Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    }
   ],
   "source": [
    "##For the data visualisation as centerd plots\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "</style>\n",
    "\"\"\");\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')#Applying warning filter that if warning comes then ignore\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "\n",
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns#For data visualisation\n",
    "\n",
    "import pylab as plot\n",
    "\n",
    "params = {\n",
    "    'axes.labelsize' : \"large\",\n",
    "    'xtick.labelsize': \"x-large\",\n",
    "    'legend.fontsize': 21,\n",
    "    'figure.dpi' : 150,\n",
    "    'figure.figsize' : [25,7]\n",
    "}\n",
    "plot.rcParams.update(params)\n",
    "\n",
    "#loading the training data set\n",
    "\n",
    "data = pd.read_csv('/Users/adityamishra/data/train.csv')# . signifies the current directory\n",
    "print(data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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",
       "    </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": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In Survived coumn if value is 0 means passenger died else survived.\n",
    "\n",
    "1. Pclass: Passenger class\n",
    "2. SibSp: number of siblings or spouse\n",
    "3. Parch: number of parents and children traveling with the passenger\n",
    "4. Embarked: Areas where passenger can accomodate, these are divided into three parts S,C,Q."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>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": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we can see that 177 values are missing from the age column.\n",
    "\n",
    "The solution to fill compensate those missing values is to fill in the values with the median age.\n",
    "We can also represent it with the mean age but the median is better to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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>891.000000</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>29.361582</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>13.019697</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>22.000000</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>35.000000</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  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.361582    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.019697    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    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": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'] = data['Age'].fillna(data['Age'].median())#fillna fills the NaN values with a given number with which we want to substitute\n",
    "#Checking the result\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here we can see that the value in AGE column is rectified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#By the simple formula of probablity we have read that P(Died) = 1-P(Survived), similarly\n",
    "#taking note from the above formula\n",
    "data['Died'] = 1 - data['Survived']\n",
    "\n",
    "#Here we are grouping the dataframe and aggregating the sum of who survived and dies by\n",
    "#based on the gender\n",
    "data.groupby('Sex').agg('sum')[['Survived', 'Died']].plot(kind = 'bar', figsize=(25,7),\n",
    "                                                         stacked = True, colors=['g','r']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here, we can see that the males are most likely to die than women\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the same graph with the ratio.\n",
    "data.groupby('Sex').agg('mean')[['Survived', 'Died']].plot(kind='bar', figsize=(25,7),\n",
    "                                                          stacked = True, colors=['g','r']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here it's again visualised that women are most likely to survive as we remember the scene from the movie Titanic where it is said that \"Women and Children first\" so connecting the dots from that scene females survived more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlating the survival with the age variable\n",
    "fig = plt.figure(figsize=(25,7))\n",
    "sns.violinplot(x='Sex', y='Age',\n",
    "              hue='Survived', data=data,\n",
    "              split=True,\n",
    "              palette={0:\"r\", 1:'g'});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Here we can see that men of young age survived more.\n",
    "2. Male passenger from age 20 to 40 died more.\n",
    "3. Age doesn't played much role in the survival of women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Survival based on the ticket of the passenger\n",
    "figure = plt.figure(figsize=(25,7))\n",
    "plt.hist([data[data['Survived'] == 1]['Fare'], data[data['Survived'] ==0]['Fare']],\n",
    "        stacked =True, color = ['g', 'r'],\n",
    "        bins=50, label = ['Survived', 'Dead'])\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Number of passengers')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here, we can see that the passengers with cheap ticket price are more likely to die and the important socio-economic figures were rescured first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Combining the fare, age and survival in the single chart\n",
    "plt.figure(figsize=(25,7))\n",
    "ax=plt.subplot()\n",
    "\n",
    "ax.scatter(data[data['Survived'] == 1]['Age'], data[data['Survived'] == 1]['Fare'],\n",
    "          c='green', s=data[data['Survived'] == 1] ['Fare'])\n",
    "ax.scatter(data[data['Survived'] == 0]['Age'], data[data['Survived'] == 0]['Fare'],\n",
    "          c='red', s=data[data['Survived'] == 0] ['Fare']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we can observe following things\n",
    "1. Adults between age 20 to 45 which is represented on z axis were having largest ticket fares.\n",
    "2. Small red dots between age 10 to 45 shows adults from lower classes on the boat\n",
    "3. Small green dots from 0 to 7 on x axis shows theese are children that were saved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlating the fare with the Passenger class that is 1st, 2nd and 3rd\n",
    "ax = plot.subplot()\n",
    "ax.set_ylabel('Average fare')\n",
    "data.groupby('Pclass').mean()['Fare'].plot(kind = 'bar', figsize=(25,7), ax = ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3750x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accommodation area effecting the survival\n",
    "fig = plt.figure(figsize=(25,7))\n",
    "sns.violinplot(x='Embarked', y='Fare', hue='Survived', data=data,\n",
    "              split=True, palette={0:\"r\", 1:\"g\"});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we can observe following things\n",
    "1. In class C, passengers who payed higher price survived.\n",
    "2. Less people died in Q class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering\n",
    "\n",
    "Here in this part, we will learn how to process and transform these variables in such a way the data can be easily manageable by a ML algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#printing a function that states where a feature has been processed or not\n",
    "def status(feature):\n",
    "    print('Processing', feature, ' :ok')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the data\n",
    "\n",
    "The trick while starting a ML problem is to append the training set to the test set together.\n",
    "Here, we will build new features using the train set to prevent from information leakage.\n",
    "After that the next step will be to add these variables to the test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the train and test sets and appending them together\n",
    "def get_combined_data():\n",
    "    #reading train data\n",
    "    train = pd.read_csv('/Users/adityamishra/data/train.csv')\n",
    "    \n",
    "    #reading test data\n",
    "    test = pd.read_csv('/Users/adityamishra/data/test.csv')\n",
    "    \n",
    "    #extracting and then removing the targets from the training data\n",
    "    targets = train.Survived\n",
    "    train.drop(['Survived'], 1, inplace = True)\n",
    "    #Here in train.drop() we remove rows or columns by specifying label names and corresponding names\n",
    "    #or by specifying directly index or column names.\n",
    "    #inplace take default value as false and if True then do operation and return None\n",
    "    \n",
    "    #Here, we'll also remove the PassengerID since this is not an informative feature\n",
    "    combined = train.append(test)\n",
    "    combined.reset_index(inplace = True)\n",
    "    combined.drop(['index', 'PassengerId'], inplace = True, axis=1)\n",
    "    \n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 10)\n"
     ]
    }
   ],
   "source": [
    "combined = get_combined_data()\n",
    "print(combined.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here, we can see that train and test sets are combined\n",
    "\n",
    "The total number of rows(1309) is the exact summation of the number of rows in the train set and the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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>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>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>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>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>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>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": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1      1      0          PC 17599  71.2833   C85        C  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      1      0            113803  53.1000  C123        S  \n",
       "4      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()#Returning the 5 default rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the passenger titles\n",
    "\n",
    "Here we will extract the title of the passengers in the ship like Master, Sir or Dona.\n",
    "Here we may introduce an additional information about the social status by simply parsing the name and extracting the title and converting to a binary variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Different titles in the train set\n",
    "titles = set()\n",
    "for name in data['Name']:\n",
    "    titles.add(name.split(', ')[1].split('.')[0].strip())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Rev', 'Major', 'Lady', 'Jonkheer', 'Capt', 'Dr', 'Master', 'Mrs', 'Ms', 'Sir', 'Mme', 'Don', 'Col', 'the Countess', 'Mlle', 'Mr', 'Miss'}\n"
     ]
    }
   ],
   "source": [
    "print(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\n",
    "def get_titles():\n",
    "    #Extracting the title from each name\n",
    "    combined['Title'] = combined['Name'].map(lambda name:name.split(', ')[1].split('. ')[0].strip())\n",
    "    \n",
    "    #a map of more aggregated title and we map each title\n",
    "    combined['Title'] = combined.Title.map(Title_Dictionary)\n",
    "    status('Title')\n",
    "    return combined\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here function get_titles extracts the titles from the name. Then, it maps the titles to categories of titles. We selected:\n",
    "1. Officer\n",
    "2. Royalty\n",
    "3. Mr\n",
    "4. Mrs\n",
    "5. Miss\n",
    "6. Master\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Title  :ok\n"
     ]
    }
   ],
   "source": [
    "combined = get_titles()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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>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>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch            Ticket     Fare Cabin Embarked Title  \n",
       "0      1      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1      1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2      0      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3      1      0            113803  53.1000  C123        S   Mrs  \n",
       "4      0      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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>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>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pclass                          Name     Sex   Age  SibSp  Parch  \\\n",
       "1305       1  Oliva y Ocana, Dona. Fermina  female  39.0      0      0   \n",
       "\n",
       "        Ticket   Fare Cabin Embarked Title  \n",
       "1305  PC 17758  108.9  C105        C   NaN  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined['Title'].isnull()]#Cehcking if the titles have been filled correctly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is indeed a NaN value in the line 1305. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Processing the ages\n",
    "\n",
    "In the 1st part the Age column was missing 177 values. There we replaced the values with the median age but it can't be the best solution since the age may differ by the gender or categories of passengers.\n",
    "To avoid data leakage from the test set, we fill in missing ages in the train using the train set and we fill in ages im the test set using values calculated from the train set as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177\n"
     ]
    }
   ],
   "source": [
    "#Number of missing ages in train set\n",
    "print (combined.iloc[:891].Age.isnull().sum())#Here by this syntax we are doing data selection\n",
    "                                            #where iloc is used to do the selection row wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n"
     ]
    }
   ],
   "source": [
    "#Numbers of missing ages in the test set\n",
    "print (combined.iloc[891:].Age.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_train = combined.iloc[:891].groupby(['Sex','Pclass','Title'])\n",
    "grouped_median_train = grouped_train.median()\n",
    "grouped_median_train = grouped_median_train.reset_index()[['Sex', 'Pclass', 'Title', 'Age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "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>Title</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Officer</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>female</td>\n",
       "      <td>2</td>\n",
       "      <td>Miss</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Pclass    Title   Age\n",
       "0  female       1     Miss  30.0\n",
       "1  female       1      Mrs  40.0\n",
       "2  female       1  Officer  49.0\n",
       "3  female       1  Royalty  40.5\n",
       "4  female       2     Miss  24.0"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_median_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This dataframe will help us represent missing age values based on different criteria.\n",
    "#### Here we can see that the median age value can be different based on the Sex, Pclass and Title put together\n",
    "\n",
    "For example:\n",
    "1. If the passenger is female, from Pclass 1 and from Royalty then the median age is 40.5\n",
    "2. If the passenger is male, from Pclass 3, with a Mr title, the median age is 26."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a function that fills in the missing age in combined based on these different attributes.\n",
    "def fill_age(row):\n",
    "    condition = (\n",
    "        (grouped_median_train['Sex'] == row['Sex']) & \n",
    "        (grouped_median_train['Title'] == row['Title']) & \n",
    "        (grouped_median_train['Pclass'] == row['Pclass'])\n",
    "    ) \n",
    "    return grouped_median_train[condition]['Age'].values[0]\n",
    "\n",
    "\n",
    "def process_age():\n",
    "    global combined\n",
    "    # a function that fills the missing values of the Age variable\n",
    "    combined['Age'] = combined.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)\n",
    "    status('age')\n",
    "    return combined\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing age  :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_age()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, we can see that the missing ages have been replaced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Processing names\n",
    "def process_names():\n",
    "    global combined\n",
    "    #cleaning the name variable\n",
    "    combined.drop('Name', axis=1, inplace = True)\n",
    "    \n",
    "    #encoding in dummy variable\n",
    "    titles_dummies = pd.get_dummies(combined['Title'], prefix='Title')\n",
    "    combined = pd.concat([combined, titles_dummies], axis=1)\n",
    "    \n",
    "    #removing the title variable\n",
    "    combined.drop('Title', axis=1, inplace = True)\n",
    "    \n",
    "    status('names')\n",
    "    return combined\n",
    "#here we dropped the NAME column since wr won't be using it anymore because we created a title column\n",
    "\n",
    "#Then we encoded the title values using dummy encoding by using pands.get_dummies\n",
    "#By this we can convert the categorical value into the dummy/indicator variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing names  :ok\n"
     ]
    }
   ],
   "source": [
    "combined = process_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Pclass</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>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>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>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>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>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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</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>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": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "3       1  female  35.0      1      0            113803  53.1000  C123   \n",
       "4       3    male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "  Embarked  Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0        S             0           0         1          0              0   \n",
       "1        C             0           0         0          1              0   \n",
       "2        S             0           1         0          0              0   \n",
       "3        S             0           0         0          1              0   \n",
       "4        S             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here we can see that\n",
    "1. There is no longer a name feature\n",
    "2. New variables (Ttile_X) appeared. These features are binary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Processing the missing fare value by the average fare computed on the train set\n",
    "def process_fares():\n",
    "    global combined\n",
    "    #There is one missing fare value and we are replacing it with the mean\n",
    "    combined.Fare.fillna(combined.iloc[:891].Fare.mean(), inplace = True)\n",
    "    status('fare')\n",
    "    return combined\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing fare  :ok\n"
     ]
    }
   ],
   "source": [
    "#The above function simply replaces one missing Fare value by the mean\n",
    "combined = process_fares()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_embarked():\n",
    "    global combined\n",
    "    #Two missing embarked values, we will filling them with the most frequent one in the train set(S)\n",
    "    combined.Embarked.fillna('S', inplace = True)\n",
    "    #dummy encoding\n",
    "    embarked_dummies = pd.get_dummies(combined['Embarked'], prefix='Embarked')\n",
    "    combined = pd.concat([combined, embarked_dummies], axis=1)\n",
    "    combined.drop('Embarked', axis=1, inplace = True)\n",
    "    status('embarked')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing embarked  :ok\n"
     ]
    }
   ],
   "source": [
    "#above function replaces the two missing values of Embarked with the most frequent embarked value\n",
    "combined = process_embarked()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Parch</th>\n",
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       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</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",
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       "      <th>2</th>\n",
       "      <td>3</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",
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       "      <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>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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</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>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare Cabin  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   NaN   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   C85   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN   \n",
       "3       1  female  35.0      1      0            113803  53.1000  C123   \n",
       "4       3    male  35.0      0      0            373450   8.0500   NaN   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0             0           0         1          0              0   \n",
       "1             0           0         0          1              0   \n",
       "2             0           1         0          0              0   \n",
       "3             0           0         0          1              0   \n",
       "4             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0              0           0           0           1  \n",
       "1              0           1           0           0  \n",
       "2              0           0           0           1  \n",
       "3              0           0           0           1  \n",
       "4              0           0           0           1  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Cabin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cabin, test_cabin = set(), set()\n",
    "\n",
    "for c in combined.iloc[:891]['Cabin']:\n",
    "    try:\n",
    "        train_cabin.add(c[0])\n",
    "    except:\n",
    "        train_cabin.add('U')\n",
    "\n",
    "for c in combined.iloc[891:]['Cabin']:\n",
    "    try:\n",
    "        test_cabin.add(c[0])\n",
    "    except:\n",
    "        test_cabin.add('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'E', 'D', 'G', 'F', 'A', 'T', 'B', 'C', 'U'}\n"
     ]
    }
   ],
   "source": [
    "print(train_cabin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'E', 'D', 'G', 'F', 'A', 'B', 'C', 'U'}\n"
     ]
    }
   ],
   "source": [
    "print(test_cabin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we can see that we don't have any cabin letter in the test set that is not present in the train set\n",
    "def process_cabin():\n",
    "    global combined\n",
    "    #replacing missing cabins with U stands for unknown\n",
    "    combined.Cabin.fillna('U', inplace=True)\n",
    "    \n",
    "    #mapping each Cabin value with the cabin letter\n",
    "    combined['Cabin'] = combined['Cabin'].map(lambda c: c[0])\n",
    "    \n",
    "    #dummy encoding\n",
    "    cabin_dummies = pd.get_dummies(combined['Cabin'], prefix='Cabin')\n",
    "    combined = pd.concat([combined, cabin_dummies], axis=1)\n",
    "    \n",
    "    combined.drop('Cabin', axis=1, inplace = True)\n",
    "    status('cabin')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing cabin  :ok\n"
     ]
    }
   ],
   "source": [
    "#The above function replaces NaN values with U.\n",
    "#It then maps each Cabin value to the first letter then it encodes the cabin values using dummy encoding again.\n",
    "combined = process_cabin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>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>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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</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>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3       1  female  35.0      1      0            113803  53.1000   \n",
       "4       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Officer  \\\n",
       "0             0           0         1          0              0   \n",
       "1             0           0         0          1              0   \n",
       "2             0           1         0          0              0   \n",
       "3             0           0         0          1              0   \n",
       "4             0           0         1          0              0   \n",
       "\n",
       "   Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  Cabin_A  Cabin_B  \\\n",
       "0              0           0           0           1        0        0   \n",
       "1              0           1           0           0        0        0   \n",
       "2              0           0           0           1        0        0   \n",
       "3              0           0           0           1        0        0   \n",
       "4              0           0           0           1        0        0   \n",
       "\n",
       "   Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_U  \n",
       "0        0        0        0        0        0        0        1  \n",
       "1        1        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        0        1  \n",
       "3        1        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        0        1  "
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, no missing values therefore\n",
    "combined.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Sex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_sex():\n",
    "    global combined\n",
    "    #mapping the string values to numerical one\n",
    "    combined['Sex'] = combined['Sex'].map({'male':1, 'female':0})\n",
    "    status('Sex')\n",
    "    return combined\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Sex  :ok\n"
     ]
    }
   ],
   "source": [
    "#Above function maps the string values male and female to 1 and 0 respectively\n",
    "combined = process_sex()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing PClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_pclass():\n",
    "    global combined\n",
    "    #encoding into 3 categories\n",
    "    pclass_dummies = pd.get_dummies(combined['Pclass'], prefix=\"Pclass\")\n",
    "    \n",
    "    #adding dummy variable\n",
    "    combined = pd.concat([combined, pclass_dummies], axis=1)\n",
    "    \n",
    "    #removing Pclass\n",
    "    combined.drop('Pclass', axis=1, inplace=True)\n",
    "    \n",
    "    status('Pclass')\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Pclass  :ok\n"
     ]
    }
   ],
   "source": [
    "#The above function encodes the values of Pclass(1,2,3) using a dummy encoding\n",
    "combined = process_pclass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many different ticket prefixes are in our dataset\n",
    "def cleanTicket(ticket):\n",
    "    ticket = ticket.replace('.', '')\n",
    "    ticket = ticket.replace('/', '')\n",
    "    ticket = ticket.split()\n",
    "    ticket = map(lambda t : t.strip(), ticket)\n",
    "    ticket = list(filter(lambda t : not t.isdigit(), ticket))\n",
    "    if len(\"ticket\") > 0:\n",
    "        return ticket[0]\n",
    "    else: \n",
    "        return 'XXX'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickets = set()\n",
    "for t in combined['Ticket']:\n",
    "    tickets.add(cleanTicket(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    }
   ],
   "source": [
    "print(len(tickets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'filter' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-272-dd3516d7b65a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcombined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess_ticket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-271-31fb7d7c3758>\u001b[0m in \u001b[0;36mprocess_ticket\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;31m# Extracting dummy variables from tickets:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m     \u001b[0mcombined\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcombined\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcleanTicket\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m     \u001b[0mtickets_dummies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombined\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mcombined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcombined\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtickets_dummies\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   2994\u001b[0m         \"\"\"\n\u001b[1;32m   2995\u001b[0m         new_values = super(Series, self)._map_values(\n\u001b[0;32m-> 2996\u001b[0;31m             arg, na_action=na_action)\n\u001b[0m\u001b[1;32m   2997\u001b[0m         return self._constructor(new_values,\n\u001b[1;32m   2998\u001b[0m                                  index=self.index).__finalize__(self)\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m   1002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1003\u001b[0m         \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-271-31fb7d7c3758>\u001b[0m in \u001b[0;36mcleanTicket\u001b[0;34m(ticket)\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mticket\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdigit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticket\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ticket\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mticket\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m'XXX'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'filter' object is not subscriptable"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Families\n",
    "\n",
    "This part includes creating new variables based on the size of the family.\n",
    "\n",
    "The creation of new variables is done under a realistic assumption: Large families are grouped together, hence they are more likely to get rescued than people travelling alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using 5-fold cross validation\n",
    "def compute_score(clf, X, y, scoring='accuracy'):\n",
    "    xval = cross_val_score(clf, X, y, cv = 5, scoring=scoring)\n",
    "    return np.mean(xval)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Recovering the train set and the test set from the combined dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recover_train_test_target():\n",
    "    global combined\n",
    "    \n",
    "    targets = pd.read_csv('/Users/adityamishra/data/train.csv', usecols=['Survived'])['Survived'].values\n",
    "    train = combined.iloc[:891]\n",
    "    test = combined.iloc[891:]\n",
    "    \n",
    "    return train, test, targets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test, targets = recover_train_test_target()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'W./C. 6607'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-267-412038ba185f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sqrt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \"\"\"\n\u001b[1;32m    246\u001b[0m         \u001b[0;31m# Validate or convert input data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    431\u001b[0m                                       force_all_finite)\n\u001b[1;32m    432\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'W./C. 6607'"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')\n",
    "clf = clf.fit(train,targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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