flight_fare_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o0SaHJdPMtec"
   },
   "source": [
    "# ML Flight Fare Prediction (Kaggle Problem Statement)\n",
    "\n",
    "### In this notebook, I have made an attempt to develop a model to solve the Kaggle Problem Statement - Flight Fare Prediction using ML Regression. Here, I have implemented various ML regression models and finally decided upon Random Forest Regressor for predictions.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N7TXIg-u4SG9"
   },
   "source": [
    "# Importing the libraries and the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ng_ZlD_CKvYP"
   },
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "3M48BKJYKvYY",
    "outputId": "205ac6bf-59fb-4240-9b6a-91df4a5e34e3"
   },
   "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",
       "    </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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data is in excel format so, read data as 'read_excel'\n",
    "train = pd.read_excel('Data_Train.xlsx')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "7uTRRkYGKvYm",
    "outputId": "c2f7a764-6cde-4386-9c3b-68434b1d2680"
   },
   "outputs": [
    {
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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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>17:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>10h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>12/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → MAA → BLR</td>\n",
       "      <td>06:20</td>\n",
       "      <td>10:20</td>\n",
       "      <td>4h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>19:15</td>\n",
       "      <td>19:00 22 May</td>\n",
       "      <td>23h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>08:00</td>\n",
       "      <td>21:00</td>\n",
       "      <td>13h</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>24/06/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>23:55</td>\n",
       "      <td>02:45 25 Jun</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
       "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
       "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
       "1    06:20         10:20       4h      1 stop                      No info  \n",
       "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
       "3    08:00         21:00      13h      1 stop                      No info  \n",
       "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_excel('Test_set.xlsx')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "evbJpmZ7KvY3",
    "outputId": "0dfe3a03-d87b-45c5-a771-73c647583416"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset shape: (10683, 11)\n",
      "Test dataset shape: (2671, 10)\n"
     ]
    }
   ],
   "source": [
    "print('Training dataset shape:', train.shape)\n",
    "print('Test dataset shape:', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4nE2Cs8xKvZC"
   },
   "source": [
    "# Checking if there are any missing values in Training and Test Dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "qLQLxH6iKvZD",
    "outputId": "252a1a2c-a239-45b7-f07e-83378f9dd031"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "colab_type": "code",
    "id": "sjxm0Hd2KvZL",
    "outputId": "2f1bcccd-7535-4372-96d4-9cebecbc1fd0"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "    }\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>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09:45</td>\n",
       "      <td>09:25 07 May</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
       "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
       "\n",
       "      Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "9039  09:25 07 May  23h 40m         NaN         No info   7480  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Total_Stops'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "colab_type": "code",
    "id": "zh0G6ZDYNIlX",
    "outputId": "a1630a90-d2cc-420b-eae7-286b0f7f2e79"
   },
   "outputs": [
    {
     "data": {
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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>9039</th>\n",
       "      <td>Air India</td>\n",
       "      <td>6/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09:45</td>\n",
       "      <td>09:25 07 May</td>\n",
       "      <td>23h 40m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No info</td>\n",
       "      <td>7480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
       "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
       "\n",
       "      Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "9039  09:25 07 May  23h 40m         NaN         No info   7480  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train['Route'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EsF8SAtFKvZV"
   },
   "outputs": [],
   "source": [
    "train=train.dropna(axis=0, how='any')\n",
    "#since, there is only one missing value in Total_Stops and Route and both coincidentally are from same record, we can just drop that record/row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "_aUvPbA2KvZc",
    "outputId": "bd9a4a26-c593-4642-8399-d0b9e9775345"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10682, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "iUF1o4VzKvZi",
    "outputId": "7d58001c-99eb-4388-e6c1-171fa218da73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            object\n",
       "Date_of_Journey    object\n",
       "Source             object\n",
       "Destination        object\n",
       "Route              object\n",
       "Dep_Time           object\n",
       "Arrival_Time       object\n",
       "Duration           object\n",
       "Total_Stops        object\n",
       "Additional_Info    object\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "n1_nmL0_KvZn",
    "outputId": "f3bbe535-fb4d-4775-cc76-b153c8966365"
   },
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ykNCLXzqKvZv"
   },
   "source": [
    "No missing values in Test dataset, and the missing value row in Training dataset has been removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IkYqPk9WKvZy"
   },
   "source": [
    "# Feature Engineering on Training Set "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2_Pci1hjKvZ0"
   },
   "source": [
    "Looking at the dataset, there are 4 columns with date-time variables and we need to apply featrure engineering to those columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "gEGd22lFKvZ4",
    "outputId": "059cbeaf-5bbf-4d27-dc25-9460c8877c67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     550\n",
       "1h 30m     386\n",
       "2h 45m     337\n",
       "2h 55m     337\n",
       "2h 35m     329\n",
       "          ... \n",
       "30h 25m      1\n",
       "30h 15m      1\n",
       "42h 45m      1\n",
       "29h 40m      1\n",
       "42h 5m       1\n",
       "Name: Duration, Length: 368, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First we consider Duration column\n",
    "train[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "LTDkyeXFKvaC",
    "outputId": "bf264a2c-f6d1-41b3-81f5-3f053d2028ff"
   },
   "outputs": [
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       " ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting 'Duration' column into a list\n",
    "duration_train = list(train[\"Duration\"])\n",
    "duration_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bjODXdBTKvaT"
   },
   "outputs": [],
   "source": [
    "#apply loop to separate hours from minutes\n",
    "#.split() method splits a string into a list\n",
    "#also, hour is represented by 'h' and minute by'm'\n",
    "#.strip() method returns a copy of the string by removing both the leading and the trailing characters\n",
    "\n",
    "for i in range(len(duration_train)):\n",
    "    if len(duration_train[i].split()) != 2:   \n",
    "        if \"h\" in duration_train[i]:\n",
    "            duration_train[i] = duration_train[i].strip() + ' 0m'  # add 0 minute \n",
    "        else:\n",
    "            duration_train[i] = '0h '+ duration_train[i]           # add 0 hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "RNg9Stv1Kvcg",
    "outputId": "14b1d434-550d-4651-9e66-6cbf48efff71"
   },
   "outputs": [
    {
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       " '20h 20m',\n",
       " '28h 5m',\n",
       " '15h 25m',\n",
       " '12h 5m',\n",
       " '10h 20m',\n",
       " '12h 25m',\n",
       " '7h 35m',\n",
       " '2h 50m',\n",
       " '4h 35m',\n",
       " '11h 15m',\n",
       " '2h 35m',\n",
       " '26h 0m',\n",
       " '2h 25m',\n",
       " '2h 35m',\n",
       " '14h 15m',\n",
       " '2h 35m',\n",
       " '22h 5m',\n",
       " '6h 35m',\n",
       " '8h 30m',\n",
       " '18h 45m',\n",
       " '35h 15m',\n",
       " '2h 15m',\n",
       " '2h 30m',\n",
       " '2h 20m',\n",
       " '2h 30m',\n",
       " '7h 35m',\n",
       " '35h 35m',\n",
       " '26h 30m',\n",
       " '11h 30m',\n",
       " '26h 40m',\n",
       " '3h 0m',\n",
       " '1h 30m',\n",
       " '2h 55m',\n",
       " '14h 15m',\n",
       " '16h 55m',\n",
       " '12h 20m',\n",
       " '29h 35m',\n",
       " '20h 20m',\n",
       " '8h 25m',\n",
       " '13h 50m',\n",
       " '10h 0m',\n",
       " '19h 25m',\n",
       " '28h 0m',\n",
       " '2h 50m',\n",
       " '17h 20m',\n",
       " '2h 45m',\n",
       " '2h 55m',\n",
       " '13h 25m',\n",
       " '12h 55m',\n",
       " '9h 45m',\n",
       " '21h 25m',\n",
       " '15h 40m',\n",
       " '2h 20m',\n",
       " '14h 25m',\n",
       " '2h 50m',\n",
       " '6h 35m',\n",
       " '22h 10m',\n",
       " '14h 25m',\n",
       " '2h 30m',\n",
       " '23h 35m',\n",
       " '13h 5m',\n",
       " '1h 30m',\n",
       " '5h 5m',\n",
       " '1h 20m',\n",
       " '22h 55m',\n",
       " '1h 30m',\n",
       " '2h 35m',\n",
       " '37h 20m',\n",
       " '2h 50m',\n",
       " '2h 50m',\n",
       " '36h 10m',\n",
       " '9h 15m',\n",
       " '1h 25m',\n",
       " '5h 5m',\n",
       " '2h 15m',\n",
       " '15h 10m',\n",
       " '25h 55m',\n",
       " '5h 40m',\n",
       " '7h 30m',\n",
       " '17h 45m',\n",
       " '2h 20m',\n",
       " '8h 15m',\n",
       " '3h 40m',\n",
       " '5h 55m',\n",
       " '2h 30m',\n",
       " '17h 30m',\n",
       " '13h 50m',\n",
       " '2h 15m',\n",
       " '21h 35m',\n",
       " '9h 30m',\n",
       " '8h 40m',\n",
       " '10h 25m',\n",
       " '35h 5m',\n",
       " '14h 15m',\n",
       " '3h 0m',\n",
       " '25h 50m',\n",
       " '2h 30m',\n",
       " '8h 40m',\n",
       " '10h 0m',\n",
       " '11h 30m',\n",
       " '7h 30m',\n",
       " ...]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yna9mrT6Kvdf"
   },
   "outputs": [],
   "source": [
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration_train)):\n",
    "    duration_hours.append(int(duration_train[i].split(sep = \"h\")[0]))\n",
    "    duration_mins.append(int(duration_train[i].split(sep = \"m\")[0].split()[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "Q0XXtQrFKveS",
    "outputId": "abedbc96-82ab-4322-d1c8-468bd492e9fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2\n",
       "1         7\n",
       "2        19\n",
       "3         5\n",
       "4         4\n",
       "         ..\n",
       "10678     2\n",
       "10679     2\n",
       "10680     3\n",
       "10681     2\n",
       "10682     8\n",
       "Name: Duration_hrs, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Duration_hrs'] = duration_hours\n",
    "train['Duration_hrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "ZtSY8pknKvei",
    "outputId": "daaa9bbc-efee-4073-aa40-d8409b7ba25e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        50\n",
       "1        25\n",
       "2         0\n",
       "3        25\n",
       "4        45\n",
       "         ..\n",
       "10678    30\n",
       "10679    35\n",
       "10680     0\n",
       "10681    40\n",
       "10682    20\n",
       "Name: Duration_mins, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Duration_mins'] = duration_mins\n",
    "train['Duration_mins']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZsGY33UbKvev"
   },
   "outputs": [],
   "source": [
    "train.drop('Duration', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OLaXb73VKve7"
   },
   "source": [
    "Now, we consider 'Date_of_Journey', 'Dep_Time',\t& 'Arrival_Time'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2KDSfWuOKvfB"
   },
   "outputs": [],
   "source": [
    "#first we consider 'Date_of_Journey'\n",
    "train['Day_of_Journey']=pd.to_datetime(train['Date_of_Journey'], format='%d/%m/%Y').dt.day\n",
    "train['Month_of_Journey']=pd.to_datetime(train['Date_of_Journey'], format='%d/%m/%Y').dt.month\n",
    "train.drop('Date_of_Journey', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6UiL4IEBKvfN"
   },
   "outputs": [],
   "source": [
    "# Now, we need to take care of Dep_Time\n",
    "train['Dep_hr'] = pd.to_datetime(train['Dep_Time']).dt.hour\n",
    "train['Dep_min'] = pd.to_datetime(train['Dep_Time']).dt.minute\n",
    "train.drop('Dep_Time', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OBYhDLK6Kvfw"
   },
   "outputs": [],
   "source": [
    "#Now, we take care of Arrival_Time\n",
    "train['Arrival_hr'] = pd.to_datetime(train['Arrival_Time']).dt.hour\n",
    "train['Arrival_min'] = pd.to_datetime(train['Arrival_Time']).dt.minute\n",
    "train.drop('Arrival_Time', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ERWbginyKvgh",
    "outputId": "eccfe2a5-ea6b-46a4-956f-45d97573c099"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset shape: (10682, 15)\n"
     ]
    }
   ],
   "source": [
    "print('Train dataset shape:', train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Duration_hrs</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Day_of_Journey</th>\n",
       "      <th>Month_of_Journey</th>\n",
       "      <th>Dep_hr</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hr</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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <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>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>7</td>\n",
       "      <td>25</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>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>19</td>\n",
       "      <td>0</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>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>25</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>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>4</td>\n",
       "      <td>45</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 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  Duration_hrs  Duration_mins  Day_of_Journey  \\\n",
       "0         No info   3897             2             50              24   \n",
       "1         No info   7662             7             25               1   \n",
       "2         No info  13882            19              0               9   \n",
       "3         No info   6218             5             25              12   \n",
       "4         No info  13302             4             45               1   \n",
       "\n",
       "   Month_of_Journey  Dep_hr  Dep_min  Arrival_hr  Arrival_min  \n",
       "0                 3      22       20           1           10  \n",
       "1                 5       5       50          13           15  \n",
       "2                 6       9       25           4           25  \n",
       "3                 5      18        5          23           30  \n",
       "4                 3      16       50          21           35  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xRhfZXu0KvhF"
   },
   "source": [
    "The datetime columns have been taken care of. So, we now go through remaining columns columns in the dataset. As observed from training dataset, there are 6 columns with categorical values.So, we now try to apply Feature Engineering onto those columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 569
    },
    "colab_type": "code",
    "id": "AfIy9IhlKvhI",
    "outputId": "bcfb1daa-0c28-40a0-ef7d-9485a949a873"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IndiGo' 'Air India' 'Jet Airways' 'SpiceJet' 'Multiple carriers' 'GoAir'\n",
      " 'Vistara' 'Air Asia' 'Vistara Premium economy' 'Jet Airways Business'\n",
      " 'Multiple carriers Premium economy' 'Trujet']\n",
      "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\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Taking care of Airline column\n",
    "print(train['Airline'].unique())\n",
    "#print(train['Airline'].nunique())\n",
    "print(train['Airline'].value_counts())\n",
    "sns.catplot(y = 'Price', x = 'Airline', data = train.sort_values('Price', ascending = False), \n",
    "            kind='box', height = 4, aspect = 3, orient='v')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding the Categorical Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple categorical encoding can be done with 'pd.get_dummies'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10682, 15)\n"
     ]
    },
    {
     "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Duration_hrs</th>\n",
       "      <th>Duration_mins</th>\n",
       "      <th>Day_of_Journey</th>\n",
       "      <th>Month_of_Journey</th>\n",
       "      <th>Dep_hr</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arrival_hr</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>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <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>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>7</td>\n",
       "      <td>25</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>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>19</td>\n",
       "      <td>0</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>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>5</td>\n",
       "      <td>25</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>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>4</td>\n",
       "      <td>45</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 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  Duration_hrs  Duration_mins  Day_of_Journey  \\\n",
       "0         No info   3897             2             50              24   \n",
       "1         No info   7662             7             25               1   \n",
       "2         No info  13882            19              0               9   \n",
       "3         No info   6218             5             25              12   \n",
       "4         No info  13302             4             45               1   \n",
       "\n",
       "   Month_of_Journey  Dep_hr  Dep_min  Arrival_hr  Arrival_min  \n",
       "0                 3      22       20           1           10  \n",
       "1                 5       5       50          13           15  \n",
       "2                 6       9       25           4           25  \n",
       "3                 5      18        5          23           30  \n",
       "4                 3      16       50          21           35  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline_Air India</th>\n",
       "      <th>Airline_GoAir</th>\n",
       "      <th>Airline_IndiGo</th>\n",
       "      <th>Airline_Jet Airways</th>\n",
       "      <th>Airline_Jet Airways Business</th>\n",
       "      <th>Airline_Multiple carriers</th>\n",
       "      <th>Airline_Multiple carriers Premium economy</th>\n",
       "      <th>Airline_SpiceJet</th>\n",
       "      <th>Airline_Trujet</th>\n",
       "      <th>Airline_Vistara</th>\n",
       "      <th>Airline_Vistara Premium economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \n",
       "0               0                0                                0  \n",
       "1               0                0                                0  \n",
       "2               0                0                                0  \n",
       "3               0                0                                0  \n",
       "4               0                0                                0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select categorical variables from then dataset, and then implement categorical encoding for nominal variables\n",
    "Airline=train[['Airline']]\n",
    "Airline=pd.get_dummies(Airline, drop_first=True)\n",
    "Airline.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source_Chennai</th>\n",
       "      <th>Source_Delhi</th>\n",
       "      <th>Source_Kolkata</th>\n",
       "      <th>Source_Mumbai</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai\n",
       "0               0             0               0              0\n",
       "1               0             0               1              0\n",
       "2               0             1               0              0\n",
       "3               0             0               1              0\n",
       "4               0             0               0              0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Source=train[['Source']]\n",
    "Source=pd.get_dummies(Source, drop_first= True)\n",
    "Source.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Destination_Cochin</th>\n",
       "      <th>Destination_Delhi</th>\n",
       "      <th>Destination_Hyderabad</th>\n",
       "      <th>Destination_Kolkata</th>\n",
       "      <th>Destination_New Delhi</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\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": [
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Destination=train[['Destination']]\n",
    "Destination=pd.get_dummies(Destination, drop_first= True)\n",
    "Destination.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate dataset with Airline, Source, Destination, Additional_Info\n",
    "\n",
    "train = pd.concat([train, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "#Dropping the non-encoded Airline, Source, Destination variables\n",
    "train.drop(['Airline', 'Source', 'Destination', 'Additional_Info', 'Route'], axis = 1, inplace = True)\n",
    "#dropping route column as we have a stop column which basically covers the entire zest of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "lkXoe_SkKvjN",
    "outputId": "526a1d34-4847-4bdd-e223-be9d97244763"
   },
   "outputs": [],
   "source": [
    "# Replacing Total_Stops\n",
    "train.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10682, 30)\n"
     ]
    },
    {
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       "      <th>Duration_mins</th>\n",
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       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</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>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": [
       "   Total_Stops  Price  Duration_hrs  Duration_mins  Day_of_Journey  \\\n",
       "0            0   3897             2             50              24   \n",
       "1            2   7662             7             25               1   \n",
       "2            2  13882            19              0               9   \n",
       "3            1   6218             5             25              12   \n",
       "4            1  13302             4             45               1   \n",
       "\n",
       "   Month_of_Journey  Dep_hr  Dep_min  Arrival_hr  Arrival_min  \\\n",
       "0                 3      22       20           1           10   \n",
       "1                 5       5       50          13           15   \n",
       "2                 6       9       25           4           25   \n",
       "3                 5      18        5          23           30   \n",
       "4                 3      16       50          21           35   \n",
       "\n",
       "   Airline_Air India  Airline_GoAir  Airline_IndiGo  Airline_Jet Airways  \\\n",
       "0                  0              0               1                    0   \n",
       "1                  1              0               0                    0   \n",
       "2                  0              0               0                    1   \n",
       "3                  0              0               1                    0   \n",
       "4                  0              0               1                    0   \n",
       "\n",
       "   Airline_Jet Airways Business  Airline_Multiple carriers  \\\n",
       "0                             0                          0   \n",
       "1                             0                          0   \n",
       "2                             0                          0   \n",
       "3                             0                          0   \n",
       "4                             0                          0   \n",
       "\n",
       "   Airline_Multiple carriers Premium economy  Airline_SpiceJet  \\\n",
       "0                                          0                 0   \n",
       "1                                          0                 0   \n",
       "2                                          0                 0   \n",
       "3                                          0                 0   \n",
       "4                                          0                 0   \n",
       "\n",
       "   Airline_Trujet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0               0                0                                0   \n",
       "1               0                0                                0   \n",
       "2               0                0                                0   \n",
       "3               0                0                                0   \n",
       "4               0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             0               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             0               1              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   0                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   0                  0                      0   \n",
       "4                   0                  0                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      1  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hKUSSi5xKvkK"
   },
   "source": [
    "# Applying Feature Engineering which we implemented on Training dataset now on Test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5PRCMmjEKvkO"
   },
   "source": [
    "First we consider datetime variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "e1PStaAKKvkR",
    "outputId": "d62791a7-bef7-4b50-8c1c-874977f10472"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2h 50m     122\n",
       "1h 30m     107\n",
       "2h 45m      95\n",
       "2h 55m      81\n",
       "3h          72\n",
       "          ... \n",
       "20h 30m      1\n",
       "32h 25m      1\n",
       "4h 10m       1\n",
       "3h 45m       1\n",
       "20h 25m      1\n",
       "Name: Duration, Length: 320, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First we consider Duration column\n",
    "test[\"Duration\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Yu71ifvyKvki",
    "outputId": "d2346913-e337-435f-e914-62386acb0b56"
   },
   "outputs": [
    {
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       " '23h 10m',\n",
       " ...]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting 'Duration' column into a list\n",
    "duration_test = list(test[\"Duration\"])\n",
    "duration_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yELoCajFKvkx"
   },
   "outputs": [],
   "source": [
    "#apply loop to separate hours from minutes\n",
    "#.split() method splits a string into a list\n",
    "#also, hour is represented by 'h' and minute by'm'\n",
    "#.strip() method returns a copy of the string by removing both the leading and the trailing characters\n",
    "for i in range(len(duration_test)):\n",
    "    if len(duration_test[i].split()) != 2:   \n",
    "        if \"h\" in duration_test[i]:\n",
    "            duration_test[i] = duration_test[i].strip() + ' 0m'  # add 0 minute \n",
    "        else:\n",
    "            duration_test[i] = '0h '+ duration_test[i]           # add 0 hour\n",
    "            \n",
    "duration_hours = []\n",
    "duration_mins = []\n",
    "for i in range(len(duration_test)):\n",
    "    duration_hours.append(int(duration_test[i].split(sep = \"h\")[0]))\n",
    "    duration_mins.append(int(duration_test[i].split(sep = \"m\")[0].split()[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "bHv2xkkPKvlB",
    "outputId": "fd5e8c30-801f-406b-a508-95bc2627a391"
   },
   "outputs": [
    {
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       " ...]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duration_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "3kqfDcqbKvlM",
    "outputId": "e922b04c-6eee-48a1-dda2-72d271f1ecd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10\n",
       "1        4\n",
       "2       23\n",
       "3       13\n",
       "4        2\n",
       "        ..\n",
       "2666    23\n",
       "2667     2\n",
       "2668     6\n",
       "2669    15\n",
       "2670    14\n",
       "Name: Duration_hrs, Length: 2671, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Duration_hrs'] = duration_hours\n",
    "test['Duration_hrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "Gt5BTUCYKvlc",
    "outputId": "136a74cf-0f52-47de-d09d-1a76b5b4f04e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       55\n",
       "1        0\n",
       "2       45\n",
       "3        0\n",
       "4       50\n",
       "        ..\n",
       "2666    55\n",
       "2667    35\n",
       "2668    35\n",
       "2669    15\n",
       "2670    20\n",
       "Name: Duration_mins, Length: 2671, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Duration_mins'] = duration_mins\n",
    "test['Duration_mins']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9tJ432gUKvmG"
   },
   "outputs": [],
   "source": [
    "test.drop('Duration', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z7jo0ChCKvmS"
   },
   "outputs": [],
   "source": [
    "test['Day_of_Journey']=pd.to_datetime(test['Date_of_Journey'], format='%d/%m/%Y').dt.day\n",
    "test['Month_of_Journey']=pd.to_datetime(test['Date_of_Journey'], format='%d/%m/%Y').dt.month\n",
    "test.drop('Date_of_Journey', axis = 1, inplace = True)\n",
    "\n",
    "test['Dep_hr'] = pd.to_datetime(test['Dep_Time']).dt.hour\n",
    "test['Dep_min'] = pd.to_datetime(test['Dep_Time']).dt.minute\n",
    "test.drop('Dep_Time', axis = 1, inplace = True)\n",
    "\n",
    "test['Arrival_hr'] = pd.to_datetime(test['Arrival_Time']).dt.hour\n",
    "test['Arrival_min'] = pd.to_datetime(test['Arrival_Time']).dt.minute\n",
    "test.drop('Arrival_Time', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "uiVAKjS8Kvmo",
    "outputId": "66aff20a-d0eb-4ad9-db42-76d89f93b388"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test dataset shape: (2671, 14)\n"
     ]
    }
   ],
   "source": [
    "print('Test dataset shape:', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bvke2Eb-Kvm_"
   },
   "source": [
    "Now that the datetime variables have been taken care of, we shift our focus to categorical variable columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2671, 28)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "   Total_Stops  Duration_hrs  Duration_mins  Day_of_Journey  Month_of_Journey  \\\n",
       "0            1            10             55               6                 6   \n",
       "1            1             4              0              12                 5   \n",
       "2            1            23             45              21                 5   \n",
       "3            1            13              0              21                 5   \n",
       "4            0             2             50              24                 6   \n",
       "\n",
       "   Dep_hr  Dep_min  Arrival_hr  Arrival_min  Airline_Air India  Airline_GoAir  \\\n",
       "0      17       30           4           25                  0              0   \n",
       "1       6       20          10           20                  0              0   \n",
       "2      19       15          19            0                  0              0   \n",
       "3       8        0          21            0                  0              0   \n",
       "4      23       55           2           45                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               0                    1                             0   \n",
       "1               1                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               0                    0                             0   \n",
       "4               0                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          1                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Vistara  Airline_Vistara Premium economy  \\\n",
       "0                 0                0                                0   \n",
       "1                 0                0                                0   \n",
       "2                 0                0                                0   \n",
       "3                 0                0                                0   \n",
       "4                 0                0                                0   \n",
       "\n",
       "   Source_Chennai  Source_Delhi  Source_Kolkata  Source_Mumbai  \\\n",
       "0               0             1               0              0   \n",
       "1               0             0               1              0   \n",
       "2               0             1               0              0   \n",
       "3               0             1               0              0   \n",
       "4               0             0               0              0   \n",
       "\n",
       "   Destination_Cochin  Destination_Delhi  Destination_Hyderabad  \\\n",
       "0                   1                  0                      0   \n",
       "1                   0                  0                      0   \n",
       "2                   1                  0                      0   \n",
       "3                   1                  0                      0   \n",
       "4                   0                  1                      0   \n",
       "\n",
       "   Destination_Kolkata  Destination_New Delhi  \n",
       "0                    0                      0  \n",
       "1                    0                      0  \n",
       "2                    0                      0  \n",
       "3                    0                      0  \n",
       "4                    0                      0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select categorical variables from then dataset, and then implement categorical encoding for nominal variables\n",
    "Airline=test[['Airline']]\n",
    "Airline=pd.get_dummies(Airline, drop_first=True)\n",
    "\n",
    "Source=test[['Source']]\n",
    "Source=pd.get_dummies(Source, drop_first= True)\n",
    "\n",
    "Destination=test[['Destination']]\n",
    "Destination=pd.get_dummies(Destination, drop_first= True)\n",
    "\n",
    "\n",
    "# Concatenate dataset with Airline, Source, Destination, Additional_Info\n",
    "test= pd.concat([test, Airline, Source, Destination], axis = 1)\n",
    "\n",
    "#Dropping the non-encoded Airline, Source, Destination variables\n",
    "test.drop(['Airline', 'Source', 'Destination', 'Additional_Info', 'Route'], axis = 1, inplace = True)\n",
    "#dropping route column as we have a stop column which basically covers the entire zest of it \n",
    "\n",
    "\n",
    "#Let's take care of Total_Stops\n",
    "test.replace({\"non-stop\": 0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\": 3, \"4 stops\": 4}, inplace = True)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "test['Total_Stops'] = encoder.fit_transform(test['Total_Stops'])\n",
    "\n",
    "print(test.shape)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Price', 'Duration_hrs', 'Duration_mins',\n",
       "       'Day_of_Journey', 'Month_of_Journey', 'Dep_hr', 'Dep_min', 'Arrival_hr',\n",
       "       'Arrival_min', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
       "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
       "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
       "       'Destination_Kolkata', 'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total_Stops', 'Duration_hrs', 'Duration_mins', 'Day_of_Journey',\n",
       "       'Month_of_Journey', 'Dep_hr', 'Dep_min', 'Arrival_hr', 'Arrival_min',\n",
       "       'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
       "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
       "       'Airline_Multiple carriers',\n",
       "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
       "       'Airline_Vistara', 'Airline_Vistara Premium economy', 'Source_Chennai',\n",
       "       'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai', 'Destination_Cochin',\n",
       "       'Destination_Delhi', 'Destination_Hyderabad', 'Destination_Kolkata',\n",
       "       'Destination_New Delhi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train.drop(labels=['Airline_Trujet'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jYiYnwb0KvqJ"
   },
   "source": [
    "## Building the training and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O0uRW6ti4SJH",
    "outputId": "93dd4801-978e-4612-cec6-a116a489a9c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_Stops                                  int64\n",
       "Price                                        int64\n",
       "Duration_hrs                                 int64\n",
       "Duration_mins                                int64\n",
       "Day_of_Journey                               int64\n",
       "Month_of_Journey                             int64\n",
       "Dep_hr                                       int64\n",
       "Dep_min                                      int64\n",
       "Arrival_hr                                   int64\n",
       "Arrival_min                                  int64\n",
       "Airline_Air India                            uint8\n",
       "Airline_GoAir                                uint8\n",
       "Airline_IndiGo                               uint8\n",
       "Airline_Jet Airways                          uint8\n",
       "Airline_Jet Airways Business                 uint8\n",
       "Airline_Multiple carriers                    uint8\n",
       "Airline_Multiple carriers Premium economy    uint8\n",
       "Airline_SpiceJet                             uint8\n",
       "Airline_Trujet                               uint8\n",
       "Airline_Vistara                              uint8\n",
       "Airline_Vistara Premium economy              uint8\n",
       "Source_Chennai                               uint8\n",
       "Source_Delhi                                 uint8\n",
       "Source_Kolkata                               uint8\n",
       "Source_Mumbai                                uint8\n",
       "Destination_Cochin                           uint8\n",
       "Destination_Delhi                            uint8\n",
       "Destination_Hyderabad                        uint8\n",
       "Destination_Kolkata                          uint8\n",
       "Destination_New Delhi                        uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "cdaGwWScKvqK",
    "outputId": "22cf913f-309f-4280-e769-8bdb4fc02991"
   },
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Stops</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7662</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Total_Stops  Duration_hrs  Duration_mins  Day_of_Journey  Month_of_Journey  \\\n",
       "0            0             2             50              24                 3   \n",
       "1            2             7             25               1                 5   \n",
       "2            2            19              0               9                 6   \n",
       "3            1             5             25              12                 5   \n",
       "4            1             4             45               1                 3   \n",
       "\n",
       "   Dep_hr  Dep_min  Arrival_hr  Arrival_min  Airline_Air India  Airline_GoAir  \\\n",
       "0      22       20           1           10                  0              0   \n",
       "1       5       50          13           15                  1              0   \n",
       "2       9       25           4           25                  0              0   \n",
       "3      18        5          23           30                  0              0   \n",
       "4      16       50          21           35                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  Price  \n",
       "0                      0                    0                      1   3897  \n",
       "1                      0                    0                      0   7662  \n",
       "2                      0                    0                      0  13882  \n",
       "3                      0                    0                      0   6218  \n",
       "4                      0                    0                      1  13302  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price=train.Price\n",
    "train.drop('Price', axis=1, inplace=True)\n",
    "train=train.join(price)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4gPxIyCtKvqU"
   },
   "outputs": [
    {
     "data": {
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       "      <td>19</td>\n",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>25</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>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>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>1</td>\n",
       "      <td>4</td>\n",
       "      <td>45</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>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>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": [
       "   Total_Stops  Duration_hrs  Duration_mins  Day_of_Journey  Month_of_Journey  \\\n",
       "0            0             2             50              24                 3   \n",
       "1            2             7             25               1                 5   \n",
       "2            2            19              0               9                 6   \n",
       "3            1             5             25              12                 5   \n",
       "4            1             4             45               1                 3   \n",
       "\n",
       "   Dep_hr  Dep_min  Arrival_hr  Arrival_min  Airline_Air India  Airline_GoAir  \\\n",
       "0      22       20           1           10                  0              0   \n",
       "1       5       50          13           15                  1              0   \n",
       "2       9       25           4           25                  0              0   \n",
       "3      18        5          23           30                  0              0   \n",
       "4      16       50          21           35                  0              0   \n",
       "\n",
       "   Airline_IndiGo  Airline_Jet Airways  Airline_Jet Airways Business  \\\n",
       "0               1                    0                             0   \n",
       "1               0                    0                             0   \n",
       "2               0                    1                             0   \n",
       "3               1                    0                             0   \n",
       "4               1                    0                             0   \n",
       "\n",
       "   Airline_Multiple carriers  Airline_Multiple carriers Premium economy  \\\n",
       "0                          0                                          0   \n",
       "1                          0                                          0   \n",
       "2                          0                                          0   \n",
       "3                          0                                          0   \n",
       "4                          0                                          0   \n",
       "\n",
       "   Airline_SpiceJet  Airline_Trujet  Airline_Vistara  \\\n",
       "0                 0               0                0   \n",
       "1                 0               0                0   \n",
       "2                 0               0                0   \n",
       "3                 0               0                0   \n",
       "4                 0               0                0   \n",
       "\n",
       "   Airline_Vistara Premium economy  Source_Chennai  Source_Delhi  \\\n",
       "0                                0               0             0   \n",
       "1                                0               0             0   \n",
       "2                                0               0             1   \n",
       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "\n",
       "   Source_Kolkata  Source_Mumbai  Destination_Cochin  Destination_Delhi  \\\n",
       "0               0              0                   0                  0   \n",
       "1               1              0                   0                  0   \n",
       "2               0              0                   1                  0   \n",
       "3               1              0                   0                  0   \n",
       "4               0              0                   0                  0   \n",
       "\n",
       "   Destination_Hyderabad  Destination_Kolkata  Destination_New Delhi  \n",
       "0                      0                    0                      1  \n",
       "1                      0                    0                      0  \n",
       "2                      0                    0                      0  \n",
       "3                      0                    0                      0  \n",
       "4                      0                    0                      1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train.loc[:, ['Total_Stops', 'Duration_hrs', 'Duration_mins',\n",
    "       'Day_of_Journey', 'Month_of_Journey', 'Dep_hr', 'Dep_min', 'Arrival_hr',\n",
    "       'Arrival_min', 'Airline_Air India', 'Airline_GoAir', 'Airline_IndiGo',\n",
    "       'Airline_Jet Airways', 'Airline_Jet Airways Business',\n",
    "       'Airline_Multiple carriers',\n",
    "       'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet',\n",
    "       'Airline_Trujet', 'Airline_Vistara', 'Airline_Vistara Premium economy',\n",
    "       'Source_Chennai', 'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai',\n",
    "       'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
    "       'Destination_Kolkata', 'Destination_New Delhi']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         3897\n",
       "1         7662\n",
       "2        13882\n",
       "3         6218\n",
       "4        13302\n",
       "         ...  \n",
       "10678     4107\n",
       "10679     4145\n",
       "10680     7229\n",
       "10681    12648\n",
       "10682    11753\n",
       "Name: Price, Length: 10682, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = train.iloc[:, -1]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pWhDhM5q4SJL",
    "outputId": "dcbe6559-8595-458e-c18b-4aac06bf2af0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Important feature using ExtraTreesRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(X, y)\n",
    "\n",
    "#bar graph of feature importances \n",
    "plt.figure(figsize = (10,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2oEqp3Ob4SJN"
   },
   "source": [
    "From the above graph, \"Total_Stops\" is the most important feature. Looking closely, at the high end of the feature importance spectrum, there are no 2 features which are similar in their importance. However, at the lower end, we have 4 features which are almost similar in their importance. So, here I may decide on reducing the dimensionality of the features but chose not to do so because the dimensionality of the dataset is not too large and my model seems to handle it well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NgU5R_A74SJP"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "kfold = StratifiedKFold(n_splits=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rte7rr2AhRbA"
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B70vwhez4SJT"
   },
   "source": [
    "# Training Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### R2 is a statistic that will give some information about the goodness of fit of a model. In regression, the R2 coefficient of determination is a statistical measure of how well the regression predictions approximate the real data points. An R2 of 1 indicates that the regression predictions perfectly fit the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Score on Training set is 0.632211207212663\n",
      "Linear Regression Score on Test Set is 0.5888639023134974\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/flightfare/lib/python3.6/site-packages/sklearn/model_selection/_split.py:672: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=20.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.56348676 0.57670098 0.52740464 0.60808342 0.60919873 0.60086353\n",
      " 0.58424154 0.63494715 0.61982921 0.75026022 0.64214428 0.60795505\n",
      " 0.64426589 0.77259566 0.66851263 0.65452934 0.67243843 0.68649251\n",
      " 0.58903934 0.48634662]\n",
      "Accuracy: 62.50 %\n",
      "Standard Deviation: 6.57 %\n",
      "Mean Absolute Error: 2048.2726266051595\n",
      "Mean Squared Error: 9646558.195863314\n",
      "RMSE: 3105.8908860201955\n",
      "The r2_score is 0.5888639023134974\n"
     ]
    }
   ],
   "source": [
    "#Linear Regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg=LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "y_pred=lin_reg.predict(X_test)\n",
    "\n",
    "print(\"Linear Regression Score on Training set is\",lin_reg.score(X_train, y_train))#Training Accuracy\n",
    "print(\"Linear Regression Score on Test Set is\",lin_reg.score(X_test, y_test))#Testing Accuracy\n",
    "\n",
    "accuracies = cross_val_score(lin_reg, X_train, y_train, cv = kfold)\n",
    "print(accuracies)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))\n",
    "\n",
    "mae=mean_absolute_error(y_pred, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "mse=mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\" , mse)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "print('The r2_score is', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z-WCiIXZ4SJT"
   },
   "source": [
    "#### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3rLllDuS4SJU",
    "outputId": "773f88bd-24c1-4f34-9cce-c758f48f72c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Score on Training set is 0.9711683243930616\n",
      "Decision Tree Score on Test Set is 0.6954008339903984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/flightfare/lib/python3.6/site-packages/sklearn/model_selection/_split.py:672: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=20.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76632498 0.73377156 0.63841465 0.70883606 0.77836437 0.74763147\n",
      " 0.70082349 0.62857383 0.66322775 0.71009527 0.69802443 0.71596437\n",
      " 0.77150069 0.77623934 0.73944594 0.75718428 0.75660023 0.67252257\n",
      " 0.69490409 0.69643139]\n",
      "Accuracy: 71.77 %\n",
      "Standard Deviation: 4.36 %\n",
      "Mean Absolute Error: 1431.794018093901\n",
      "Mean Squared Error: 7146863.527326601\n",
      "RMSE: 2673.3618399548163\n",
      "The r2_score is 0.6954008339903984\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree Regressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt_reg = DecisionTreeRegressor(random_state = 0)\n",
    "dt_reg.fit(X_train, y_train)\n",
    "y_pred=dt_reg.predict(X_test)\n",
    "\n",
    "print(\"Decision Tree Score on Training set is\",dt_reg.score(X_train, y_train))#Training Accuracy\n",
    "print(\"Decision Tree Score on Test Set is\",dt_reg.score(X_test, y_test))#Testing Accuracy\n",
    "\n",
    "accuracies = cross_val_score(dt_reg, X_train, y_train, cv = kfold)\n",
    "print(accuracies)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))\n",
    "\n",
    "mae=mean_absolute_error(y_pred, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "mse=mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\" , mse)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "print('The r2_score is', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P6g82hCo4SJW"
   },
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pibJBItJ4SJW",
    "outputId": "8c16ba6e-c4c9-4250-c074-59fb4b8dcb29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Score on Training set is 0.8893398094183942\n",
      "Random Forest Score on Test Set is 0.8231291895253225\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/flightfare/lib/python3.6/site-packages/sklearn/model_selection/_split.py:672: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=20.\n",
      "  % (min_groups, self.n_splits)), UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86370474 0.84899475 0.76931963 0.85660182 0.83637176 0.85031606\n",
      " 0.77797147 0.84886801 0.84009264 0.70152005 0.83298053 0.83975564\n",
      " 0.87202998 0.86046618 0.85811518 0.85977315 0.85079489 0.84019289\n",
      " 0.8146483  0.74049761]\n",
      "Accuracy: 82.82 %\n",
      "Standard Deviation: 4.43 %\n",
      "Mean Absolute Error: 1210.2693463211538\n",
      "Mean Squared Error: 4149950.772978552\n",
      "RMSE: 2037.1427964132884\n",
      "The r2_score is 0.8231291895253225\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/allyson/anaconda3/envs/flightfare/lib/python3.6/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forest Regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_reg = RandomForestRegressor(n_estimators=400,min_samples_split=15,min_samples_leaf=2,\n",
    "max_features='auto', max_depth=30)\n",
    "rf_reg.fit(X_train, y_train)\n",
    "y_pred=rf_reg.predict(X_test)\n",
    "\n",
    "print(\"Random Forest Score on Training set is\",rf_reg.score(X_train, y_train))#Training Accuracy\n",
    "print(\"Random Forest Score on Test Set is\",rf_reg.score(X_test, y_test))#Testing Accuracy\n",
    "\n",
    "accuracies = cross_val_score(rf_reg, X_train, y_train, cv = kfold)\n",
    "print(accuracies)\n",
    "print(\"Accuracy: {:.2f} %\".format(accuracies.mean()*100))\n",
    "print(\"Standard Deviation: {:.2f} %\".format(accuracies.std()*100))\n",
    "\n",
    "mae=mean_absolute_error(y_pred, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "mse=mean_squared_error(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\" , mse)\n",
    "\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))\n",
    "\n",
    "print('The r2_score is', metrics.r2_score(y_test, y_pred))\n",
    "\n",
    "sns.distplot(y_test-y_pred)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(y_test, y_pred, alpha = 0.5)\n",
    "plt.xlabel(\"y_test\")\n",
    "plt.ylabel(\"y_pred\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UErL21eO5pZE"
   },
   "source": [
    "#### On plotting the value {y_test-y_pred}, I am getting a very narrow gaussian distributed curve for all regression models, which shows that the difference between actual(test) and predicted value is minimal. \n",
    "\n",
    "#### When all the metrics are considered, Random Forest Regressor model gave me the best scores.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q3PS85FQ4-a0"
   },
   "source": [
    "## Save the Model and dump as a Pickle File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# dump information to the file\n",
    "pickle.dump(rf_reg, open('rf_reg.pkl', 'wb'))\n",
    "model = pickle.load(open('rf_reg.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5321.24261994])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0,2,50,24,3,22,20,1,10,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]])"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "flight_fare_prediction.ipynb",
   "provenance": []
  },
  "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}