6rQdHN+vqs6ejn+2qjbOOfY70/4vVtXP7Mu6gaVb6rivqo1VdWtVXTa9Xr+vaweWbgFj//FVdUlV3V5Vz5p37OSqunp6nbzvqgaWa5ljf/uc5/65+65qYLkWMPZfXFWfr6qtVfWRqnrQnGOe+7AKLXPce+avQdawH1BVrUvypSQ/neS6JJ9L8pzu/vycc/5TkiO7+9SqOinJz3f3iVV1eJKzkhyT5N8l+XCSh3X39n19H8DCLXPcb0zy/u7+0X1fObAcCxz7G5PcI8lLkpzb3e+Z9t87yZYkm5J0kouT/Hh3/799eAvAEixn7E/HvtXdB+7LmoHlW+DYf1KSz3b3LVX160meOP2b33MfVqHljPvpmGf+GmSG/ZiOSfLl7r62u/81ybuSPG3eOU9L8rbp/XuSHFtVNe1/V3d/p7u/kuTLU3/A2JYz7oHVa7djv7u3dffWJN+d1/Znknyou785/Wf9Q0mesi+KBpZtOWMfWL0WMvY/1t23TJufSXLw9N5zH1an5Yx71iiB/ZgekOSrc7avm/bt8Jzuvj3JTUnus8C2wHiWM+6T5JCqurSqPlFVj9vbxQJ7zHKe2575sHotd/zuX1VbquozVfX0PVsasBctduy/IMkHl9gWGMNyxn3imb8mrV/pAtihHc2Ynb920c7OWUhbYDzLGff/mOSB3X1jVf14kr+qqod397/s6SKBPW45z23PfFi9ljt+H9jdX6uqByf5aFVd0d3X7KHagL1nwWO/qn4pM8vfPGGxbYGhLGfcJ575a5IZ9mO6LsmPzNk+OMnXdnZOVa1PclCSby6wLTCeJY/7VSzezgAAActJREFUaQmsG5Okuy9Ock2Sh+31ioE9YTnPbc98WL2WNX67+2vTz2uTfDzJ0XuyOGCvWdDYr6rjkpye5ITu/s5i2gLDWc6498xfowT2Y/pckkOr6pCqumuSk5LM/yboc5PMfiv8s5J8tGe+QfjcJCdV1X5VdUiSQ5NctI/qBpZuyeO+qu43fZFNpk/dD01y7T6qG1iehYz9nTk/yZOr6l5Vda8kT572AeNb8tifxvx+0/v7JvnJJJ/fdStgELsd+1V1dJI3ZCa0+6c5hzz3YXVa8rj3zF+7LIkzoO6+var+c2YevuuSvKW7r6qqlyfZ0t3nJnlzkrdX1ZczM7P+pKntVVX17swM4NuTvLC7t6/IjQALtpxxn+TxSV5eVbcn2Z7k1O7+5r6/C2CxFjL2q+qRSd6X5F5JnlpVf9DdD+/ub1bVH2bmPwFJ8nJjH1aH5Yz9JP8+yRuq6ruZmYC1ubv95x1WgQX+m/8VSQ5Mck5VJck/dPcJnvuwOi1n3Mczf82qmUnZAAAAAADASrIkDgAAAAAADEBgDwAAAAAAAxDYAwAAAADAAAT2AAAAAAAwAIE9AAAAAAAMQGAPAAAAAAADENgDAAAAAMAA/j8fJ3jbkkeDEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x89bb2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='barh',figsize=(25,25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 6)\n"
     ]
    }
   ],
   "source": [
    "model=SelectFromModel(clf,prefit=True)\n",
    "train_reduced=model.transform(train_features)\n",
    "print(train_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total features coming out to be 6.\n",
    "# We will still continue with our original features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From above graph we see that feature 'Fare',is the most important one, along wit the 'age', 'Sex_Val', 'Family_size',\n",
    "# 'Titles_Mr.'. Title_Royalty is the least important, which can be seen from the test set,that there are no passenger who have the \n",
    "# title of being royalty which are:{\"Jonkheer\",\"Don\",\"Sir\",\"the Countess\",\"Lady\"}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating random forest object\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy of Random Forest: 0.983165\n"
     ]
    }
   ],
   "source": [
    "# Fit the training data and create the decision trees.\n",
    "train_features = trainDataOnModel[:,1:]   # Excluding Survive column.\n",
    "train_target = trainDataOnModel[:,0]       # Only survive column.\n",
    "\n",
    "# Fit the model to our training data.\n",
    "clf = clf.fit(train_features,train_target)\n",
    "score = clf.score(train_features,train_target)\n",
    "print('Mean accuracy of Random Forest: %f' %(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, we will get into test data.\n",
    "data_test = pd.read_csv('test.csv')\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Now, we will clean the ytest data(Data wrangle the test).\n",
    "\n",
    "data_test = CleanData(data_test,drop_passenger_id=False)\n",
    "\n",
    "# Convert it to numpy.\n",
    "testDataOnModel = data_test.values\n",
    "#data_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take the decision tree, and convert it to numpy array.\n",
    "x_test = testDataOnModel[:,1:]\n",
    "\n",
    "#predict the survival values  for the test data\n",
    "y_test = clf.predict(x_test).astype(int)\n",
    "# In kaggle, it comes out to be 0.75119 on 50% of test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare For Kaggle Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataFrame by combining index from the test data with the output of predictions, then write the results to the output:\n",
    "data_test['Survived']=y_test\n",
    "data_test[['PassengerId','Survived']].to_csv('result1.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Model Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 14) (891,)\n",
      "(712, 14) (712,)\n",
      "(179, 14) (179,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\solutions\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#To get idea of accuracy.\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "train_x,test_x,train_y,test_y=train_test_split(train_features,train_target,test_size=0.20,random_state=0)\n",
    "\n",
    "print(train_features.shape,train_target.shape)\n",
    "print(train_x.shape,train_y.shape)\n",
    "print(test_x.shape,test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.84\n"
     ]
    }
   ],
   "source": [
    "clf=clf.fit(train_x,train_y)\n",
    "predict_y=clf.predict(test_x)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy: %.2f'%(accuracy_score(test_y,predict_y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Score 0.84\n",
      "\n",
      "Confusion Matrix  [[100  10]\n",
      " [ 19  50]]\n",
      "          Predicted\n",
      "         |  0  |  1  |\n",
      "         |-----|-----|\n",
      "       0 | 100 |  10 |\n",
      "Actual   |-----|-----|\n",
      "       1 |  19 |  50 |\n",
      "         |-----|-----|\n"
     ]
    }
   ],
   "source": [
    "model_score=clf.score(test_x,test_y)\n",
    "print(\"Model Score %0.2f\\n\" %(model_score))\n",
    "\n",
    "confusion_matrix = metrics.confusion_matrix(test_y,predict_y)\n",
    "print (\"Confusion Matrix \", confusion_matrix)\n",
    "\n",
    "print (\"          Predicted\")\n",
    "print (\"         |  0  |  1  |\")\n",
    "print (\"         |-----|-----|\")\n",
    "print (\"       0 | %3d | %3d |\" % (confusion_matrix[0, 0],\n",
    "                                   confusion_matrix[0, 1]))\n",
    "print (\"Actual   |-----|-----|\")\n",
    "print (\"       1 | %3d | %3d |\" % (confusion_matrix[1, 0],\n",
    "                                   confusion_matrix[1, 1]))\n",
    "print (\"         |-----|-----|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "Not Survived       0.84      0.91      0.87       110\n",
      "    Survived       0.83      0.72      0.78        69\n",
      "\n",
      " avg / total       0.84      0.84      0.84       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Display the classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(test_y,predict_y,target_names=['Not Survived', 'Survived']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training: Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy of Support Vector Machine: 0.832772\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc=SVC(kernel='linear')\n",
    "\n",
    "# Fit the training data.\n",
    "train_features = trainDataOnModel[:,1:]   # Excluding Survive column.\n",
    "train_target = trainDataOnModel[:,0]       # Only survive column.\n",
    "\n",
    "svc=svc.fit(train_features,train_target)\n",
    "score = svc.score(train_features,train_target)\n",
    "print('Mean accuracy of Support Vector Machine: %f' %(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting: Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we will get into test data.\n",
    "data_test = pd.read_csv('test.csv')\n",
    "\n",
    "# Now, we will clean the ytest data(Data wrangle the test).\n",
    "data_test = CleanData(data_test,drop_passenger_id=False)\n",
    "\n",
    "# Convert it to numpy.\n",
    "testDataOnModel = data_test.values\n",
    "#data_test\n",
    "\n",
    "x_test = testDataOnModel[:,1:]\n",
    "\n",
    "#predict the survival values  for the test data\n",
    "y_test = svc.predict(x_test).astype(int)\n",
    "# In kaggle, it comes out to be 0.77990 on 50% of test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare For Kaggle Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataFrame by combining index from the test data with the output of predictions, then write the results to the output:\n",
    "data_test['Survived']=y_test\n",
    "data_test[['PassengerId','Survived']].to_csv('result2.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean accuracy of Linear Regression: 0.833895\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial')\n",
    "\n",
    "# Fit the training data.\n",
    "train_features = trainDataOnModel[:,1:]   # Excluding Survive column.\n",
    "train_target = trainDataOnModel[:,0]       # Only survive column.\n",
    "\n",
    "lr=lr.fit(train_features,train_target)\n",
    "score = lr.score(train_features,train_target)\n",
    "print('Mean accuracy of Linear Regression: %f' %(score))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we will get into test data.\n",
    "\n",
    "data_test = pd.read_csv('test.csv')\n",
    "\n",
    "# Now, we will clean the ytest data(Data wrangle the test).\n",
    "data_test = CleanData(data_test,drop_passenger_id=False)\n",
    "\n",
    "# Convert it to numpy.\n",
    "testDataOnModel = data_test.values\n",
    "#data_test\n",
    "\n",
    "x_test = testDataOnModel[:,1:]\n",
    "\n",
    "#predict the survival values  for the test data\n",
    "y_test = lr.predict(x_test).astype(int)\n",
    "# In kaggle, it comes out to be 0.77990 on 50% of test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataFrame by combining index from the test data with the output of predictions, then write the results to the output:\n",
    "\n",
    "data_test['Survived']=y_test\n",
    "data_test[['PassengerId','Survived']].to_csv('result3.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lets Try Different Base Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression,LogisticRegressionCV\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To evaluate our model  we will be using 5-fold cross validation with the accuracy since its the metric that the competition uses\n",
    "# in the leaderboard.\n",
    "\n",
    "def ComputeScore(clf, X, y, scoring='accuracy'):\n",
    "    xval = cross_val_score(clf,X,y,cv=5,scoring=scoring)\n",
    "    xvalmean = np.mean(xval)\n",
    "    return xvalmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "logregCV = LogisticRegressionCV()\n",
    "rf = RandomForestClassifier()\n",
    "gboost = GradientBoostingClassifier()\n",
    "\n",
    "models = [logreg, logregCV, rf, gboost]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation of <class 'sklearn.linear_model.logistic.LogisticRegression'>\n",
      "CV Score = 0.822690\n",
      "---\n",
      "Cross-Validation of <class 'sklearn.linear_model.logistic.LogisticRegressionCV'>\n",
      "CV Score = 0.828314\n",
      "---\n",
      "Cross-Validation of <class 'sklearn.ensemble.forest.RandomForestClassifier'>\n",
      "CV Score = 0.803626\n",
      "---\n",
      "Cross-Validation of <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>\n",
      "CV Score = 0.830561\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "train_features = trainDataOnModel[:,1:]   # Excluding Survive column.\n",
    "train_target = trainDataOnModel[:,0]       # Only survive column.\n",
    "\n",
    "for model in models:\n",
    "    print('Cross-Validation of %s' %(model.__class__))\n",
    "    score = ComputeScore(clf=model,X=train_features,y=train_target,scoring='accuracy')\n",
    "    print('CV Score = %f' %(score))\n",
    "    print('---')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameters Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "run=False\n",
    "\n",
    "if run:\n",
    "    parameters_gid={'max_dept':[4,6,8],\n",
    "                   'n_estimators':[50,10],\n",
    "                    'max_features':['sqrt','auto','log'],\n",
    "                    'min_samples_split':[2,3,10],\n",
    "                    'min_samples_leaf':[1,2,10],\n",
    "                    'bootstrap':[True,False]\n",
    "                   }\n",
    "    \n",
    "    forest=RandomForestClassifier()\n",
    "    cross_validation=StratifiedKFold(n_splits=5)\n",
    "    \n",
    "    grid_search=GridSearch(forest,\n",
    "                       scoring='accuracy',\n",
    "                       param_grid=parameter_grid,\n",
    "                       cv=cross_validation,\n",
    "                       verbose=1\n",
    "                      )\n",
    "    \n",
    "    grid_search.fit(train_features,train_target)\n",
    "    model=grid_search\n",
    "    parameters=grid_search.best_params\n",
    "    \n",
    "    print('Best Score: %f' %(grid_search.best_score_))\n",
    "    print('Best Parameters: %f' %(parameters))\n",
    "\n",
    "    \n",
    "else:\n",
    "    \n",
    "    model = RandomForestClassifier(bootstrap= False, min_samples_leaf= 3, n_estimators= 50, \n",
    "                  min_samples_split= 10, max_features= 'sqrt', max_depth= 6)\n",
    "    model.fit(train_features,train_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we will get into test data.\n",
    "\n",
    "data_test = pd.read_csv('test.csv')\n",
    "\n",
    "# Now, we will clean the ytest data(Data wrangle the test).\n",
    "data_test = CleanData(data_test,drop_passenger_id=False)\n",
    "\n",
    "# Convert it to numpy.\n",
    "testDataOnModel = data_test.values\n",
    "#data_test\n",
    "\n",
    "x_test = testDataOnModel[:,1:]\n",
    "\n",
    "#predict the survival values  for the test data\n",
    "y_test = model.predict(x_test).astype(int)\n",
    "# In kaggle, it comes out to be 0.78468(RandomForestClassifier) on 50% of test data.\n",
    "# In kaggle, it comes out to be 0.76076(GradientBoostingClassifier) on 50% of test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataFrame by combining index from the test data with the output of predictions, then write the results to the output:\n",
    "\n",
    "data_test['Survived']=y_test\n",
    "data_test[['PassengerId','Survived']].to_csv('result4.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FROM SCRATCH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the training data.\n",
    "X = trainDataOnModel[:,1:]   # Excluding Survive column.\n",
    "y = trainDataOnModel[:,0]       # Only survive column.\n",
    "y=np.array([y]).T\n",
    "[m,n] = X.shape\n",
    "\n",
    "ones = np.ones((m,1))\n",
    "\n",
    "X = np.hstack((ones,X))\n",
    "theta = np.zeros((n+1,1))\n",
    "\n",
    "#print(theta.shape)         #(15, 1) \n",
    "#print(X.shape)             #(891, 15)\n",
    "\n",
    "#CostFunction(X,y,theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Predict(X, theta):\n",
    "    return Sigmoid(X.dot(theta)) >= 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Sigmoid(z):\n",
    "    g = (1 / ( 1 + np.exp(-z)))\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def CostFunction(theta,X,y):\n",
    "    \n",
    "    m=len(y)\n",
    "    g=np.zeros((m,1))\n",
    "    \n",
    "    h = X.dot(theta)\n",
    "    g = Sigmoid(h)\n",
    "    \n",
    "    #print(y.shape)\n",
    "    #print(g.shape)\n",
    "    \n",
    "    cost1 = -((y.T).dot(np.log(g)))\n",
    "    cost0 = -((1-y).T).dot(np.log(1-g))     \n",
    "    \n",
    "    J = sum(cost0+cost1) * (1/m)\n",
    "\n",
    "    temp = g-y\n",
    "    #print(temp.shape)\n",
    "    grad = (X.T.dot(temp) ) * (1/m)\n",
    "    #print(grad.shape)\n",
    "   \n",
    "    return J, grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "[J,grad]=CostFunction(theta,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost is: 0.693147\n",
      "[[  1.16161616e-01]\n",
      " [  4.05723906e-01]\n",
      " [ -2.47391105e+00]\n",
      " [  2.01459035e-01]\n",
      " [  1.16722783e-01]\n",
      " [ -1.01010101e-02]\n",
      " [  9.53984287e-03]\n",
      " [  3.76702020e+00]\n",
      " [  9.20314254e-02]\n",
      " [ -3.36700337e-03]\n",
      " [ -4.15263749e-02]\n",
      " [  1.99214366e-01]\n",
      " [ -4.20875421e-02]\n",
      " [  4.48933782e-03]\n",
      " [ -5.61167228e-04]]\n"
     ]
    }
   ],
   "source": [
    "print('Cost is: %f'%J)\n",
    "print(grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.438324\n",
      "         Iterations: 3833\n",
      "         Function evaluations: 5026\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "wrapping =lambda t : CostFunction(t, X, y)[0]\n",
    "result = minimize(wrapping, theta, method='Nelder-Mead', options= {'maxiter': 10000, 'disp': True})\n",
    "theta = result.x\n",
    "cost = result.fun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost is: 0.438324\n",
      "[ 0.13168509 -0.19008182  0.01383278  0.27795375  0.35999535  0.83885169\n",
      "  0.2436756  -0.00529385 -0.43343085  0.27583722  0.8264291  -2.23311216\n",
      "  1.7199767  -0.47305426 -0.88141334]\n"
     ]
    }
   ],
   "source": [
    "print('Cost is: %f' %cost)\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.8181818182\n"
     ]
    }
   ],
   "source": [
    "predictions = Predict(X, theta)\n",
    "accuracy = np.mean(predictions == y[:, 0]) * 100\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we will get into test data.\n",
    "\n",
    "data_test = pd.read_csv('test.csv')\n",
    "\n",
    "# Now, we will clean the ytest data(Data wrangle the test).\n",
    "data_test = CleanData(data_test,drop_passenger_id=False)\n",
    "\n",
    "# Convert it to numpy.\n",
    "testDataOnModel = data_test.values\n",
    "#data_test\n",
    "\n",
    "x_test = testDataOnModel[:,1:]\n",
    "\n",
    "[m,n] = x_test.shape\n",
    "ones = np.ones((m,1))\n",
    "x_test = np.hstack((ones,x_test))\n",
    "\n",
    "#predict the survival values  for the test data\n",
    "#y_test = lr.predict(x_test).astype(int)\n",
    "y_test = Predict(x_test, theta).astype(int)\n",
    "# In kaggle, it comes out to be 0.77033 on 50% of test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataFrame by combining index from the test data with the output of predictions, then write the results to the output:\n",
    "\n",
    "data_test['Survived']=y_test\n",
    "data_test[['PassengerId','Survived']].to_csv('result_scratch.csv',index=False)"
   ]
  }
 ],
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