flight_price_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ee93fee",
   "metadata": {},
   "source": [
    "# Flight Price Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1965aaf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799a1282",
   "metadata": {},
   "source": [
    "## Importing the dataset \n",
    "1. Since Data is in the form of excel file, we use pandas read_excel to load the data \n",
    "2. After loading, it's important that we check the complete information of the data as it is an indication of many hidden information, such as null values in a column or a row\n",
    "3. Check whether any null values are there or not. If there, following can be done,\n",
    "    - Inputing data using inputation method in sklearn\n",
    "    - Filling NaN values with mean, median and mode using the fillna() method \n",
    "4. Describe Data --> This can give us a statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2718cb1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-44ee5a8ceefa>:1: FutureWarning: Your version of xlrd is 1.2.0. In xlrd >= 2.0, only the xls format is supported. As a result, the openpyxl engine will be used if it is installed and the engine argument is not specified. Install openpyxl instead.\n",
      "  train_data = pd.read_excel(r\"Data_train.xlsx\")\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_excel(r\"Data_train.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdd584a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None) # display all the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f2f9b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cce11585",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10683 entries, 0 to 10682\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          10683 non-null  object\n",
      " 1   Date_of_Journey  10683 non-null  object\n",
      " 2   Source           10683 non-null  object\n",
      " 3   Destination      10683 non-null  object\n",
      " 4   Route            10682 non-null  object\n",
      " 5   Dep_Time         10683 non-null  object\n",
      " 6   Arrival_Time     10683 non-null  object\n",
      " 7   Duration         10683 non-null  object\n",
      " 8   Total_Stops      10682 non-null  object\n",
      " 9   Additional_Info  10683 non-null  object\n",
      " 10  Price            10683 non-null  int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 918.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info() # object data type = string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a093f4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 55m     337\n",
       "2h 45m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "40h 20m      1\n",
       "32h 55m      1\n",
       "31h 30m      1\n",
       "31h 50m      1\n",
       "33h 20m      1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2759ef73",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73d01ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 11)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b5b968b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f08bb05d",
   "metadata": {},
   "source": [
    "## EDA \n",
    "### Date and Month of Journey\n",
    "From the description, we can see that the Date_of_Journey is a object data type. Therefore, to convert this datatype to timestamp so as to use this column properly for prediction\n",
    "\n",
    "For this, we require pandas to_datetime to convert object data type to datetime dtype\n",
    "\n",
    "- .dt.day method will extract only day of that date \n",
    "- .dt.month method will extract only month of that date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d40fd06",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_day\"] = pd.to_datetime(train_data.Date_of_Journey, format = \"%d/%m/%Y\").dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "60481972",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data[\"Journey_month\"] = pd.to_datetime(train_data[\"Date_of_Journey\"], format = \"%d/%m/%Y\").dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7757a401",
   "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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>1/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
       "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
       "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
       "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
       "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
       "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
       "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
       "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
       "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
       "3    18:05         23:30   5h 25m      1 stop         No info   6218   \n",
       "4    16:50         21:35   4h 45m      1 stop         No info  13302   \n",
       "\n",
       "   Journey_day  Journey_month  \n",
       "0           24              3  \n",
       "1            1              5  \n",
       "2            9              6  \n",
       "3           12              5  \n",
       "4            1              3  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d714f9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we have converted Date_of_Journey column into integers,\n",
    "# We can drop it as of now, it's of no use\n",
    "\n",
    "train_data.drop([\"Date_of_Journey\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6f72d7",
   "metadata": {},
   "source": [
    "### Departure Time\n",
    "This is the time when the flight leaves the gate. Similar to Date_of_Journey we can extract values of Dep_Time\n",
    "We thereby extract the hours, the minutes from the given \"Dep_Time\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "806c339c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting hours \n",
    "train_data[\"Dep_hour\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.hour\n",
    "# Extracting minutes \n",
    "train_data[\"Dep_min\"] = pd.to_datetime(train_data[\"Dep_Time\"]).dt.minute \n",
    "\n",
    "# Now, we can drop Dep_Time as that is of no use\n",
    "train_data.drop([\"Dep_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "867d447b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route  Arrival_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL  01:10 22 Mar   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR         13:15   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK  04:25 10 Jun   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR         23:30   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL         21:35   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
       "0   2h 50m    non-stop         No info   3897           24              3   \n",
       "1   7h 25m     2 stops         No info   7662            1              5   \n",
       "2      19h     2 stops         No info  13882            9              6   \n",
       "3   5h 25m      1 stop         No info   6218           12              5   \n",
       "4   4h 45m      1 stop         No info  13302            1              3   \n",
       "\n",
       "   Dep_hour  Dep_min  \n",
       "0        22       20  \n",
       "1         5       50  \n",
       "2         9       25  \n",
       "3        18        5  \n",
       "4        16       50  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6dafd24",
   "metadata": {},
   "source": [
    "### Arrival Time\n",
    "This indicates when the flight pulls up to the gate \n",
    "Similar to Date_of_Journey we can extract values from Arrival_time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f299b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting hours \n",
    "train_data[\"Arrival_hour\"] = pd.to_datetime(train_data.Arrival_Time).dt.hour \n",
    "\n",
    "# Extracting minutes \n",
    "train_data[\"Arrival_min\"] = pd.to_datetime(train_data.Arrival_Time).dt.minute\n",
    "\n",
    "# Now, we can drop the Arrival_Time as it is of no use \n",
    "train_data.drop([\"Arrival_Time\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b1156ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hour</th>\n",
       "      <th>Arrival_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
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       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  \n",
       "0       20             1           10  \n",
       "1       50            13           15  \n",
       "2       25             4           25  \n",
       "3        5            23           30  \n",
       "4       50            21           35  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e09311b",
   "metadata": {},
   "source": [
    "### Time Taken\n",
    "Indicates the actual time required by a flight to reach from source to destination.\n",
    "It is the differnce between departure time and arrival time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4fde4509",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time taken by plane to reach destination is called Duration\n",
    "# It is the differnce betwwen Departure Time and Arrival time\n",
    "\n",
    "\n",
    "# Assigning and converting Duration column into list\n",
    "duration = list(train_data[\"Duration\"])\n",
    "\n",
    "for i in range(len(duration)):\n",
    "    if len(duration[i].split()) != 2:    # Check if duration contains only hour or mins\n",
    "        if \"h\" in duration[i]:\n",
    "            duration[i] = duration[i].strip() + \" 0m\"   # Adds 0 minute\n",
    "        else:\n",
    "            duration[i] = \"0h \" + duration[i]           # Adds 0 hour\n",
    "\n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration)):\n",
    "    duration_hours.append(int(duration[i].split(sep = \"h\")[0]))    # Extract hours from duration\n",
    "    duration_mins.append(int(duration[i].split(sep = \"m\")[0].split()[-1]))   # Extracts only minutes from duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "551d668a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding duration_hours and duration_mins lists to the train_data dataframe\n",
    "train_data[\"Duration_hours\"] = duration_hours\n",
    "train_data[\"Duration_mins\"]  = duration_mins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5b82fa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>IndiGo</td>\n",
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       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR   5h 25m   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL   4h 45m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  Journey_day  Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   3897           24              3        22   \n",
       "1     2 stops         No info   7662            1              5         5   \n",
       "2     2 stops         No info  13882            9              6         9   \n",
       "3      1 stop         No info   6218           12              5        18   \n",
       "4      1 stop         No info  13302            1              3        16   \n",
       "\n",
       "   Dep_min  Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0       20             1           10               2             50  \n",
       "1       50            13           15               7             25  \n",
       "2       25             4           25              19              0  \n",
       "3        5            23           30               5             25  \n",
       "4       50            21           35               4             45  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e046c849",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"Duration\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01470662",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Total_Stops  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR      1 stop   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL      1 stop   \n",
       "\n",
       "  Additional_Info  Price  Journey_day  Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   3897           24              3        22       20   \n",
       "1         No info   7662            1              5         5       50   \n",
       "2         No info  13882            9              6         9       25   \n",
       "3         No info   6218           12              5        18        5   \n",
       "4         No info  13302            1              3        16       50   \n",
       "\n",
       "   Arrival_hour  Arrival_min  Duration_hours  Duration_mins  \n",
       "0             1           10               2             50  \n",
       "1            13           15               7             25  \n",
       "2             4           25              19              0  \n",
       "3            23           30               5             25  \n",
       "4            21           35               4             45  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaec28fb",
   "metadata": {},
   "source": [
    "### Handling Categorical Data \n",
    "There can be many ways to handle categorical data. Some of the categorical data are,\n",
    "1. **Nominal Data** : Data is not in any order --> OneHotEncoder is used in this case\n",
    "2. **Ordinal Data** : Data is in order --> LabelEncoder is used in this case"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6dead0b",
   "metadata": {},
   "source": [
    "### The Airlines\n",
    "This is a categorical data, we can use OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b7fb4ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3849\n",
       "IndiGo                               2053\n",
       "Air India                            1751\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              818\n",
       "Vistara                               479\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Airline.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a0e9c280",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From graph, we can see tht Jet Airways Business has the highest price.\n",
    "# Apart from the first Airline almost all are having similar median\n",
    "\n",
    "sns.catplot(y = \"Price\", x = \"Airline\", data = train_data.sort_values(\"Price\", ascending = False), kind = \"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d18cabda",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00c379c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5eb94270",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37a92283",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91ca74b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}