Airline_Fare_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Airline Fare Prediction using Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9142c40e",
   "metadata": {},
   "source": [
    "  Author: [KRITIK AGARWAL](https://github.com/Kritik007) \n",
    "  \n",
    "  [Copyright (c) 2022 Kritik Agarwal](https://github.com/Kritik007/Airline-Fare-Prediction/blob/8ce82e4fec395c4acfc942d18471b7fe89b6bf8d/LICENSE#L3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "abe861b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </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": [
    "df=pd.read_excel('.\\Airline_Fare_Prediction_Data.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10683, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
       "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
       "       'Additional_Info', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detail_info(data):\n",
    "    temp_df = pd.DataFrame(index= data.columns)\n",
    "    \n",
    "    temp_df['data_type'] = data.dtypes\n",
    "    temp_df['unique_val'] = data.nunique()\n",
    "    temp_df['duplicate_val'] = data.duplicated().sum()\n",
    "    temp_df['missing_val'] = data.isnull().sum()\n",
    "    temp_df['missing_val_%'] = round(data.isnull().mean()*100,2)\n",
    "    \n",
    "    return temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_type</th>\n",
       "      <th>unique_val</th>\n",
       "      <th>duplicate_val</th>\n",
       "      <th>missing_val</th>\n",
       "      <th>missing_val_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Airline</th>\n",
       "      <td>object</td>\n",
       "      <td>12</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <td>object</td>\n",
       "      <td>44</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <td>object</td>\n",
       "      <td>6</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route</th>\n",
       "      <td>object</td>\n",
       "      <td>128</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time</th>\n",
       "      <td>object</td>\n",
       "      <td>222</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time</th>\n",
       "      <td>object</td>\n",
       "      <td>1343</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>object</td>\n",
       "      <td>368</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Additional_Info</th>\n",
       "      <td>object</td>\n",
       "      <td>10</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>int64</td>\n",
       "      <td>1870</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                data_type  unique_val  duplicate_val  missing_val  \\\n",
       "Airline            object          12            220            0   \n",
       "Date_of_Journey    object          44            220            0   \n",
       "Source             object           5            220            0   \n",
       "Destination        object           6            220            0   \n",
       "Route              object         128            220            1   \n",
       "Dep_Time           object         222            220            0   \n",
       "Arrival_Time       object        1343            220            0   \n",
       "Duration           object         368            220            0   \n",
       "Total_Stops        object           5            220            1   \n",
       "Additional_Info    object          10            220            0   \n",
       "Price               int64        1870            220            0   \n",
       "\n",
       "                 missing_val_%  \n",
       "Airline                   0.00  \n",
       "Date_of_Journey           0.00  \n",
       "Source                    0.00  \n",
       "Destination               0.00  \n",
       "Route                     0.01  \n",
       "Dep_Time                  0.00  \n",
       "Arrival_Time              0.00  \n",
       "Duration                  0.00  \n",
       "Total_Stops               0.01  \n",
       "Additional_Info           0.00  \n",
       "Price                     0.00  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail_info(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation:\n",
    "\n",
    "* There are two missing values in Route and Total_Stops features & 220 Duplicate records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </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",
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      ],
      "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": [
    "df[df['Route'].isnull()==True]\n",
    "\n",
    "# Since there is only one record, we may delete the row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10677</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>19:55</td>\n",
       "      <td>22:25</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>Air India</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>20:45</td>\n",
       "      <td>23:20</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10679</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>08:20</td>\n",
       "      <td>11:20</td>\n",
       "      <td>3h</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10680</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>11:30</td>\n",
       "      <td>14:10</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10681</th>\n",
       "      <td>Air India</td>\n",
       "      <td>9/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>10:55</td>\n",
       "      <td>19:15</td>\n",
       "      <td>8h 20m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>11753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10682 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline Date_of_Journey    Source Destination  \\\n",
       "0           IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "1        Air India       1/05/2019   Kolkata    Banglore   \n",
       "2      Jet Airways       9/06/2019     Delhi      Cochin   \n",
       "3           IndiGo      12/05/2019   Kolkata    Banglore   \n",
       "4           IndiGo      01/03/2019  Banglore   New Delhi   \n",
       "...            ...             ...       ...         ...   \n",
       "10677     Air Asia       9/04/2019   Kolkata    Banglore   \n",
       "10678    Air India      27/04/2019   Kolkata    Banglore   \n",
       "10679  Jet Airways      27/04/2019  Banglore       Delhi   \n",
       "10680      Vistara      01/03/2019  Banglore   New Delhi   \n",
       "10681    Air India       9/05/2019     Delhi      Cochin   \n",
       "\n",
       "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "0                  BLR → DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
       "1      CCU → IXR → BBI → BLR    05:50         13:15   7h 25m     2 stops   \n",
       "2      DEL → LKO → BOM → COK    09:25  04:25 10 Jun      19h     2 stops   \n",
       "3            CCU → NAG → BLR    18:05         23:30   5h 25m      1 stop   \n",
       "4            BLR → NAG → DEL    16:50         21:35   4h 45m      1 stop   \n",
       "...                      ...      ...           ...      ...         ...   \n",
       "10677              CCU → BLR    19:55         22:25   2h 30m    non-stop   \n",
       "10678              CCU → BLR    20:45         23:20   2h 35m    non-stop   \n",
       "10679              BLR → DEL    08:20         11:20       3h    non-stop   \n",
       "10680              BLR → DEL    11:30         14:10   2h 40m    non-stop   \n",
       "10681  DEL → GOI → BOM → COK    10:55         19:15   8h 20m     2 stops   \n",
       "\n",
       "      Additional_Info  Price  \n",
       "0             No info   3897  \n",
       "1             No info   7662  \n",
       "2             No info  13882  \n",
       "3             No info   6218  \n",
       "4             No info  13302  \n",
       "...               ...    ...  \n",
       "10677         No info   4107  \n",
       "10678         No info   4145  \n",
       "10679         No info   7229  \n",
       "10680         No info  12648  \n",
       "10681         No info  11753  \n",
       "\n",
       "[10682 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
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       "      <th>Price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>1/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → NAG → BOM → COK</td>\n",
       "      <td>14:35</td>\n",
       "      <td>04:25 02 Jun</td>\n",
       "      <td>13h 50m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1061</th>\n",
       "      <td>Air India</td>\n",
       "      <td>21/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>22:00</td>\n",
       "      <td>19:15 22 May</td>\n",
       "      <td>21h 15m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>10231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>Air India</td>\n",
       "      <td>18/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → HYD → BOM → COK</td>\n",
       "      <td>17:15</td>\n",
       "      <td>19:15 19 May</td>\n",
       "      <td>26h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>12392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1418</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>05:30</td>\n",
       "      <td>04:25 07 Jun</td>\n",
       "      <td>22h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>10368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</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>18:25</td>\n",
       "      <td>21:20</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → AMD → BOM → COK</td>\n",
       "      <td>23:05</td>\n",
       "      <td>12:35 28 Jun</td>\n",
       "      <td>13h 30m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>12819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10616</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>1/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>09:40</td>\n",
       "      <td>12:35 02 Jun</td>\n",
       "      <td>26h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10634</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>6/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → JAI → BOM → COK</td>\n",
       "      <td>09:40</td>\n",
       "      <td>12:35 07 Jun</td>\n",
       "      <td>26h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10672</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → AMD → BOM → COK</td>\n",
       "      <td>23:05</td>\n",
       "      <td>19:00 28 Jun</td>\n",
       "      <td>19h 55m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>In-flight meal not included</td>\n",
       "      <td>11150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10673</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → AMD → BOM → COK</td>\n",
       "      <td>13:25</td>\n",
       "      <td>04:25 28 May</td>\n",
       "      <td>15h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>16704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline Date_of_Journey    Source Destination  \\\n",
       "683    Jet Airways       1/06/2019     Delhi      Cochin   \n",
       "1061     Air India      21/05/2019     Delhi      Cochin   \n",
       "1348     Air India      18/05/2019     Delhi      Cochin   \n",
       "1418   Jet Airways       6/06/2019     Delhi      Cochin   \n",
       "1674        IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "...            ...             ...       ...         ...   \n",
       "10594  Jet Airways      27/06/2019     Delhi      Cochin   \n",
       "10616  Jet Airways       1/06/2019     Delhi      Cochin   \n",
       "10634  Jet Airways       6/06/2019     Delhi      Cochin   \n",
       "10672  Jet Airways      27/06/2019     Delhi      Cochin   \n",
       "10673  Jet Airways      27/05/2019     Delhi      Cochin   \n",
       "\n",
       "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "683    DEL → NAG → BOM → COK    14:35  04:25 02 Jun  13h 50m     2 stops   \n",
       "1061   DEL → GOI → BOM → COK    22:00  19:15 22 May  21h 15m     2 stops   \n",
       "1348   DEL → HYD → BOM → COK    17:15  19:15 19 May      26h     2 stops   \n",
       "1418   DEL → JAI → BOM → COK    05:30  04:25 07 Jun  22h 55m     2 stops   \n",
       "1674               BLR → DEL    18:25         21:20   2h 55m    non-stop   \n",
       "...                      ...      ...           ...      ...         ...   \n",
       "10594  DEL → AMD → BOM → COK    23:05  12:35 28 Jun  13h 30m     2 stops   \n",
       "10616  DEL → JAI → BOM → COK    09:40  12:35 02 Jun  26h 55m     2 stops   \n",
       "10634  DEL → JAI → BOM → COK    09:40  12:35 07 Jun  26h 55m     2 stops   \n",
       "10672  DEL → AMD → BOM → COK    23:05  19:00 28 Jun  19h 55m     2 stops   \n",
       "10673  DEL → AMD → BOM → COK    13:25  04:25 28 May      15h     2 stops   \n",
       "\n",
       "                   Additional_Info  Price  \n",
       "683                        No info  13376  \n",
       "1061                       No info  10231  \n",
       "1348                       No info  12392  \n",
       "1418   In-flight meal not included  10368  \n",
       "1674                       No info   7303  \n",
       "...                            ...    ...  \n",
       "10594                      No info  12819  \n",
       "10616                      No info  13014  \n",
       "10634  In-flight meal not included  11733  \n",
       "10672  In-flight meal not included  11150  \n",
       "10673                      No info  16704  \n",
       "\n",
       "[220 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>IndiGo</td>\n",
       "      <td>24/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
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       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
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       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
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       "      <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",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10457</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>9/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>19:55</td>\n",
       "      <td>22:25</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458</th>\n",
       "      <td>Air India</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>20:45</td>\n",
       "      <td>23:20</td>\n",
       "      <td>2h 35m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10459</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>27/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>08:20</td>\n",
       "      <td>11:20</td>\n",
       "      <td>3h</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10460</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>11:30</td>\n",
       "      <td>14:10</td>\n",
       "      <td>2h 40m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10461</th>\n",
       "      <td>Air India</td>\n",
       "      <td>9/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>10:55</td>\n",
       "      <td>19:15</td>\n",
       "      <td>8h 20m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>11753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10462 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Airline Date_of_Journey    Source Destination  \\\n",
       "0           IndiGo      24/03/2019  Banglore   New Delhi   \n",
       "1        Air India       1/05/2019   Kolkata    Banglore   \n",
       "2      Jet Airways       9/06/2019     Delhi      Cochin   \n",
       "3           IndiGo      12/05/2019   Kolkata    Banglore   \n",
       "4           IndiGo      01/03/2019  Banglore   New Delhi   \n",
       "...            ...             ...       ...         ...   \n",
       "10457     Air Asia       9/04/2019   Kolkata    Banglore   \n",
       "10458    Air India      27/04/2019   Kolkata    Banglore   \n",
       "10459  Jet Airways      27/04/2019  Banglore       Delhi   \n",
       "10460      Vistara      01/03/2019  Banglore   New Delhi   \n",
       "10461    Air India       9/05/2019     Delhi      Cochin   \n",
       "\n",
       "                       Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
       "0                  BLR → DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
       "1      CCU → IXR → BBI → BLR    05:50         13:15   7h 25m     2 stops   \n",
       "2      DEL → LKO → BOM → COK    09:25  04:25 10 Jun      19h     2 stops   \n",
       "3            CCU → NAG → BLR    18:05         23:30   5h 25m      1 stop   \n",
       "4            BLR → NAG → DEL    16:50         21:35   4h 45m      1 stop   \n",
       "...                      ...      ...           ...      ...         ...   \n",
       "10457              CCU → BLR    19:55         22:25   2h 30m    non-stop   \n",
       "10458              CCU → BLR    20:45         23:20   2h 35m    non-stop   \n",
       "10459              BLR → DEL    08:20         11:20       3h    non-stop   \n",
       "10460              BLR → DEL    11:30         14:10   2h 40m    non-stop   \n",
       "10461  DEL → GOI → BOM → COK    10:55         19:15   8h 20m     2 stops   \n",
       "\n",
       "      Additional_Info  Price  \n",
       "0             No info   3897  \n",
       "1             No info   7662  \n",
       "2             No info  13882  \n",
       "3             No info   6218  \n",
       "4             No info  13302  \n",
       "...               ...    ...  \n",
       "10457         No info   4107  \n",
       "10458         No info   4145  \n",
       "10459         No info   7229  \n",
       "10460         No info  12648  \n",
       "10461         No info  11753  \n",
       "\n",
       "[10462 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep ='first', inplace=True)\n",
    "df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data_type</th>\n",
       "      <th>unique_val</th>\n",
       "      <th>duplicate_val</th>\n",
       "      <th>missing_val</th>\n",
       "      <th>missing_val_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Airline</th>\n",
       "      <td>object</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <td>object</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <td>object</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Route</th>\n",
       "      <td>object</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time</th>\n",
       "      <td>object</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time</th>\n",
       "      <td>object</td>\n",
       "      <td>1343</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>object</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>object</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Additional_Info</th>\n",
       "      <td>object</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>int64</td>\n",
       "      <td>1870</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                data_type  unique_val  duplicate_val  missing_val  \\\n",
       "Airline            object          12              0            0   \n",
       "Date_of_Journey    object          44              0            0   \n",
       "Source             object           5              0            0   \n",
       "Destination        object           6              0            0   \n",
       "Route              object         128              0            0   \n",
       "Dep_Time           object         222              0            0   \n",
       "Arrival_Time       object        1343              0            0   \n",
       "Duration           object         368              0            0   \n",
       "Total_Stops        object           5              0            0   \n",
       "Additional_Info    object          10              0            0   \n",
       "Price               int64        1870              0            0   \n",
       "\n",
       "                 missing_val_%  \n",
       "Airline                    0.0  \n",
       "Date_of_Journey            0.0  \n",
       "Source                     0.0  \n",
       "Destination                0.0  \n",
       "Route                      0.0  \n",
       "Dep_Time                   0.0  \n",
       "Arrival_Time               0.0  \n",
       "Duration                   0.0  \n",
       "Total_Stops                0.0  \n",
       "Additional_Info            0.0  \n",
       "Price                      0.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail_info(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10462, 11)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_date(data, col):\n",
    "    data['date'] = data[col].str.split('/').str[0]\n",
    "    data['month']= data[col].str.split('/').str[1]\n",
    "    data['year'] = data[col].str.split('/').str[2]\n",
    "    \n",
    "    data['date'] = data['date'].astype(int)\n",
    "    data['month']= data['month'].astype(int)\n",
    "    data['year'] = data['year'].astype(int)\n",
    "    data.drop(col, axis =1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_date(df,'Date_of_Journey')\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</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>22:20</td>\n",
       "      <td>01:10 22 Mar</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</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>05:50</td>\n",
       "      <td>13:15</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</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>09:25</td>\n",
       "      <td>04:25 10 Jun</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</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>18:05</td>\n",
       "      <td>23:30</td>\n",
       "      <td>5h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</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>16:50</td>\n",
       "      <td>21:35</td>\n",
       "      <td>4h 45m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Dep_Time  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL    22:20   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR    05:50   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK    09:25   \n",
       "3       IndiGo   Kolkata    Banglore        CCU → NAG → BLR    18:05   \n",
       "4       IndiGo  Banglore   New Delhi        BLR → NAG → DEL    16:50   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info  Price  date  month  year  \n",
       "0  01:10 22 Mar   2h 50m    non-stop         No info   3897    24      3  2019  \n",
       "1         13:15   7h 25m     2 stops         No info   7662     1      5  2019  \n",
       "2  04:25 10 Jun      19h     2 stops         No info  13882     9      6  2019  \n",
       "3         23:30   5h 25m      1 stop         No info   6218    12      5  2019  \n",
       "4         21:35   4h 45m      1 stop         No info  13302     1      3  2019  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hours(data, col):\n",
    "    data[col] =data[col].str.split(' ').str[0]\n",
    "    \n",
    "    data[col+'_hour'] = data[col].str.split(':').str[0]\n",
    "    data[col+'_min']  = data[col].str.split(':').str[1]\n",
    "    data[col+'_hour'] = data[col+'_hour'].astype(int)\n",
    "    data[col+'_min']  = data[col+'_min'].astype(int)    \n",
    "    \n",
    "    data.drop(col, axis =1, inplace=True)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_hours(df, 'Dep_Time')\n",
    "extract_hours(df, 'Arrival_Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK      19h   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  date  month  year  Dep_Time_hour  \\\n",
       "0    non-stop         No info   3897    24      3  2019             22   \n",
       "1     2 stops         No info   7662     1      5  2019              5   \n",
       "2     2 stops         No info  13882     9      6  2019              9   \n",
       "\n",
       "   Dep_Time_min  Arrival_Time_hour  Arrival_Time_min  \n",
       "0            20                  1                10  \n",
       "1            50                 13                15  \n",
       "2            25                  4                25  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_duration(data, col):\n",
    "    duration = list(data[col])\n",
    "    for i in range(len(duration)):\n",
    "        if len(duration[i].split(' '))==2:\n",
    "            pass\n",
    "        else:\n",
    "            if 'h' in duration[i]:                  # Check if duration contains only hours\n",
    "                duration[i] = duration[i] + ' 0m'  # Adds 0 minutes\n",
    "            else:\n",
    "                duration[i] ='0h '+ duration[i]    # Adds 0 hours, if only minutes available\n",
    "    data[col] = duration                  \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_duration(df, 'Duration')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 50m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</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>7h 25m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>19h 0m</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline    Source Destination                  Route Duration  \\\n",
       "0       IndiGo  Banglore   New Delhi              BLR → DEL   2h 50m   \n",
       "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR   7h 25m   \n",
       "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK   19h 0m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price  date  month  year  Dep_Time_hour  \\\n",
       "0    non-stop         No info   3897    24      3  2019             22   \n",
       "1     2 stops         No info   7662     1      5  2019              5   \n",
       "2     2 stops         No info  13882     9      6  2019              9   \n",
       "\n",
       "   Dep_Time_min  Arrival_Time_hour  Arrival_Time_min  \n",
       "0            20                  1                10  \n",
       "1            50                 13                15  \n",
       "2            25                  4                25  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_duration(data, col):\n",
    "    data[col+'_hour'] = data[col].str.split(' ').str[0].str.replace('h','')\n",
    "    data[col+'_min']  = data[col].str.split(' ').str[1].str.replace('m','')\n",
    "    data[col+'_hour'] = data[col+'_hour'].astype(int)\n",
    "    data[col+'_min']  = data[col+'_min'].astype(int)    \n",
    "    \n",
    "    data.drop(col, axis =1, inplace=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_duration(df, 'Duration')"
   ]
  },
  {
   "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",
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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>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>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>Duration_hour</th>\n",
       "      <th>Duration_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>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </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",
       "\n",
       "  Additional_Info  Price  date  month  year  Dep_Time_hour  Dep_Time_min  \\\n",
       "0         No info   3897    24      3  2019             22            20   \n",
       "1         No info   7662     1      5  2019              5            50   \n",
       "2         No info  13882     9      6  2019              9            25   \n",
       "\n",
       "   Arrival_Time_hour  Arrival_Time_min  Duration_hour  Duration_min  \n",
       "0                  1                10              2            50  \n",
       "1                 13                15              7            25  \n",
       "2                  4                25             19             0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>Duration_hour</th>\n",
       "      <th>Duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6474</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → GOI → PNQ → HYD</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>17327</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Airline  Source Destination                  Route Total_Stops  \\\n",
       "6474  Air India  Mumbai   Hyderabad  BOM → GOI → PNQ → HYD     2 stops   \n",
       "\n",
       "     Additional_Info  Price  date  month  year  Dep_Time_hour  Dep_Time_min  \\\n",
       "6474         No info  17327     6      3  2019             16            50   \n",
       "\n",
       "      Arrival_Time_hour  Arrival_Time_min  Duration_hour  Duration_min  \n",
       "6474                 16                55              0             5  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets find how many flights took duration only in minutes \n",
    "\n",
    "df[df['Duration_hour']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>Duration_hour</th>\n",
       "      <th>Duration_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>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → NAG → BLR</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → NAG → DEL</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10456</th>\n",
       "      <td>Air Asia</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4107</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10457</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4145</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>7229</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10459</th>\n",
       "      <td>Vistara</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>12648</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10460</th>\n",
       "      <td>Air India</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → GOI → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>11753</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10461 rows × 16 columns</p>\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",
       "10456     Air Asia   Kolkata    Banglore              CCU → BLR    non-stop   \n",
       "10457    Air India   Kolkata    Banglore              CCU → BLR    non-stop   \n",
       "10458  Jet Airways  Banglore       Delhi              BLR → DEL    non-stop   \n",
       "10459      Vistara  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "10460    Air India     Delhi      Cochin  DEL → GOI → BOM → COK     2 stops   \n",
       "\n",
       "      Additional_Info  Price  date  month  year  Dep_Time_hour  Dep_Time_min  \\\n",
       "0             No info   3897    24      3  2019             22            20   \n",
       "1             No info   7662     1      5  2019              5            50   \n",
       "2             No info  13882     9      6  2019              9            25   \n",
       "3             No info   6218    12      5  2019             18             5   \n",
       "4             No info  13302     1      3  2019             16            50   \n",
       "...               ...    ...   ...    ...   ...            ...           ...   \n",
       "10456         No info   4107     9      4  2019             19            55   \n",
       "10457         No info   4145    27      4  2019             20            45   \n",
       "10458         No info   7229    27      4  2019              8            20   \n",
       "10459         No info  12648     1      3  2019             11            30   \n",
       "10460         No info  11753     9      5  2019             10            55   \n",
       "\n",
       "       Arrival_Time_hour  Arrival_Time_min  Duration_hour  Duration_min  \n",
       "0                      1                10              2            50  \n",
       "1                     13                15              7            25  \n",
       "2                      4                25             19             0  \n",
       "3                     23                30              5            25  \n",
       "4                     21                35              4            45  \n",
       "...                  ...               ...            ...           ...  \n",
       "10456                 22                25              2            30  \n",
       "10457                 23                20              2            35  \n",
       "10458                 11                20              3             0  \n",
       "10459                 14                10              2            40  \n",
       "10460                 19                15              8            20  \n",
       "\n",
       "[10461 rows x 16 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It is not possible that the flight took only 5 min to travel between Mumbai to Hyderbad\n",
    "\n",
    "df.drop(6474, axis=0, inplace=True)\n",
    "df.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jet Airways                          3700\n",
       "IndiGo                               2043\n",
       "Air India                            1693\n",
       "Multiple carriers                    1196\n",
       "SpiceJet                              815\n",
       "Vistara                               478\n",
       "Air Asia                              319\n",
       "GoAir                                 194\n",
       "Multiple carriers Premium economy      13\n",
       "Jet Airways Business                    6\n",
       "Vistara Premium economy                 3\n",
       "Trujet                                  1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_encoder_Airline = df['Airline'].value_counts()\n",
    "pre_encoder_Airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     3700\n",
       "3     2043\n",
       "1     1693\n",
       "6     1196\n",
       "8      815\n",
       "10     478\n",
       "0      319\n",
       "2      194\n",
       "7       13\n",
       "5        6\n",
       "11       3\n",
       "9        1\n",
       "Name: Airline, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Airline']=labelencoder.fit_transform(df['Airline'])\n",
    "\n",
    "post_encoder_Airline = df['Airline'].value_counts()\n",
    "post_encoder_Airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>Duration_hour</th>\n",
       "      <th>Duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2 stops</td>\n",
       "      <td>No info</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination                  Route Total_Stops  \\\n",
       "0        3  Banglore   New Delhi              BLR → DEL    non-stop   \n",
       "1        1   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
       "2        4     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
       "\n",
       "  Additional_Info  Price  date  month  year  Dep_Time_hour  Dep_Time_min  \\\n",
       "0         No info   3897    24      3  2019             22            20   \n",
       "1         No info   7662     1      5  2019              5            50   \n",
       "2         No info  13882     9      6  2019              9            25   \n",
       "\n",
       "   Arrival_Time_hour  Arrival_Time_min  Duration_hour  Duration_min  \n",
       "0                  1                10              2            50  \n",
       "1                 13                15              7            25  \n",
       "2                  4                25             19             0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Jet Airways': 4,\n",
       " 'IndiGo': 3,\n",
       " 'Air India': 1,\n",
       " 'Multiple carriers': 6,\n",
       " 'SpiceJet': 8,\n",
       " 'Vistara': 10,\n",
       " 'Air Asia': 0,\n",
       " 'GoAir': 2,\n",
       " 'Multiple carriers Premium economy': 7,\n",
       " 'Jet Airways Business': 5,\n",
       " 'Vistara Premium economy': 11,\n",
       " 'Trujet': 9}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airline_dict={}\n",
    "\n",
    "for i,j in zip(pre_encoder_Airline.keys(), post_encoder_Airline.keys()):\n",
    "    airline_dict.update({i: j})\n",
    "\n",
    "airline_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Source.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Destination.unique()\n",
    "\n",
    "# New Delhi and Delhi should be the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Destination'] = df['Destination'].replace(\"New Delhi\", \"Delhi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Hyderabad'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Destination.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Both Source and Destination features should have same enconding for repective cities\n",
    "# sd_dict refers to Source_Destination Dictionary\n",
    "\n",
    "sd_dict = {'Banglore':0, 'Kolkata':1, 'Delhi':2, 'Chennai':3, 'Mumbai':4,'Cochin':5, 'Hyderabad':6 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Source'] = df['Source'].map(sd_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Destination'] = df['Destination'].map(sd_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_stop_dict = {'non-stop':0,'1 stop':1,'2 stops':2,'3 stops':3,'4 stops':4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total_Stops']=df['Total_Stops'].map(total_stop_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', 'In-flight meal not included',\n",
       "       'No check-in baggage included', '1 Short layover', 'No Info',\n",
       "       '1 Long layover', 'Change airports', 'Business class',\n",
       "       'Red-eye flight', '2 Long layover'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Additional_Info.unique()\n",
    "\n",
    "# 'No info' and 'No Info' should be same category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Additional_Info'] = df['Additional_Info'].replace('No Info', 'No info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No info', 'In-flight meal not included',\n",
       "       'No check-in baggage included', '1 Short layover',\n",
       "       '1 Long layover', 'Change airports', 'Business class',\n",
       "       'Red-eye flight', '2 Long layover'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Additional_Info.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         8184\n",
       "In-flight meal not included     1926\n",
       "No check-in baggage included     318\n",
       "1 Long layover                    19\n",
       "Change airports                    7\n",
       "Business class                     4\n",
       "1 Short layover                    1\n",
       "Red-eye flight                     1\n",
       "2 Long layover                     1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_encoder_Additional_Info = df['Additional_Info'].value_counts()\n",
    "pre_encoder_Additional_Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    8184\n",
       "5    1926\n",
       "6     318\n",
       "0      19\n",
       "4       7\n",
       "3       4\n",
       "1       1\n",
       "8       1\n",
       "2       1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Additional_Info']=labelencoder.fit_transform(df['Additional_Info'])\n",
    "\n",
    "post_encoder_Additional_Info = df['Additional_Info'].value_counts()\n",
    "post_encoder_Additional_Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'No info': 7,\n",
       " 'In-flight meal not included': 5,\n",
       " 'No check-in baggage included': 6,\n",
       " '1 Long layover': 0,\n",
       " 'Change airports': 4,\n",
       " 'Business class': 3,\n",
       " '1 Short layover': 1,\n",
       " 'Red-eye flight': 8,\n",
       " '2 Long layover': 2}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Additional_Info_dict = {}\n",
    "\n",
    "for i,j in zip(pre_encoder_Additional_Info.keys(), post_encoder_Additional_Info.keys()):\n",
    "    Additional_Info_dict.update({i:j})\n",
    "    \n",
    "Additional_Info_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>Duration_hour</th>\n",
       "      <th>Duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3897</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CCU → IXR → BBI → BLR</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>DEL → LKO → BOM → COK</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline  Source  Destination                  Route  Total_Stops  \\\n",
       "0        3       0            2              BLR → DEL            0   \n",
       "1        1       1            0  CCU → IXR → BBI → BLR            2   \n",
       "2        4       2            5  DEL → LKO → BOM → COK            2   \n",
       "\n",
       "   Additional_Info  Price  date  month  year  Dep_Time_hour  Dep_Time_min  \\\n",
       "0                7   3897    24      3  2019             22            20   \n",
       "1                7   7662     1      5  2019              5            50   \n",
       "2                7  13882     9      6  2019              9            25   \n",
       "\n",
       "   Arrival_Time_hour  Arrival_Time_min  Duration_hour  Duration_min  \n",
       "0                  1                10              2            50  \n",
       "1                 13                15              7            25  \n",
       "2                  4                25             19             0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Route', 'Total_Stops',\n",
       "       'Additional_Info', 'Price', 'date', 'month', 'year', 'Dep_Time_hour',\n",
       "       'Dep_Time_min', 'Arrival_Time_hour', 'Arrival_Time_min',\n",
       "       'Duration_hour', 'Duration_min'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <th>Duration_hour</th>\n",
       "      <th>Duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3897</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",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7662</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>13882</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6218</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>13302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10456</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4107</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10457</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4145</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10458</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7229</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10459</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>12648</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10460</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>11753</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10461 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Airline  Source  Destination  Total_Stops  Additional_Info  Price  \\\n",
       "0            3       0            2            0                7   3897   \n",
       "1            1       1            0            2                7   7662   \n",
       "2            4       2            5            2                7  13882   \n",
       "3            3       1            0            1                7   6218   \n",
       "4            3       0            2            1                7  13302   \n",
       "...        ...     ...          ...          ...              ...    ...   \n",
       "10456        0       1            0            0                7   4107   \n",
       "10457        1       1            0            0                7   4145   \n",
       "10458        4       0            2            0                7   7229   \n",
       "10459       10       0            2            0                7  12648   \n",
       "10460        1       2            5            2                7  11753   \n",
       "\n",
       "       date  month  Dep_Time_hour  Dep_Time_min  Arrival_Time_hour  \\\n",
       "0        24      3             22            20                  1   \n",
       "1         1      5              5            50                 13   \n",
       "2         9      6              9            25                  4   \n",
       "3        12      5             18             5                 23   \n",
       "4         1      3             16            50                 21   \n",
       "...     ...    ...            ...           ...                ...   \n",
       "10456     9      4             19            55                 22   \n",
       "10457    27      4             20            45                 23   \n",
       "10458    27      4              8            20                 11   \n",
       "10459     1      3             11            30                 14   \n",
       "10460     9      5             10            55                 19   \n",
       "\n",
       "       Arrival_Time_min  Duration_hour  Duration_min  \n",
       "0                    10              2            50  \n",
       "1                    15              7            25  \n",
       "2                    25             19             0  \n",
       "3                    30              5            25  \n",
       "4                    35              4            45  \n",
       "...                 ...            ...           ...  \n",
       "10456                25              2            30  \n",
       "10457                20              2            35  \n",
       "10458                20              3             0  \n",
       "10459                10              2            40  \n",
       "10460                15              8            20  \n",
       "\n",
       "[10461 rows x 14 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Route', 'year'], axis =1, inplace = True)\n",
    "df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Airline', 'Source', 'Destination', 'Total_Stops', 'Additional_Info',\n",
       "       'Price', 'date', 'month', 'Dep_Time_hour', 'Dep_Time_min',\n",
       "       'Arrival_Time_hour', 'Arrival_Time_min', 'Duration_hour',\n",
       "       'Duration_min'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['date', 'month', 'Dep_Time_hour', 'Dep_Time_min', 'Arrival_Time_hour','Arrival_Time_min', \n",
    "        'Duration_hour','Duration_min','Airline','Source', 'Destination','Total_Stops', 'Additional_Info','Price']\n",
    "\n",
    "df = df.reindex(cols, axis =1)                 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,10))\n",
    "sns.heatmap(df.corr(), annot=True, cmap=\"RdYlGn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>data_type</th>\n",
       "      <th>unique_val</th>\n",
       "      <th>duplicate_val</th>\n",
       "      <th>missing_val</th>\n",
       "      <th>missing_val_%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>int32</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>int32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_hour</th>\n",
       "      <td>int32</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_Time_min</th>\n",
       "      <td>int32</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>int32</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_min</th>\n",
       "      <td>int32</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_hour</th>\n",
       "      <td>int32</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_min</th>\n",
       "      <td>int32</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airline</th>\n",
       "      <td>int32</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <td>int64</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <td>int64</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>int64</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Additional_Info</th>\n",
       "      <td>int32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>int64</td>\n",
       "      <td>1870</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  data_type  unique_val  duplicate_val  missing_val  \\\n",
       "date                  int32          10              2            0   \n",
       "month                 int32           4              2            0   \n",
       "Dep_Time_hour         int32          24              2            0   \n",
       "Dep_Time_min          int32          12              2            0   \n",
       "Arrival_Time_hour     int32          24              2            0   \n",
       "Arrival_Time_min      int32          12              2            0   \n",
       "Duration_hour         int32          43              2            0   \n",
       "Duration_min          int32          12              2            0   \n",
       "Airline               int32          12              2            0   \n",
       "Source                int64           5              2            0   \n",
       "Destination           int64           5              2            0   \n",
       "Total_Stops           int64           5              2            0   \n",
       "Additional_Info       int32           9              2            0   \n",
       "Price                 int64        1870              2            0   \n",
       "\n",
       "                   missing_val_%  \n",
       "date                         0.0  \n",
       "month                        0.0  \n",
       "Dep_Time_hour                0.0  \n",
       "Dep_Time_min                 0.0  \n",
       "Arrival_Time_hour            0.0  \n",
       "Arrival_Time_min             0.0  \n",
       "Duration_hour                0.0  \n",
       "Duration_min                 0.0  \n",
       "Airline                      0.0  \n",
       "Source                       0.0  \n",
       "Destination                  0.0  \n",
       "Total_Stops                  0.0  \n",
       "Additional_Info              0.0  \n",
       "Price                        0.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail_info(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>13302</td>\n",
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       "      <th>10454</th>\n",
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       "      <td>7</td>\n",
       "      <td>4107</td>\n",
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       "      <th>10455</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4145</td>\n",
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       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
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       "<p>10459 rows × 14 columns</p>\n",
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      ],
      "text/plain": [
       "       date  month  Dep_Time_hour  Dep_Time_min  Arrival_Time_hour  \\\n",
       "0        24      3             22            20                  1   \n",
       "1         1      5              5            50                 13   \n",
       "2         9      6              9            25                  4   \n",
       "3        12      5             18             5                 23   \n",
       "4         1      3             16            50                 21   \n",
       "...     ...    ...            ...           ...                ...   \n",
       "10454     9      4             19            55                 22   \n",
       "10455    27      4             20            45                 23   \n",
       "10456    27      4              8            20                 11   \n",
       "10457     1      3             11            30                 14   \n",
       "10458     9      5             10            55                 19   \n",
       "\n",
       "       Arrival_Time_min  Duration_hour  Duration_min  Airline  Source  \\\n",
       "0                    10              2            50        3       0   \n",
       "1                    15              7            25        1       1   \n",
       "2                    25             19             0        4       2   \n",
       "3                    30              5            25        3       1   \n",
       "4                    35              4            45        3       0   \n",
       "...                 ...            ...           ...      ...     ...   \n",
       "10454                25              2            30        0       1   \n",
       "10455                20              2            35        1       1   \n",
       "10456                20              3             0        4       0   \n",
       "10457                10              2            40       10       0   \n",
       "10458                15              8            20        1       2   \n",
       "\n",
       "       Destination  Total_Stops  Additional_Info  Price  \n",
       "0                2            0                7   3897  \n",
       "1                0            2                7   7662  \n",
       "2                5            2                7  13882  \n",
       "3                0            1                7   6218  \n",
       "4                2            1                7  13302  \n",
       "...            ...          ...              ...    ...  \n",
       "10454            0            0                7   4107  \n",
       "10455            0            0                7   4145  \n",
       "10456            2            0                7   7229  \n",
       "10457            2            0                7  12648  \n",
       "10458            5            2                7  11753  \n",
       "\n",
       "[10459 rows x 14 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep='first', inplace=True)\n",
    "df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in df.columns:\n",
    "    df[feature] = df[feature].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arrival_Time_hour</th>\n",
       "      <td>int64</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>Arrival_Time_min</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Duration_hour</th>\n",
       "      <td>int64</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration_min</th>\n",
       "      <td>int64</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Airline</th>\n",
       "      <td>int64</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Source</th>\n",
       "      <td>int64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <td>int64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Stops</th>\n",
       "      <td>int64</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Additional_Info</th>\n",
       "      <td>int64</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>int64</td>\n",
       "      <td>1870</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  data_type  unique_val  duplicate_val  missing_val  \\\n",
       "date                  int64          10              0            0   \n",
       "month                 int64           4              0            0   \n",
       "Dep_Time_hour         int64          24              0            0   \n",
       "Dep_Time_min          int64          12              0            0   \n",
       "Arrival_Time_hour     int64          24              0            0   \n",
       "Arrival_Time_min      int64          12              0            0   \n",
       "Duration_hour         int64          43              0            0   \n",
       "Duration_min          int64          12              0            0   \n",
       "Airline               int64          12              0            0   \n",
       "Source                int64           5              0            0   \n",
       "Destination           int64           5              0            0   \n",
       "Total_Stops           int64           5              0            0   \n",
       "Additional_Info       int64           9              0            0   \n",
       "Price                 int64        1870              0            0   \n",
       "\n",
       "                   missing_val_%  \n",
       "date                         0.0  \n",
       "month                        0.0  \n",
       "Dep_Time_hour                0.0  \n",
       "Dep_Time_min                 0.0  \n",
       "Arrival_Time_hour            0.0  \n",
       "Arrival_Time_min             0.0  \n",
       "Duration_hour                0.0  \n",
       "Duration_min                 0.0  \n",
       "Airline                      0.0  \n",
       "Source                       0.0  \n",
       "Destination                  0.0  \n",
       "Total_Stops                  0.0  \n",
       "Additional_Info              0.0  \n",
       "Price                        0.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detail_info(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['Price']>30000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price']=np.where(df['Price']>=30000,df['Price'].median(),df['Price'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'month', 'Dep_Time_hour', 'Dep_Time_min', 'Arrival_Time_hour',\n",
       "       'Arrival_Time_min', 'Duration_hour', 'Duration_min', 'Airline',\n",
       "       'Source', 'Destination', 'Total_Stops', 'Additional_Info', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8367, 13)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2092, 13)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2092, 13)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExtraTreesRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesRegressor</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Important Features using ExtraTreesRegressor\n",
    "\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "xtr = ExtraTreesRegressor()\n",
    "xtr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.10492757 0.06436787 0.02755851 0.02461992 0.02445387 0.02166416\n",
      " 0.17750423 0.01941174 0.14065778 0.02772322 0.01954887 0.29750071\n",
      " 0.05006154]\n"
     ]
    }
   ],
   "source": [
    "print(xtr.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(xtr.feature_importances_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01941174, 0.01954887, 0.02166416, 0.02445387, 0.02461992,\n",
       "       0.02755851, 0.02772322, 0.05006154, 0.06436787, 0.10492757,\n",
       "       0.14065778, 0.17750423, 0.29750071])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(xtr.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.Series(np.sort(xtr.feature_importances_), index=X_train.columns).plot(kind='barh'))\n",
    "\n",
    "# Additional_Info is most important feature for dependant feature Price as it has the information of Business Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'month', 'Dep_Time_hour', 'Dep_Time_min', 'Arrival_Time_hour',\n",
       "       'Arrival_Time_min', 'Duration_hour', 'Duration_min', 'Airline',\n",
       "       'Source', 'Destination', 'Total_Stops', 'Additional_Info'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso,ElasticNet, RidgeCV,LassoCV,ElasticNetCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = pd.DataFrame(index=['Train_Score', 'Test_Score','CV_Score', \n",
    "                              'Train_Score_Tuned', 'Test_Score_Tuned', \n",
    "                              'MAE_Tuned', 'MSE_Tuned', 'RMSE_Tuned'],\n",
    "                       columns=['LinearReg','LR_Lasso', 'LR_Ridge', 'ElasticNet','KNeighborsReg','DecisionTreeReg', 'RandomForestReg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg LR_Lasso LR_Ridge ElasticNet KNeighborsReg  \\\n",
       "Train_Score             NaN      NaN      NaN        NaN           NaN   \n",
       "Test_Score              NaN      NaN      NaN        NaN           NaN   \n",
       "CV_Score                NaN      NaN      NaN        NaN           NaN   \n",
       "Train_Score_Tuned       NaN      NaN      NaN        NaN           NaN   \n",
       "Test_Score_Tuned        NaN      NaN      NaN        NaN           NaN   \n",
       "MAE_Tuned               NaN      NaN      NaN        NaN           NaN   \n",
       "MSE_Tuned               NaN      NaN      NaN        NaN           NaN   \n",
       "RMSE_Tuned              NaN      NaN      NaN        NaN           NaN   \n",
       "\n",
       "                  DecisionTreeReg RandomForestReg  \n",
       "Train_Score                   NaN             NaN  \n",
       "Test_Score                    NaN             NaN  \n",
       "CV_Score                      NaN             NaN  \n",
       "Train_Score_Tuned             NaN             NaN  \n",
       "Test_Score_Tuned              NaN             NaN  \n",
       "MAE_Tuned                     NaN             NaN  \n",
       "MSE_Tuned                     NaN             NaN  \n",
       "RMSE_Tuned                    NaN             NaN  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "\n",
    "models.append(('LinearReg',LinearRegression()))\n",
    "models.append(('LR_Lasso',Lasso()))\n",
    "models.append(('LR_Ridge',Ridge()))\n",
    "models.append(('ElasticNet',ElasticNet()))\n",
    "\n",
    "models.append(('KNeighborsReg',KNeighborsRegressor()))\n",
    "models.append(('DecisionTreeReg',DecisionTreeRegressor()))\n",
    "models.append(('RandomForestReg',RandomForestRegressor()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('LinearReg', LinearRegression()),\n",
       " ('LR_Lasso', Lasso()),\n",
       " ('LR_Ridge', Ridge()),\n",
       " ('ElasticNet', ElasticNet()),\n",
       " ('KNeighborsReg', KNeighborsRegressor()),\n",
       " ('DecisionTreeReg', DecisionTreeRegressor()),\n",
       " ('RandomForestReg', RandomForestRegressor())]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearReg     [0.47640205 0.49258576 0.48156121 0.48605938 0.45685307] 0.47869229271337377 0.012146851129489137\n",
      "LR_Lasso      [0.47647821 0.49258034 0.48156903 0.48599972 0.45684586] 0.4786946316555379 0.012138541158683402\n",
      "LR_Ridge      [0.4764164  0.49258802 0.48155438 0.48605367 0.45684945] 0.4786923831759706 0.012147115592675426\n",
      "ElasticNet    [0.41825857 0.42586628 0.41143854 0.41327403 0.3872235 ] 0.4112121865683763 0.012993486345295166\n",
      "KNeighborsReg [0.62767313 0.64505669 0.62376548 0.59486063 0.63192956] 0.6246570979797308 0.01653332028372965\n",
      "DecisionTreeReg [0.80556831 0.85274647 0.84831497 0.81050031 0.81302052] 0.8260301177610885 0.020196477691493978\n",
      "RandomForestReg [0.87209216 0.8906979  0.90046236 0.90247377 0.89438277]  0.892021791307451 0.010816691949558262\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    temp_model = model.fit(X_train, y_train)\n",
    "    result = temp_model.score(X_train, y_train)\n",
    "    metrics.loc['Train_Score', name] = round(result*100,4)\n",
    "  \n",
    "    y_pred = temp_model.predict(X_test)\n",
    "    metrics.loc['Test_Score', name] = round(r2_score(y_test,y_pred)*100,4)\n",
    "\n",
    "    cv_score = cross_val_score(model, X_train, y_train, cv = 5, n_jobs=-1, scoring ='r2')\n",
    "    metrics.loc['CV_Score', name] = round(cv_score.mean()*100,4)                                       \n",
    "    print(f\"{name:{13}} {cv_score} {cv_score.mean():{18}} {cv_score.std():{18}}\")                                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg LR_Lasso LR_Ridge ElasticNet KNeighborsReg  \\\n",
       "Train_Score         48.1224  48.1223  48.1224    41.3618       77.9363   \n",
       "Test_Score            47.51  47.5087  47.5106    40.8835       63.4804   \n",
       "CV_Score            47.8692  47.8695  47.8692    41.1212       62.4657   \n",
       "Train_Score_Tuned       NaN      NaN      NaN        NaN           NaN   \n",
       "Test_Score_Tuned        NaN      NaN      NaN        NaN           NaN   \n",
       "MAE_Tuned               NaN      NaN      NaN        NaN           NaN   \n",
       "MSE_Tuned               NaN      NaN      NaN        NaN           NaN   \n",
       "RMSE_Tuned              NaN      NaN      NaN        NaN           NaN   \n",
       "\n",
       "                  DecisionTreeReg RandomForestReg  \n",
       "Train_Score               99.6077         98.3238  \n",
       "Test_Score                81.3536         90.1826  \n",
       "CV_Score                   82.603         89.2022  \n",
       "Train_Score_Tuned             NaN             NaN  \n",
       "Test_Score_Tuned              NaN             NaN  \n",
       "MAE_Tuned                     NaN             NaN  \n",
       "MSE_Tuned                     NaN             NaN  \n",
       "RMSE_Tuned                    NaN             NaN  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LassoCV(cv=10, max_iter=200000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LassoCV</label><div class=\"sk-toggleable__content\"><pre>LassoCV(cv=10, max_iter=200000, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LassoCV(cv=10, max_iter=200000, random_state=42)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lassocv = LassoCV(alphas = None, cv=10, max_iter = 200000, random_state=42)\n",
    "lassocv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.746845097073507"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lassocv.alpha_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_lr = Lasso(alpha= lassocv.alpha_)\n",
    "lasso_lr.fit(X_train, y_train)\n",
    "metrics.loc['Train_Score_Tuned', 'LR_Lasso'] = round(lasso_lr.score(X_train, y_train)*100,4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lasso = lasso_lr.predict(X_test)\n",
    "\n",
    "metrics.loc['Test_Score_Tuned', 'LR_Lasso'] = round(r2_score(y_test, y_pred_lasso)*100,4)\n",
    "metrics.loc['MAE_Tuned', 'LR_Lasso'] = mean_absolute_error(y_test, y_pred_lasso)\n",
    "metrics.loc['MSE_Tuned', 'LR_Lasso'] = mean_squared_error(y_test, y_pred_lasso)\n",
    "metrics.loc['RMSE_Tuned', 'LR_Lasso'] = np.sqrt(mean_squared_error(y_test, y_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg        LR_Lasso LR_Ridge ElasticNet KNeighborsReg  \\\n",
       "Train_Score         48.1224         48.1223  48.1224    41.3618       77.9363   \n",
       "Test_Score            47.51         47.5087  47.5106    40.8835       63.4804   \n",
       "CV_Score            47.8692         47.8695  47.8692    41.1212       62.4657   \n",
       "Train_Score_Tuned       NaN          48.102      NaN        NaN           NaN   \n",
       "Test_Score_Tuned        NaN         47.4654      NaN        NaN           NaN   \n",
       "MAE_Tuned               NaN     2338.543135      NaN        NaN           NaN   \n",
       "MSE_Tuned               NaN  9557540.120462      NaN        NaN           NaN   \n",
       "RMSE_Tuned              NaN      3091.52715      NaN        NaN           NaN   \n",
       "\n",
       "                  DecisionTreeReg RandomForestReg  \n",
       "Train_Score               99.6077         98.3238  \n",
       "Test_Score                81.3536         90.1826  \n",
       "CV_Score                   82.603         89.2022  \n",
       "Train_Score_Tuned             NaN             NaN  \n",
       "Test_Score_Tuned              NaN             NaN  \n",
       "MAE_Tuned                     NaN             NaN  \n",
       "MSE_Tuned                     NaN             NaN  \n",
       "RMSE_Tuned                    NaN             NaN  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgecv = RidgeCV(alphas = np.arange(.1,10,100), cv=10)\n",
    "ridgecv.fit(X_train, y_train)\n",
    "ridgecv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_lr = Ridge(alpha=ridgecv.alpha_)\n",
    "ridge_lr.fit(X_train, y_train)\n",
    "metrics.loc['Train_Score_Tuned', 'LR_Ridge'] = round(ridge_lr.score(X_train,y_train)*100,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rg = ridge_lr.predict(X_test)\n",
    "\n",
    "metrics.loc['Test_Score_Tuned', 'LR_Ridge'] = round(r2_score(y_test, y_pred_rg)*100,4)\n",
    "metrics.loc['MAE_Tuned', 'LR_Ridge'] = mean_absolute_error(y_test, y_pred_rg)\n",
    "metrics.loc['MSE_Tuned', 'LR_Ridge'] = mean_squared_error(y_test, y_pred_rg)\n",
    "metrics.loc['RMSE_Tuned', 'LR_Ridge'] = np.sqrt(mean_squared_error(y_test, y_pred_rg))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.102</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>47.5101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>2340.56381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>9549406.814959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>3090.211451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg        LR_Lasso        LR_Ridge ElasticNet  \\\n",
       "Train_Score         48.1224         48.1223         48.1224    41.3618   \n",
       "Test_Score            47.51         47.5087         47.5106    40.8835   \n",
       "CV_Score            47.8692         47.8695         47.8692    41.1212   \n",
       "Train_Score_Tuned       NaN          48.102         48.1224        NaN   \n",
       "Test_Score_Tuned        NaN         47.4654         47.5101        NaN   \n",
       "MAE_Tuned               NaN     2338.543135      2340.56381        NaN   \n",
       "MSE_Tuned               NaN  9557540.120462  9549406.814959        NaN   \n",
       "RMSE_Tuned              NaN      3091.52715     3090.211451        NaN   \n",
       "\n",
       "                  KNeighborsReg DecisionTreeReg RandomForestReg  \n",
       "Train_Score             77.9363         99.6077         98.3238  \n",
       "Test_Score              63.4804         81.3536         90.1826  \n",
       "CV_Score                62.4657          82.603         89.2022  \n",
       "Train_Score_Tuned           NaN             NaN             NaN  \n",
       "Test_Score_Tuned            NaN             NaN             NaN  \n",
       "MAE_Tuned                   NaN             NaN             NaN  \n",
       "MSE_Tuned                   NaN             NaN             NaN  \n",
       "RMSE_Tuned                  NaN             NaN             NaN  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.746845097073507"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Theory Lambda = Alpha\n",
    "# Thoery Alpha = l1_ratio\n",
    "\n",
    "elastic = ElasticNetCV(alphas = None, l1_ratio= np.arange(0.1,1.1,.1), cv =10)\n",
    "elastic.fit(X_train, y_train)\n",
    "elastic.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.l1_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic_lr = ElasticNet(alpha = elastic.alpha_, l1_ratio = elastic.l1_ratio_)\n",
    "elastic_lr.fit(X_train, y_train)\n",
    "metrics.loc['Train_Score_Tuned', 'ElasticNet'] = round(elastic_lr.score(X_train, y_train)*100,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_elastic = elastic_lr.predict(X_test)\n",
    "\n",
    "metrics.loc['Test_Score_Tuned', 'ElasticNet'] = round(r2_score(y_test, y_pred_elastic)*100,4)\n",
    "metrics.loc['MAE_Tuned', 'ElasticNet'] = mean_absolute_error(y_test, y_pred_elastic)\n",
    "metrics.loc['MSE_Tuned', 'ElasticNet'] = mean_squared_error(y_test, y_pred_elastic)\n",
    "metrics.loc['RMSE_Tuned', 'ElasticNet'] = np.sqrt(mean_squared_error(y_test, y_pred_elastic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.102</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>47.5101</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>2340.56381</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>9549406.814959</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>3090.211451</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg        LR_Lasso        LR_Ridge      ElasticNet  \\\n",
       "Train_Score         48.1224         48.1223         48.1224         41.3618   \n",
       "Test_Score            47.51         47.5087         47.5106         40.8835   \n",
       "CV_Score            47.8692         47.8695         47.8692         41.1212   \n",
       "Train_Score_Tuned       NaN          48.102         48.1224          48.102   \n",
       "Test_Score_Tuned        NaN         47.4654         47.5101         47.4654   \n",
       "MAE_Tuned               NaN     2338.543135      2340.56381     2338.543135   \n",
       "MSE_Tuned               NaN  9557540.120462  9549406.814959  9557540.120462   \n",
       "RMSE_Tuned              NaN      3091.52715     3090.211451      3091.52715   \n",
       "\n",
       "                  KNeighborsReg DecisionTreeReg RandomForestReg  \n",
       "Train_Score             77.9363         99.6077         98.3238  \n",
       "Test_Score              63.4804         81.3536         90.1826  \n",
       "CV_Score                62.4657          82.603         89.2022  \n",
       "Train_Score_Tuned           NaN             NaN             NaN  \n",
       "Test_Score_Tuned            NaN             NaN             NaN  \n",
       "MAE_Tuned                   NaN             NaN             NaN  \n",
       "MSE_Tuned                   NaN             NaN             NaN  \n",
       "RMSE_Tuned                  NaN             NaN             NaN  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'leaf_size': 10,\n",
       " 'metric': 'minkowski',\n",
       " 'n_jobs': -1,\n",
       " 'n_neighbors': 2,\n",
       " 'p': 1,\n",
       " 'weights': 'uniform'}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNeighbor\n",
    "\n",
    "param_grid = {'n_neighbors': np.arange(2,25,1),\n",
    "              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
    "              'weights' : ['uniform', 'distance'],\n",
    "              'leaf_size': [10,20,30],\n",
    "              'p' : [1,2],\n",
    "              'metric': ['minkowski'],\n",
    "              'n_jobs' : [-1]\n",
    "                }\n",
    "\n",
    "\n",
    "kn_model = GridSearchCV(KNeighborsRegressor(), param_grid, cv =3, scoring='accuracy')\n",
    "kn_model.fit(X_train,y_train)\n",
    "kn_model.best_params_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#{'algorithm': 'auto', 'leaf_size': 10, 'metric': 'minkowski', 'n_jobs': -1, 'n_neighbors': 2, 'p': 1, 'weights': 'uniform'}# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6950139833622448"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kn_model = KNeighborsRegressor(n_neighbors= 2, algorithm= 'auto', weights= 'uniform', leaf_size= 10, \n",
    "                               p =1 , metric= 'minkowski', n_jobs = -1)\n",
    "kn_model.fit(X_train,y_train)\n",
    "y_pred_kn = kn_model.predict(X_test)\n",
    "r2_score(y_test, y_pred_kn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.102</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.102</td>\n",
       "      <td>90.0492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>47.5101</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>69.5014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>2340.56381</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>1460.282266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>9549406.814959</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>5548562.639221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>3090.211451</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>2355.538715</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg        LR_Lasso        LR_Ridge      ElasticNet  \\\n",
       "Train_Score         48.1224         48.1223         48.1224         41.3618   \n",
       "Test_Score            47.51         47.5087         47.5106         40.8835   \n",
       "CV_Score            47.8692         47.8695         47.8692         41.1212   \n",
       "Train_Score_Tuned       NaN          48.102         48.1224          48.102   \n",
       "Test_Score_Tuned        NaN         47.4654         47.5101         47.4654   \n",
       "MAE_Tuned               NaN     2338.543135      2340.56381     2338.543135   \n",
       "MSE_Tuned               NaN  9557540.120462  9549406.814959  9557540.120462   \n",
       "RMSE_Tuned              NaN      3091.52715     3090.211451      3091.52715   \n",
       "\n",
       "                    KNeighborsReg DecisionTreeReg RandomForestReg  \n",
       "Train_Score               77.9363         99.6077         98.3238  \n",
       "Test_Score                63.4804         81.3536         90.1826  \n",
       "CV_Score                  62.4657          82.603         89.2022  \n",
       "Train_Score_Tuned         90.0492             NaN             NaN  \n",
       "Test_Score_Tuned          69.5014             NaN             NaN  \n",
       "MAE_Tuned             1460.282266             NaN             NaN  \n",
       "MSE_Tuned          5548562.639221             NaN             NaN  \n",
       "RMSE_Tuned            2355.538715             NaN             NaN  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.loc['Train_Score_Tuned', 'KNeighborsReg'] = round(kn_model.score(X_train,  y_train)*100,4)\n",
    "metrics.loc['Test_Score_Tuned' , 'KNeighborsReg'] = round(r2_score(y_test,y_pred_kn)*100,4)\n",
    "\n",
    "metrics.loc['MAE_Tuned', 'KNeighborsReg'] = mean_absolute_error(y_test, y_pred_kn)\n",
    "metrics.loc['MSE_Tuned', 'KNeighborsReg'] = mean_squared_error(y_test, y_pred_kn)\n",
    "metrics.loc['RMSE_Tuned', 'KNeighborsReg'] = np.sqrt(mean_squared_error(y_test, y_pred_kn))\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'squared_error',\n",
       " 'max_depth': 10,\n",
       " 'max_features': 10,\n",
       " 'min_samples_leaf': 7,\n",
       " 'random_state': 42,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "param_grid = {'criterion' : ['squared_error', 'absolute_error'],\n",
    "              'splitter': ['best', 'random'],\n",
    "              'max_depth' : [2,4,6,8,10],\n",
    "              'min_samples_leaf': np.arange(1,10,1),\n",
    "              'max_features' : [2,4,6,8,10],\n",
    "              'random_state' : [42]\n",
    "             } \n",
    "\n",
    "\n",
    "dt_model = GridSearchCV(DecisionTreeRegressor(), param_grid, cv =5, scoring='r2')\n",
    "dt_model.fit(X_train,y_train)\n",
    "dt_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#{'criterion': 'squared_error', 'max_depth': 10, 'max_features': 10, 'min_samples_leaf': 5, 'random_state': 42, 'splitter': 'best'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8423923833102058"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model = DecisionTreeRegressor(criterion= 'squared_error', max_depth= 10, max_features= 10, min_samples_leaf= 5, splitter = 'best', random_state=42)\n",
    "dt_model.fit(X_train,y_train)\n",
    "y_pred_dt = dt_model.predict(X_test)\n",
    "r2_score(y_test, y_pred_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.102</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.102</td>\n",
       "      <td>90.0492</td>\n",
       "      <td>88.8799</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>47.5101</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>69.5014</td>\n",
       "      <td>84.2392</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>2340.56381</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>1460.282266</td>\n",
       "      <td>992.976914</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>9549406.814959</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>5548562.639221</td>\n",
       "      <td>2867330.58539</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>3090.211451</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>2355.538715</td>\n",
       "      <td>1693.319399</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg        LR_Lasso        LR_Ridge      ElasticNet  \\\n",
       "Train_Score         48.1224         48.1223         48.1224         41.3618   \n",
       "Test_Score            47.51         47.5087         47.5106         40.8835   \n",
       "CV_Score            47.8692         47.8695         47.8692         41.1212   \n",
       "Train_Score_Tuned       NaN          48.102         48.1224          48.102   \n",
       "Test_Score_Tuned        NaN         47.4654         47.5101         47.4654   \n",
       "MAE_Tuned               NaN     2338.543135      2340.56381     2338.543135   \n",
       "MSE_Tuned               NaN  9557540.120462  9549406.814959  9557540.120462   \n",
       "RMSE_Tuned              NaN      3091.52715     3090.211451      3091.52715   \n",
       "\n",
       "                    KNeighborsReg DecisionTreeReg RandomForestReg  \n",
       "Train_Score               77.9363         99.6077         98.3238  \n",
       "Test_Score                63.4804         81.3536         90.1826  \n",
       "CV_Score                  62.4657          82.603         89.2022  \n",
       "Train_Score_Tuned         90.0492         88.8799             NaN  \n",
       "Test_Score_Tuned          69.5014         84.2392             NaN  \n",
       "MAE_Tuned             1460.282266      992.976914             NaN  \n",
       "MSE_Tuned          5548562.639221   2867330.58539             NaN  \n",
       "RMSE_Tuned            2355.538715     1693.319399             NaN  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.loc['Train_Score_Tuned', 'DecisionTreeReg'] = round(dt_model.score(X_train, y_train)*100,4)\n",
    "metrics.loc['Test_Score_Tuned' , 'DecisionTreeReg'] = round(r2_score(y_test,y_pred_dt)*100,4)\n",
    "\n",
    "metrics.loc['MAE_Tuned', 'DecisionTreeReg'] = mean_absolute_error(y_test, y_pred_dt)\n",
    "metrics.loc['MSE_Tuned', 'DecisionTreeReg'] = mean_squared_error(y_test, y_pred_dt)\n",
    "metrics.loc['RMSE_Tuned', 'DecisionTreeReg'] = np.sqrt(mean_squared_error(y_test, y_pred_dt))\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'squared_error',\n",
       " 'max_depth': 5,\n",
       " 'max_features': 5,\n",
       " 'min_samples_leaf': 5,\n",
       " 'n_estimators': 10,\n",
       " 'n_jobs': -1,\n",
       " 'oob_score': True,\n",
       " 'random_state': 42}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "param_grid = {'criterion' : ['squared_error', 'absolute_error'],\n",
    "        'n_estimators' : [10,100,200,300],\n",
    "        'max_depth' : [5,10,15,20],\n",
    "        'max_features':  [5,6,7,8,9,10],\n",
    "        'min_samples_leaf': [5,6,7,8,9,10],\n",
    "        'oob_score' : [True,False],\n",
    "        'n_jobs': [-1],\n",
    "        'random_state': [42]\n",
    "        }\n",
    "\n",
    "rf_model = GridSearchCV(RandomForestRegressor(), param_grid, scoring='accuracy')\n",
    "rf_model.fit(X_train,y_train)\n",
    "rf_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"{'criterion': 'squared_error', 'max_depth': 5, 'max_features': 5, 'max_leaf_nodes': 5, 'n_estimators': 10,\\n                 'n_jobs': -1, 'oob_score': True, 'random_state': 42}\\n                 \\n\""
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"{'criterion': 'squared_error', 'max_depth': 5, 'max_features': 5, 'max_leaf_nodes': 5, 'n_estimators': 10,\n",
    "                 'n_jobs': -1, 'oob_score': True, 'random_state': 42}\n",
    "                 \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8799044964635007"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor(criterion= 'squared_error', n_estimators= 300, max_depth= 15, max_features= 9, min_samples_leaf= 10, oob_score=False)\n",
    "rf_model.fit(X_train,y_train)\n",
    "y_pred_rf = rf_model.predict(X_test)\n",
    "r2_score(y_test, y_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>LinearReg</th>\n",
       "      <th>LR_Lasso</th>\n",
       "      <th>LR_Ridge</th>\n",
       "      <th>ElasticNet</th>\n",
       "      <th>KNeighborsReg</th>\n",
       "      <th>DecisionTreeReg</th>\n",
       "      <th>RandomForestReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train_Score</th>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.1223</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>41.3618</td>\n",
       "      <td>77.9363</td>\n",
       "      <td>99.6077</td>\n",
       "      <td>98.3238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score</th>\n",
       "      <td>47.51</td>\n",
       "      <td>47.5087</td>\n",
       "      <td>47.5106</td>\n",
       "      <td>40.8835</td>\n",
       "      <td>63.4804</td>\n",
       "      <td>81.3536</td>\n",
       "      <td>90.1826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV_Score</th>\n",
       "      <td>47.8692</td>\n",
       "      <td>47.8695</td>\n",
       "      <td>47.8692</td>\n",
       "      <td>41.1212</td>\n",
       "      <td>62.4657</td>\n",
       "      <td>82.603</td>\n",
       "      <td>89.2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48.102</td>\n",
       "      <td>48.1224</td>\n",
       "      <td>48.102</td>\n",
       "      <td>90.0492</td>\n",
       "      <td>88.8799</td>\n",
       "      <td>89.6309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Test_Score_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>47.5101</td>\n",
       "      <td>47.4654</td>\n",
       "      <td>69.5014</td>\n",
       "      <td>84.2392</td>\n",
       "      <td>87.9904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>2340.56381</td>\n",
       "      <td>2338.543135</td>\n",
       "      <td>1460.282266</td>\n",
       "      <td>992.976914</td>\n",
       "      <td>902.185218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>9549406.814959</td>\n",
       "      <td>9557540.120462</td>\n",
       "      <td>5548562.639221</td>\n",
       "      <td>2867330.58539</td>\n",
       "      <td>2184878.609869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RMSE_Tuned</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>3090.211451</td>\n",
       "      <td>3091.52715</td>\n",
       "      <td>2355.538715</td>\n",
       "      <td>1693.319399</td>\n",
       "      <td>1478.133489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  LinearReg        LR_Lasso        LR_Ridge      ElasticNet  \\\n",
       "Train_Score         48.1224         48.1223         48.1224         41.3618   \n",
       "Test_Score            47.51         47.5087         47.5106         40.8835   \n",
       "CV_Score            47.8692         47.8695         47.8692         41.1212   \n",
       "Train_Score_Tuned       NaN          48.102         48.1224          48.102   \n",
       "Test_Score_Tuned        NaN         47.4654         47.5101         47.4654   \n",
       "MAE_Tuned               NaN     2338.543135      2340.56381     2338.543135   \n",
       "MSE_Tuned               NaN  9557540.120462  9549406.814959  9557540.120462   \n",
       "RMSE_Tuned              NaN      3091.52715     3090.211451      3091.52715   \n",
       "\n",
       "                    KNeighborsReg DecisionTreeReg RandomForestReg  \n",
       "Train_Score               77.9363         99.6077         98.3238  \n",
       "Test_Score                63.4804         81.3536         90.1826  \n",
       "CV_Score                  62.4657          82.603         89.2022  \n",
       "Train_Score_Tuned         90.0492         88.8799         89.6309  \n",
       "Test_Score_Tuned          69.5014         84.2392         87.9904  \n",
       "MAE_Tuned             1460.282266      992.976914      902.185218  \n",
       "MSE_Tuned          5548562.639221   2867330.58539  2184878.609869  \n",
       "RMSE_Tuned            2355.538715     1693.319399     1478.133489  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.loc['Train_Score_Tuned', 'RandomForestReg'] = round(rf_model.score(X_train, y_train)*100,4)\n",
    "metrics.loc['Test_Score_Tuned' , 'RandomForestReg'] = round(r2_score(y_test,y_pred_rf)*100,4)\n",
    "\n",
    "metrics.loc['MAE_Tuned', 'RandomForestReg'] = mean_absolute_error(y_test, y_pred_rf)\n",
    "metrics.loc['MSE_Tuned', 'RandomForestReg'] = mean_squared_error(y_test, y_pred_rf)\n",
    "metrics.loc['RMSE_Tuned', 'RandomForestReg'] = np.sqrt(mean_squared_error(y_test, y_pred_rf))\n",
    "\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(rf_model, open('AirlinePrice.pkl','wb'))"
   ]
  }
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