FlightFarePrediction.ipynb

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  "metadata": {
    "colab": {
      "name": "FlightFarePrediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILnyY-lqep1k"
      },
      "source": [
        "## **Import packages**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3zM1RLqEbWH"
      },
      "source": [
        "from google.colab import files\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV\n",
        "from sklearn.tree import DecisionTreeRegressor, ExtraTreeRegressor, export_graphviz\n",
        "from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "from IPython.display import Image\n",
        "from sklearn.svm import SVR\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
        "from sklearn.externals.six import StringIO\n",
        "import io\n",
        "import pydotplus\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "# ! pip install graphviz pydotplus"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3KRUvwEe6tK"
      },
      "source": [
        "## **Dataset loading**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSg4xEMRKU_m"
      },
      "source": [
        "uploaded = files.upload()\n",
        "train_df = pd.read_csv(io.BytesIO(uploaded['train.csv']))\n",
        "uploaded = files.upload()\n",
        "test_df = pd.read_csv(io.BytesIO(uploaded['test.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBRav5JAReks"
      },
      "source": [
        "## **Feature Engineering**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "barfMkKnJOYn",
        "outputId": "06d5d0e5-73c1-408b-ab2b-e080348879a4"
      },
      "source": [
        "train_df.shape, test_df.shape #getting the shape of the dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10683, 11), (2671, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2nRLSSWTN0y8",
        "outputId": "cce41630-4b13-417b-b1a1-736c8e57ca7d"
      },
      "source": [
        "train_df.head() #showing first 5 data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>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  ... Total_Stops Additional_Info  Price\n",
              "0       IndiGo      24/03/2019  Banglore  ...    non-stop         No info   3897\n",
              "1    Air India       1/05/2019   Kolkata  ...     2 stops         No info   7662\n",
              "2  Jet Airways       9/06/2019     Delhi  ...     2 stops         No info  13882\n",
              "3       IndiGo      12/05/2019   Kolkata  ...      1 stop         No info   6218\n",
              "4       IndiGo      01/03/2019  Banglore  ...      1 stop         No info  13302\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "bG5RJ8-EN-9c",
        "outputId": "3eb387cc-76e0-453b-8cb7-f27138622b68"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10683.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9087.064121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4611.359167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1759.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5277.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8372.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12373.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>79512.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Price\n",
              "count  10683.000000\n",
              "mean    9087.064121\n",
              "std     4611.359167\n",
              "min     1759.000000\n",
              "25%     5277.000000\n",
              "50%     8372.000000\n",
              "75%    12373.000000\n",
              "max    79512.000000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cJfaqKy3OOls",
        "outputId": "521a226b-2e5c-4e53-9758-2a2e49d267d2"
      },
      "source": [
        "train_df.isnull().sum() #counting null values in each column"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              1\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        1\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fx-SEWwoO-SD",
        "outputId": "6b85fcf9-9d3f-4249-b6ea-48b084abbee5"
      },
      "source": [
        "train_df = train_df.dropna() #dropping null rows\n",
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10682, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3j6SwEFPY5z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "673575a7-bfd6-4649-c882-eb30cab4a09c"
      },
      "source": [
        "#extracting features from data\n",
        "\n",
        "train_df['Day'], train_df['Month'], train_df['Year'] = train_df['Date_of_Journey'].str.split('/', 3).str\n",
        "train_df['Dep_Hr'], train_df['Dep_Min'] = train_df['Dep_Time'].str.split(':', 2).str\n",
        "\n",
        "print(train_df['Arrival_Time'].unique())\n",
        "#taking hours\n",
        "train_df['Arrival_Time'], _ = train_df['Arrival_Time'].str.split(' ', 1).str\n",
        "train_df['Arr_Hr'], train_df['Arr_Min'] = train_df['Arrival_Time'].str.split(':', 2).str\n",
        "train_df['Duration'] = train_df['Duration'].str.replace('h ', ':').str.replace('m', '')\n",
        "train_df['Duration_Hr'], train_df['Duration_Min'] = train_df['Duration'].str.split(':', 2).str\n",
        "#taking unique stops\n",
        "print(train_df['Total_Stops'].unique())\n",
        "\n",
        "train_df['Total_Stops'], _ = train_df['Total_Stops'].str.split(' stops').str\n",
        "train_df['Total_Stops'], _ = train_df['Total_Stops'].str.split(' stop').str\n",
        "train_df['Total_Stops'] = train_df['Total_Stops'].apply(lambda x: 0 if 'non-stop' in x else x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['01:10 22 Mar' '13:15' '04:25 10 Jun' ... '06:50 10 Mar' '00:05 19 Mar'\n",
            " '21:20 13 Mar']\n",
            "['non-stop' '2 stops' '1 stop' '3 stops' '4 stops']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOrxrybkZc8D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de16b435-3ba5-482b-ae33-2b6d4e3ed1b8"
      },
      "source": [
        "train_cleaned_df = train_df.copy() #copying cleaned df\n",
        "train_cleaned_df.drop(['Date_of_Journey', 'Dep_Time', 'Arrival_Time', 'Duration', 'Additional_Info'], axis = 1, inplace = True)\n",
        "#showing new dataset shape\n",
        "train_df.shape, train_cleaned_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10682, 20), (10682, 15))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ro-Fa6DCapBh"
      },
      "source": [
        "train_cleaned_df.to_csv('train_cleaned.csv', index = False) # generating new csv dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EqDAyStno80h"
      },
      "source": [
        "## **Data Visualization**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "VX4jTfNZpBQ-",
        "outputId": "99b833a1-ca2c-482c-fd16-0e906f476c5f"
      },
      "source": [
        "train_cleaned_df = pd.read_csv('train_cleaned.csv') #loading dataset\n",
        "train_cleaned_df.head() #showing first 5 data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Dep_Hr</th>\n",
              "      <th>Dep_Min</th>\n",
              "      <th>Arr_Hr</th>\n",
              "      <th>Arr_Min</th>\n",
              "      <th>Duration_Hr</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>0</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>50.0</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</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>25.0</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</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>19h</td>\n",
              "      <td>NaN</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</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>25.0</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</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>45.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline    Source Destination  ... Arr_Min  Duration_Hr  Duration_Min\n",
              "0       IndiGo  Banglore   New Delhi  ...      10            2          50.0\n",
              "1    Air India   Kolkata    Banglore  ...      15            7          25.0\n",
              "2  Jet Airways     Delhi      Cochin  ...      25          19h           NaN\n",
              "3       IndiGo   Kolkata    Banglore  ...      30            5          25.0\n",
              "4       IndiGo  Banglore   New Delhi  ...      35            4          45.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "sjd1Iu1ipz42",
        "outputId": "6dfdd676-a701-429c-fbf3-3478a8c4f8f0"
      },
      "source": [
        "train_cleaned_df.describe()  #info about the dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Total_Stops</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Dep_Hr</th>\n",
              "      <th>Dep_Min</th>\n",
              "      <th>Arr_Hr</th>\n",
              "      <th>Arr_Min</th>\n",
              "      <th>Duration_Min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.0</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>10682.000000</td>\n",
              "      <td>9650.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.824190</td>\n",
              "      <td>9087.214567</td>\n",
              "      <td>13.509081</td>\n",
              "      <td>4.708575</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>12.491013</td>\n",
              "      <td>24.409287</td>\n",
              "      <td>13.349186</td>\n",
              "      <td>24.690601</td>\n",
              "      <td>31.35544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.675229</td>\n",
              "      <td>4611.548810</td>\n",
              "      <td>8.479363</td>\n",
              "      <td>1.164408</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.748820</td>\n",
              "      <td>18.767801</td>\n",
              "      <td>6.859317</td>\n",
              "      <td>16.506808</td>\n",
              "      <td>14.93004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1759.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>5277.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>20.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8372.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>30.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>12373.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>45.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>79512.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>55.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Total_Stops         Price  ...       Arr_Min  Duration_Min\n",
              "count  10682.000000  10682.000000  ...  10682.000000    9650.00000\n",
              "mean       0.824190   9087.214567  ...     24.690601      31.35544\n",
              "std        0.675229   4611.548810  ...     16.506808      14.93004\n",
              "min        0.000000   1759.000000  ...      0.000000       5.00000\n",
              "25%        0.000000   5277.000000  ...     10.000000      20.00000\n",
              "50%        1.000000   8372.000000  ...     25.000000      30.00000\n",
              "75%        1.000000  12373.000000  ...     35.000000      45.00000\n",
              "max        4.000000  79512.000000  ...     55.000000      55.00000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "DuikCaoiqX5G",
        "outputId": "c2b48c16-fac8-48c3-a921-9ed2c04277bb"
      },
      "source": [
        "#plotting corelation with the price\n",
        "plt.figure(figsize = (12, 6))\n",
        "train_cleaned_df.corr()['Price'].sort_values().plot(kind = 'bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f421dec7c50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "a6ew0KourSY2",
        "outputId": "a92c5d06-3b7d-42f1-ce82-9825ecc1d865"
      },
      "source": [
        "#Plotting airlines with count\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.countplot(train_cleaned_df['Airline'], palette = 'Set3')\n",
        "plt.title('Count of Airlines', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "_ndBlg8pryZX",
        "outputId": "5fed36ef-9760-45ad-a429-da6f7ba24e05"
      },
      "source": [
        "#Airlines vs Price \n",
        "plt.figure(figsize = (12,6))\n",
        "sns.boxplot(x='Airline', y='Price', data=train_cleaned_df, palette='rainbow')\n",
        "plt.title('Airlines vs Price', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "ddErsL0ssaT5",
        "outputId": "e241a2d2-8944-4982-ac4d-f2ccb0758ecc"
      },
      "source": [
        "#Count of Source plotting\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.countplot(train_cleaned_df['Source'], palette = 'Set2')\n",
        "plt.title('Count of Source', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VJEmSJM17a+vRBqCqDk/yGGDp6HOq6phGdUmSJEnz2oyCdpJjgQcC5wO39c0FGLQlSZKkKcwoaAPLgJ2rqloWI0mSJI2Lmc6j/T3g91oWIkmSJI2TmfZobw1cnOTbwC0TjVX1zCZVSZIkSfPcTIP2O1oWIUmSJI2bmc46clbrQiRJkqRxMtNZR26im2UEYBNgY+Dmqtq8VWGSJEnSfDbTHu17TWwnCbAfsEeroiRJkqT5bqazjtyhOv8G7NOgHkmSJGkszHToyLNHdjeim1f7V00qkiRJksbATGcdecbI9q3AFXTDRyRJkiRNYaZjtF/WuhBJkiRpnMxojHaS7ZJ8IcnK/vH5JNu1Lk6SJEmar2Z6M+QngROB+/ePL/ZtkiRJkqYw06C9pKo+WVW39o9PAUsa1iVJkiTNazMN2tclOSDJov5xAHBdy8IkSZKk+WymQfvlwPOAnwDXAM8BXtqoJkmSJGnem+n0fu8CDqyq6wGS3Bv4v3QBXJIkSdIkM+3RfsREyAaoqtXArm1KkiRJkua/mQbtjZJsNbHT92jPtDdckiRJWnBmGpY/APxnks/1+88F3tumpOG86cvHDF2CZugDT33J0CVIkiSt0UxXhjwmyXJgr77p2VV1cbuyJEmSpPltxsM/+mBtuJYkSZJmYKZjtCVJkiStA4O2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ00C9pJtk9yRpKLk1yU5HV9+72TnJrkB/3Prfr2JPlIkhVJLkiy28i1DuzP/0GSA1vVLEmSJM2Wlj3atwJvqqqdgT2AQ5LsDLwVOK2qdgJO6/cBngrs1D8OBj4Gdyz3fijwKGB34NDR5eAlSZKkuahZ0K6qa6rqO/32TcAlwLbAfsDR/WlHA/v32/sBx1TnW8CWSe4H7AOcWlWrq+p64FRg31Z1S5IkSbNhg4zRTrIU2BU4G9imqq7pD/0E2Kbf3ha4cuRpV/Vt07VLkiRJc1bzoJ1kM+DzwOur6sbRY1VVQM3S6xycZHmS5atWrZqNS0qSJEnrrWnQTrIxXcj+dFX9a9/8035ICP3PlX371cD2I0/frm+brv23VNURVbWsqpYtWbJkdt+IJEmStI5azjoS4Ejgkqr64MihE4GJmUMOBE4YaX9JP/vIHsAN/RCTk4GnJNmqvwnyKX2bJEmSNGctbnjtPYEXAxcmOb9v+yvgfcDxSQ4Cfgw8rz92EvAnwArgF8DLAKpqdZJ3A+f0572rqlY3rFuSJEm6y5oF7ar6BpBpDu89xfkFHDLNtY4Cjpq96iRJkqS2XBlSkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ00C9pJjkqyMsn3RtruneTUJD/of27VtyfJR5KsSHJBkt1GnnNgf/4PkhzYql5JkiRpNrXs0f4UsO+ktrcCp1XVTsBp/T7AU4Gd+sfBwMegC+bAocCjgN2BQyfCuSRJkjSXNQvaVfU1YPWk5v2Ao/vto4H9R9qPqc63gC2T3A/YBzi1qlZX1fXAqfxueJckSZLmnA09Rnubqrqm3/4JsE2/vS1w5ch5V/Vt07VLkiRJc9pgN0NWVQE1W9dLcnCS5UmWr1q1arYuK0mSJK2XDR20f9oPCaH/ubJvvxrYfuS87fq26dp/R1UdUVXLqmrZkiVLZr1wSZIkaV1s6KB9IjAxc8iBwAkj7S/pZx/ZA7ihH2JyMvCUJFv1N0E+pW+TJEmS5rTFrS6c5DPAE4Ctk1xFN3vI+4DjkxwE/Bh4Xn/6ScCfACuAXwAvA6iq1UneDZzTn/euqpp8g6UkSZI05zQL2lX1wmkO7T3FuQUcMs11jgKOmsXSJEmSpOZcGVKSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpgcVDFyBJkjSujj374KFL0Dp48aOOmNXr2aMtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSA4uHLkCSpHGw4mNnDV2C1sGDXv34oUvQAmCPtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWrAoC1JkiQ1YNCWJEmSGjBoS5IkSQ3Mm6CdZN8klyVZkeStQ9cjSZIkrcm8CNpJFgF/DzwV2Bl4YZKdh61KkiRJmt68CNrA7sCKqrq8qn4NHAfsN3BNkiRJ0rTmS9DeFrhyZP+qvk2SJEmak1JVQ9ewVkmeA+xbVa/o918MPKqqXjtyzsHAwf3ug4HLNnihc9PWwLVDF6E5x8+FpuLnQlPxc6Gp+Lm40w5VtWSqA4s3dCXr6Wpg+5H97fq2O1TVEcARG7Ko+SDJ8qpaNnQdmlv8XGgqfi40FT8Xmoqfi5mZL0NHzgF2SrJjkk2AFwAnDlyTJEmSNK150aNdVbcmeS1wMrAIOKqqLhq4LEmSJGla8yJoA1TVScBJQ9cxDzmcRlPxc6Gp+LnQVPxcaCp+LmZgXtwMKUmSJM0382WMtiRJkjSvGLQlSZKkBgzakiRJUgPz5mZIrbsk96iqXwxdh6S5J8nmVXVjkntPdbyqVm/omjS8JA+pqkuT7DbV8ar6zoauSZrPvBlyDCV5DPAJYLOq+v0kuwCvqqrXDFyaBpRkCfAWYGdg04n2qtprsKI0mCRfqqqnJ/kRUEBGDldVPWCg0jSgJEdU1cFJzpjicPnfi4UpyTeq6rFJbqL778Udh+g+F5sPVNqcZ9AeQ0nOBp4DnFhVu/Zt36uqhw1bmYaU5BTgs8BfAH8GHAisqqq3DFqYJEljyqEjY6qqrkxGO6i4bahaNGfcp6qOTPK6qjoLOCvJOUMXpeEl2RbYgZF/E6rqa8NVpLkgycP43W/AjhmuIs0VSe7Lb38u/mvAcuY0g/Z4urIfPlJJNgZeB1wycE0a3m/6n9ckeRrw38CU43O1cCT5W+D5wMXc+Qd5AQbtBSzJocAT6IL2ScBTgW8ABu0FLMkzgQ8A9wdW0v2Bfgnw0CHrmsscOjKGkmwNfBh4Et34qVOA11XVdYMWpkEleTrwdWB74HBgc+AdVfXFQQvToJJcBjyiqm4ZuhbNHUkuBHYBzquqXZJsA/xTVT154NI0oCTfBfYCvlpVuyZ5InBAVR00cGlzlj3aYybJIuDDVfWioWvRnHN9Vd0A3AA8ESDJnsOWpDngcmBjwKCtUb+sqtuT3Jpkc7rey+2HLkqD+01VXZdkoyQbVdUZST40dFFzmUF7zFTVbUl2SLJJVf166Ho0pxwOTJ6ya6o2LQBJDqcbIvIL4PwkpzEStqvqz4eqTXPC8iRbAv8InAv8HPjPYUvSHPCzJJvRfTv66SQrgZsHrmlOc+jIGEpyDPAHwImM/B+gqj44WFEaTJJHA48BXg8cNnJoc+BZVbXLIIVpUEkOXNPxqjp6Q9WiuS3JUmDzqrpg4FI0sCT3BH5FNyz1RcAWwKcdmjo9e7TH0w/7x0bAvQauRcPbBNiM7v/vo5+HG+mmgdQCZJDW2kyejSbJ45yNZmGrqpuT/B6wO7AaONmQvWb2aI+x/usdqurnQ9ei4SXZoap+PHQdmhv6m92m/Qegqh6xAcvRHDPdbDRV9czhqtLQkrwC+D/A6XS92o8H3lVVRw1a2Bxm0B5D/dynx3Ln1G3XAi+pqouGq0pD61eGfDPdNEyuDLnAJdlhTcf9o2xhczYaTaX/XDxmohc7yX2Ab1bVg4etbO7aaOgC1MQRwBuraoeq2gF4E90NLVrYPg1cCuwIvBO4AnDBmgWqqn488eibduq3V9J9JayFbWI2GmnUdcBNI/s39W2ahmO0x9M9q+qMiZ2qOrO/gUELmytD6nckeSVwMN03YA8EtgP+Adh7yLo0OGej0R2SvLHfXAGcneQEuqFn+wHeJLsGBu3xdHmS/003fATgALreCS1srgypqRxCd2PT2QBV9YN+eWUtbCf2DwnuvJF+YrKFCScMUMu8YtAeTy+nGxrwr/3+1/s2LWzvSbIF3VCiiZUhXz9sSZoDbqmqXycBIMli1nCTpBYGZ6XRqKp659A1zFcG7TFUVdcDfr2nyS6aYmXIPxq2JM0BZyX5K+DuSZ4MvAb44sA1aWD9qrHv4M7p/UI368gDhqxLw0qyDHg7I9M+grMUrYmzjoyRJF9kzdN1OS3TApbkXOCZVXV1v/844O+r6uHDVqYhJdkIOAh4Cl2YOhn4RPmPw4KW5FLgDXSrQk5M74dzJi9s/awjfwlcCNw+0e4sRdMzaI+RJI9f0/H+BjgtUH3v9UeBZ9Atu/43wNOr6spBC9Pg+qkfqapVQ9eiuSHJ2VX1qKHr0NyS5BtV9dih65hPDNrSAtIvx/5xuiV0n2awWrjSDco+FHgtd071ehtweFW9a7DCNCckeR+wiO5en9FZR74zWFEaXJK9gRcCk2ej+ddpn7TAOUZ7DE2z4tsNwHLgPX71t7BMMaToHnSfhyOTOKRo4XoDsCfwR1X1I4AkDwA+luQNVXXYoNVpaBO92ctG2gpwgauF7WXAQ+jmWJ8YOlLcOfmCJrFHewwleT9dz9Q/900voAtXPwEeW1XPGKo2bXgOKdJUkpwHPLmqrp3UvgQ4pap2HaYySXNVkstcBXLd2KM9np5UVbuN7F+Y5DtVtVuSAwarSoMwSGsaG08O2dCN007iioALXJJtgL8G7l9VT02yM/Doqjpy4NI0rG8m2bmqLh66kPnCJdjH06Iku0/s9DfBLep3bx2mJA0lyU1Jbhx53DT6c+j6NJhfr+cxLQyfopuB5v79/vdx3n3BHnQrhl6W5IIkFyZxZcg1sEd7PL0COCrJZnTTdd0IvKJfhv1vBq1MG1xV3WvtZ2kB2mWaP7QCbLqhi9Gcs3VVHZ/kbQBVdWuS29b2JI29fYcuYL4xaI+hqjoHeHi/CiD9IiUTjh+mKs0FSXYB/rjf/VpV2ROxQFXVorWfpQXs5iT3ob+ROskedDdRa2Hzxr515M2QYyjJ3YA/BZby2ys3OWXXApbkdcArufPu8GcBR1TV4cNVJWkuSrIbcDjwMOB7wBLgOf5xvrCNzGo28c3XjsBlVfXQQQubwwzaYyjJV+h6Hiav6PWBwYrS4PpxdI+uqpv7/XsC/+nSuZKmkmQx8GC6UHVZVf1m4JI0x/R/kL2mql4xdC1zlUNHxtN2VeU4Kk0WRv7w6rczUC2S5r7dufOb0d36efePGbYkzSVV9Z0kriC6Bgbt8fTNJA+vqguHLkRzyieBs5N8od/fH3CqLkm/I8mxwAOB87nzD/QCDNoLWJI3juxuBG/c2z4AAATxSURBVOwG/PdA5cwLDh0ZQ0kuBh4E/IhuidQA5RCBhSnJ9lV1Zb+9G/DY/tDXgW2r6kuDFSdpTkpyCbBzGRI0IsmhI7u3AlcAn6+qXw1T0dxn0B5DSXaYqr2qfryha9HwklwK7FtVV0xqfznw9qp64CCFSZqzknwO+POqumboWqT5zKEjY2giUCe5L86HK3gjcEqSp1XVDwCSvBV4EbDG5dklLSxJvkg3RORewMVJvk33zSgAVfXMoWrTcJKcuKbjfi6mZ9AeQ0meCXyAbkWvlcAOwCWA0+8sQFV1UpJbgC8n2Z9uQaPdgcdV1fXDVidpjjkR2IZuaNmoPwbs3V64Hg1cCXwGOBtvpJ8xh46MoSTfBfYCvlpVuyZ5InBAVR00cGkaUJI/Br4AfBN4nmPqJE2W5EvA2ybfTJ/k4cBfV9UzhqlMQ0qyCHgy8ELgEcC/A5+pqosGLWwe2GjoAtTEb6rqOmCjJBtV1RnAsqGL0jCS3NQvtX0SsDmwN7BypF2SJmwz1YxVfdvSDV+O5oKquq2qvlJVBwJ7ACuAM5O8duDS5jyHjoynnyXZDPga8OkkK4GbB65JA6mqew1dg6R5Y8s1HLv7BqtCc06/6vTT6Hq1lwIfofuWVGvg0JEx1K/490u6byxeBGwBfLrv5ZYkaUpJPgOcXlX/OKn9FcCTq+r5w1SmISU5BngY3Tejx1XV9wYuad4waI+5JFsD1zkXqiRpbZJsQ9dL+Wvg3L55GbAJ8Kyq+slQtWk4SW7nzm/GR/PExDodm2/4quYHg/YYSbIH8D5gNfBu4Fhga7qe7ZdU1VcGLE+SNE/0N9E/rN+9qKpOH7Ieab4yaI+RJMuBv6IbKnIE8NSq+laSh9DdHbzroAVKkiQtIM46Ml4WV9UpVfU54CdV9S2Aqrp04LokSZIWHIP2eLl9ZPuXk4751YUkSdIG5NCRMZLkNrqbFUI3DdMvJg4Bm1bVxkPVJkmStNAYtCVJkqQGHDoiSZIkNWDQliRJkhowaEvSGEny9iQXJbkgyflJHjV0TZK0UC0eugBJ0uxI8mjg6cBuVXVLvzLsJnfxmour6tZZKVCSFhh7tCVpfNwPuLaqbgGoqmur6r+T7J3kvCQXJjkqyd0AklzRh3GSLEtyZr/9jiTHJvkP4Ngk2yT5QpLv9o/H9OcdkOTbfc/5x5MsGuRdS9IcZdCWpPFxCrB9ku8n+WiSxyfZFPgU8PyqejjdN5mvnsG1dgaeVFUvBD4CnFVVuwC7ARcl+QPg+cCeVfVI4DbgRbP/liRp/jJoS9KYqKqfA38IHAysAj4LvAr4UVV9vz/taOBxM7jciVU1sfDVXsDH+te4rapuAPbuX+ucJOf3+w+YrfciSePAMdqSNEaq6jbgTODMJBcCh6zh9Fu5s8Nl00nHbl7LSwU4uqretj51StJCYI+2JI2JJA9OstNI0yOBHwJLkzyob3sxcFa/fQVdrzTAn67h0qfRDzdJsijJFn3bc5Lct2+/d5IdZuWNSNKYMGhL0vjYDDg6ycVJLqAbZ/1W4GXA5/oe7tuBf+jPfyfw4STL6cZYT+d1wBP7558L7FxVFwP/Czilf61T6W7GlCT1XIJdkiRJasAebUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVID/x/ZusAo+rSxKAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "WDJv9jkRtAHu",
        "outputId": "189aabed-24d5-45de-b0b2-d3b6391a72f6"
      },
      "source": [
        "#source vs price plotting\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.boxplot(x='Source', y='Price', data=train_cleaned_df, palette='rainbow')\n",
        "plt.title('Source vs Price', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "GlbYbKA2iJug",
        "outputId": "732d3304-1971-43c6-a9b0-97ea010df8bd"
      },
      "source": [
        "#days counting\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.countplot(train_cleaned_df['Day'], palette = 'Set2')\n",
        "plt.title('Count of Days', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "GhwIgmqYj2Bn",
        "outputId": "22c61ecb-d686-43ab-a05e-6712930aadc8"
      },
      "source": [
        "#price vs day plotting\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.boxplot(x='Day', y='Price', data=train_cleaned_df, palette='rainbow')\n",
        "plt.title('Days vs Price', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "tye5gCTCkowm",
        "outputId": "11250d20-0d80-4435-e741-c0b1cfb82c04"
      },
      "source": [
        "train_cleaned_df['Month'] = train_cleaned_df['Month'].map({\n",
        "    1:'JAN',\n",
        "    2:'FEB',\n",
        "    3:'MAR',\n",
        "    4:'APR',\n",
        "    5:'MAY',\n",
        "    6:'JUN',\n",
        "    7:'JUL',\n",
        "    8:'AUG',\n",
        "    9:'SEP',\n",
        "    10:'OCT',\n",
        "    11:'NOV',\n",
        "    12:'DEC'\n",
        "})\n",
        "\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.barplot(train_cleaned_df['Month'], train_cleaned_df['Price'], palette = 'Set2', ci = None)\n",
        "plt.title('Month vs Price', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "JbvyUzRUlow-",
        "outputId": "4b2db502-1f47-440f-f81e-b67059c0c87c"
      },
      "source": [
        "train_cleaned_df['Duration_Hr'], _ = train_cleaned_df['Duration_Hr'].str.split('h', 1).str\n",
        "train_cleaned_df['Duration_Hr'] = train_cleaned_df['Duration_Hr'].astype(int)\n",
        "train_cleaned_df['Duration_in_Min'] = (train_cleaned_df['Duration_Hr'] * 60) + train_cleaned_df['Duration_Min']\n",
        "\n",
        "plt.figure(figsize = (12, 6))\n",
        "sns.scatterplot(train_cleaned_df['Duration_in_Min'], train_cleaned_df['Price'], palette = 'Set2')\n",
        "plt.title('Duration vs Price', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "qo9PVo-doFwd",
        "outputId": "ce47f9bb-e77f-4a5a-9609-d9bc5ed1575a"
      },
      "source": [
        "plt.figure(figsize = (12, 6))\n",
        "sns.barplot(train_cleaned_df['Total_Stops'], train_cleaned_df['Price'], palette = 'Set2', ci = None)\n",
        "plt.title('Stops vs Price', size = 15)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-Qxzwv4_e8F"
      },
      "source": [
        "train_cleaned_df = train_cleaned_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGS62jd6_i8c"
      },
      "source": [
        "train_cleaned_df.to_csv('train_final.csv', index = None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkjDMGvX_3Pj"
      },
      "source": [
        "## **Trained Models**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGquqZdcGnUY"
      },
      "source": [
        "### **Train-Test Preperation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kuOsYntANKq",
        "outputId": "9295aa6a-1186-4911-f172-b657fc2e3269"
      },
      "source": [
        "train_final_df = pd.read_csv('train_final.csv')\n",
        "train_final_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9650, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5AxyfXyBCvX",
        "outputId": "6b3e5733-164e-4041-f7a0-a9fffbd4792a"
      },
      "source": [
        "train_final_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Price</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Dep_Hr</th>\n",
              "      <th>Dep_Min</th>\n",
              "      <th>Arr_Hr</th>\n",
              "      <th>Arr_Min</th>\n",
              "      <th>Duration_Hr</th>\n",
              "      <th>Duration_Min</th>\n",
              "      <th>Duration_in_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>0</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>MAR</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>50.0</td>\n",
              "      <td>170.0</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</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>MAY</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>25.0</td>\n",
              "      <td>445.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>1</td>\n",
              "      <td>6218</td>\n",
              "      <td>12</td>\n",
              "      <td>MAY</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>25.0</td>\n",
              "      <td>325.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>1</td>\n",
              "      <td>13302</td>\n",
              "      <td>1</td>\n",
              "      <td>MAR</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>45.0</td>\n",
              "      <td>285.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SpiceJet</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>0</td>\n",
              "      <td>3873</td>\n",
              "      <td>24</td>\n",
              "      <td>JUN</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "      <td>25.0</td>\n",
              "      <td>145.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline    Source Destination  ... Duration_Hr  Duration_Min  Duration_in_Min\n",
              "0     IndiGo  Banglore   New Delhi  ...           2          50.0            170.0\n",
              "1  Air India   Kolkata    Banglore  ...           7          25.0            445.0\n",
              "2     IndiGo   Kolkata    Banglore  ...           5          25.0            325.0\n",
              "3     IndiGo  Banglore   New Delhi  ...           4          45.0            285.0\n",
              "4   SpiceJet   Kolkata    Banglore  ...           2          25.0            145.0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwmmJq9oBs67",
        "outputId": "8edce8d8-1ea1-49fc-a468-4a1a0d37b04e"
      },
      "source": [
        "df = train_final_df[['Airline', 'Source', 'Destination', 'Total_Stops', 'Price', 'Day', 'Month', 'Duration_in_Min']]\n",
        "df = df.rename(columns = {'Duration_in_Min' : 'Duration'})\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9650, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQR0B27WCuvS",
        "outputId": "2200adbc-7da5-44b1-e1dd-9fe845cdade0"
      },
      "source": [
        "df.isnull().any().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9N7MMN_DEbO1",
        "outputId": "96a0a68d-c84a-4f86-8c2b-a6d4ebd261cf"
      },
      "source": [
        "df['Month'] = df['Month'].map({\n",
        "    'JAN':1,\n",
        "    'FEB':2,\n",
        "    'MAR':3,\n",
        "    'APR':4,\n",
        "    'MAY':5,\n",
        "    'JUN':6,\n",
        "    'JUL':7,\n",
        "    'AUG':8,\n",
        "    'SEP':9,\n",
        "    'OCT':10,\n",
        "    'NOV':11,\n",
        "    'DEC':12\n",
        "})\n",
        "\n",
        "dummies = pd.get_dummies(df[['Airline', 'Source', 'Destination']])\n",
        "df = pd.concat([df, dummies], axis = 1)\n",
        "df = df.drop(['Airline', 'Source', 'Destination'], axis = 1)\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9650, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-sIxSVXEqWM",
        "outputId": "3c933db0-da95-48a9-dc27-0145a5a2a0bb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Total_Stops</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Airline_Air Asia</th>\n",
              "      <th>Airline_Air India</th>\n",
              "      <th>Airline_GoAir</th>\n",
              "      <th>Airline_IndiGo</th>\n",
              "      <th>Airline_Jet Airways</th>\n",
              "      <th>Airline_Jet Airways Business</th>\n",
              "      <th>Airline_Multiple carriers</th>\n",
              "      <th>Airline_Multiple carriers Premium economy</th>\n",
              "      <th>Airline_SpiceJet</th>\n",
              "      <th>Airline_Trujet</th>\n",
              "      <th>Airline_Vistara</th>\n",
              "      <th>Airline_Vistara Premium economy</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "      <th>Destination_Banglore</th>\n",
              "      <th>Destination_Cochin</th>\n",
              "      <th>Destination_Delhi</th>\n",
              "      <th>Destination_Hyderabad</th>\n",
              "      <th>Destination_Kolkata</th>\n",
              "      <th>Destination_New Delhi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>170.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>445.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6218</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>325.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>13302</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>285.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3873</td>\n",
              "      <td>24</td>\n",
              "      <td>6</td>\n",
              "      <td>145.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Total_Stops  Price  ...  Destination_Kolkata  Destination_New Delhi\n",
              "0            0   3897  ...                    0                      1\n",
              "1            2   7662  ...                    0                      0\n",
              "2            1   6218  ...                    0                      0\n",
              "3            1  13302  ...                    0                      1\n",
              "4            0   3873  ...                    0                      0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JRcZN7ZE-Py",
        "outputId": "d6e74f7c-28ea-48b9-e954-e864f90dc333"
      },
      "source": [
        "X = df.drop('Price', axis = 1)\n",
        "y = df['Price']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 60)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6755, 27), (2895, 27), (6755,), (2895,))"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03aZNJyqPzQr"
      },
      "source": [
        "### **Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3MJDZFe5ywl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7ee8ead-566f-4d12-c12e-9a1a6384fdc7"
      },
      "source": [
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuF8QZ7IFLtA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3303ad11-8c03-4cac-d8d1-02993bd1728e"
      },
      "source": [
        "coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])\n",
        "coeff_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Total_Stops</th>\n",
              "      <td>2826.142915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day</th>\n",
              "      <td>-80.017924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <td>-464.527847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration</th>\n",
              "      <td>-0.072202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Air Asia</th>\n",
              "      <td>-4940.494021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Air India</th>\n",
              "      <td>-3404.942756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_GoAir</th>\n",
              "      <td>-4832.434951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_IndiGo</th>\n",
              "      <td>-4749.377577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Jet Airways</th>\n",
              "      <td>-513.390594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Jet Airways Business</th>\n",
              "      <td>38420.358683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Multiple carriers</th>\n",
              "      <td>-1307.570300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Multiple carriers Premium economy</th>\n",
              "      <td>-489.236482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_SpiceJet</th>\n",
              "      <td>-5250.353272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Trujet</th>\n",
              "      <td>-7833.078766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Vistara</th>\n",
              "      <td>-2829.632532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Airline_Vistara Premium economy</th>\n",
              "      <td>-2269.847432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Banglore</th>\n",
              "      <td>502.831315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Chennai</th>\n",
              "      <td>74.433521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Delhi</th>\n",
              "      <td>98.749372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <td>59.607948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Mumbai</th>\n",
              "      <td>-735.622157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination_Banglore</th>\n",
              "      <td>59.607948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination_Cochin</th>\n",
              "      <td>98.749372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination_Delhi</th>\n",
              "      <td>-959.206650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination_Hyderabad</th>\n",
              "      <td>-735.622157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination_Kolkata</th>\n",
              "      <td>74.433521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination_New Delhi</th>\n",
              "      <td>1462.037965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            Coefficient\n",
              "Total_Stops                                 2826.142915\n",
              "Day                                          -80.017924\n",
              "Month                                       -464.527847\n",
              "Duration                                      -0.072202\n",
              "Airline_Air Asia                           -4940.494021\n",
              "Airline_Air India                          -3404.942756\n",
              "Airline_GoAir                              -4832.434951\n",
              "Airline_IndiGo                             -4749.377577\n",
              "Airline_Jet Airways                         -513.390594\n",
              "Airline_Jet Airways Business               38420.358683\n",
              "Airline_Multiple carriers                  -1307.570300\n",
              "Airline_Multiple carriers Premium economy   -489.236482\n",
              "Airline_SpiceJet                           -5250.353272\n",
              "Airline_Trujet                             -7833.078766\n",
              "Airline_Vistara                            -2829.632532\n",
              "Airline_Vistara Premium economy            -2269.847432\n",
              "Source_Banglore                              502.831315\n",
              "Source_Chennai                                74.433521\n",
              "Source_Delhi                                  98.749372\n",
              "Source_Kolkata                                59.607948\n",
              "Source_Mumbai                               -735.622157\n",
              "Destination_Banglore                          59.607948\n",
              "Destination_Cochin                            98.749372\n",
              "Destination_Delhi                           -959.206650\n",
              "Destination_Hyderabad                       -735.622157\n",
              "Destination_Kolkata                           74.433521\n",
              "Destination_New Delhi                       1462.037965"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94PInFZAGKsZ",
        "outputId": "a4ce1946-136e-411f-8caa-a8d48cdc503f"
      },
      "source": [
        "y_pred = regressor.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>4802.569871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>11189.142450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>10258.056747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>6311.735906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>11113.946199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>1795.707404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>5271.185064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>12556.615768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>12957.551314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>11137.468133</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   4802.569871\n",
              "1977    8032  11189.142450\n",
              "9107    5192  10258.056747\n",
              "9597    4340   6311.735906\n",
              "9050   10844  11113.946199\n",
              "...      ...           ...\n",
              "4007    4049   1795.707404\n",
              "9148    5989   5271.185064\n",
              "3925   15135  12556.615768\n",
              "7692   12681  12957.551314\n",
              "8031    8529  11137.468133\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRhF9jDkHpSP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e510cad-ab37-4d2e-e294-244ea894dd9a"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 1926.5518151166166\n",
            "Mean Squared Error: 7906187.845686001\n",
            "Root Mean Squared Error: 2811.794417393633\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bL-NnVGUI6x"
      },
      "source": [
        "### **Ridge Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0uDaNKmUMjy",
        "outputId": "c7cd621c-0c0c-4f26-89fa-1965050a4e20"
      },
      "source": [
        "clf = Ridge()\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clzW04wbVB8N",
        "outputId": "d3162e01-dd76-4569-b8f3-13376b75f208"
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>4836.045923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>11194.386525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>10307.303725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>6348.885305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>11105.759442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>1779.363606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>5292.250413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>12573.985533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>12968.090615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>11131.046816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   4836.045923\n",
              "1977    8032  11194.386525\n",
              "9107    5192  10307.303725\n",
              "9597    4340   6348.885305\n",
              "9050   10844  11105.759442\n",
              "...      ...           ...\n",
              "4007    4049   1779.363606\n",
              "9148    5989   5292.250413\n",
              "3925   15135  12573.985533\n",
              "7692   12681  12968.090615\n",
              "8031    8529  11131.046816\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cA9-YysIcZqc",
        "outputId": "5e36560a-702a-43a4-ebc6-80040c1665ea"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 1931.7119315555547\n",
            "Mean Squared Error: 8067530.230997833\n",
            "Root Mean Squared Error: 2840.3398090717656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_D9HpwiV48u"
      },
      "source": [
        "### **Lasso Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcYBraB6V480",
        "outputId": "0098aba0-643c-4d21-e009-a112f9b4d370"
      },
      "source": [
        "clf = Lasso()\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
              "      normalize=False, positive=False, precompute=False, random_state=None,\n",
              "      selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDI28YRsV482",
        "outputId": "ec448416-547b-40d1-ddfc-3206b5f46370"
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>4858.980225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>11192.460746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>10289.928453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>6354.685300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>11110.248253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>1801.906728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>5302.862512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>12551.271805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>12956.075178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>11130.985679</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   4858.980225\n",
              "1977    8032  11192.460746\n",
              "9107    5192  10289.928453\n",
              "9597    4340   6354.685300\n",
              "9050   10844  11110.248253\n",
              "...      ...           ...\n",
              "4007    4049   1801.906728\n",
              "9148    5989   5302.862512\n",
              "3925   15135  12551.271805\n",
              "7692   12681  12956.075178\n",
              "8031    8529  11130.985679\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8z9mtIRcXb_",
        "outputId": "b30d97f7-8887-462b-8bbe-88698e0fc7c7"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 1926.6401218436465\n",
            "Mean Squared Error: 7935850.179023421\n",
            "Root Mean Squared Error: 2817.0641063034795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y18JnbHxXSnn"
      },
      "source": [
        "### **Elastic Net**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmaYyzPSXSnv",
        "outputId": "4e649873-2d8e-44ba-a0df-8ffc0cf4e1e8"
      },
      "source": [
        "clf = ElasticNet()\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n",
              "           max_iter=1000, normalize=False, positive=False, precompute=False,\n",
              "           random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60Ek_-d0XSnw",
        "outputId": "d55baa18-c9b8-45ef-eba8-90bd3a40d806"
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>7206.661259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>12351.342433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>9499.451871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>6646.298511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>9716.369125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>4060.323598</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>7189.288960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>10706.550164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>10845.838942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>9478.425936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   7206.661259\n",
              "1977    8032  12351.342433\n",
              "9107    5192   9499.451871\n",
              "9597    4340   6646.298511\n",
              "9050   10844   9716.369125\n",
              "...      ...           ...\n",
              "4007    4049   4060.323598\n",
              "9148    5989   7189.288960\n",
              "3925   15135  10706.550164\n",
              "7692   12681  10845.838942\n",
              "8031    8529   9478.425936\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQvqaNpvcSQd",
        "outputId": "03fcac2d-be31-4b3f-c9d7-ef2d5e6e325a"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 2368.0422344828094\n",
            "Mean Squared Error: 11898204.489867266\n",
            "Root Mean Squared Error: 3449.3774061223376\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cP-TZa-rF42u"
      },
      "source": [
        "### **Decision Tree Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJvud4ALF2Yn",
        "outputId": "ff6ab371-baba-4d72-a85c-76151dd8b313"
      },
      "source": [
        "clf = DecisionTreeRegressor(random_state = 0) \n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=0, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Jiw_3epHNjQ",
        "outputId": "2ecbccfd-c0f4-4c5e-b4ec-aacffb8cb428"
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>3898.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>6850.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>7870.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>4722.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>14781.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>3010.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>4409.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>18916.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>9303.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>13044.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   3898.000000\n",
              "1977    8032   6850.000000\n",
              "9107    5192   7870.000000\n",
              "9597    4340   4722.750000\n",
              "9050   10844  14781.000000\n",
              "...      ...           ...\n",
              "4007    4049   3010.000000\n",
              "9148    5989   4409.000000\n",
              "3925   15135  18916.000000\n",
              "7692   12681   9303.666667\n",
              "8031    8529  13044.000000\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8mSnI5KGHRPx",
        "outputId": "0948667a-7ebd-47a9-98ee-ee62f51b25c5"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 1489.4788107574636\n",
            "Mean Squared Error: 6535690.766281593\n",
            "Root Mean Squared Error: 2556.499709814494\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ntwx8-XNTVz"
      },
      "source": [
        "feature_cols =['Total_Stops', 'Price', 'Day', 'Month', 'Duration', 'Airline_Air Asia', 'Airline_Air India', 'Airline_GoAir',\n",
        "               'Airline_IndiGo', 'Airline_Jet Airways', 'Airline_Jet Airways Business', 'Airline_Multiple carriers',\n",
        "               'Airline_Multiple carriers Premium economy', 'Airline_SpiceJet', 'Airline_Trujet', 'Airline_Vistara',\n",
        "               'Airline_Vistara Premium economy', 'Source_Banglore', 'Source_Chennai', 'Source_Delhi', 'Source_Kolkata',\n",
        "               'Source_Mumbai', 'Destination_Banglore', 'Destination_Cochin', 'Destination_Delhi', 'Destination_Hyderabad',\n",
        "               'Destination_Kolkata', 'Destination_New Delhi']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13mwrNFkaaOF"
      },
      "source": [
        "### **Random Forest Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxFGZ9zIaaON",
        "outputId": "944fbd22-0c11-423b-d7a3-473952daf3c4"
      },
      "source": [
        "clf = RandomForestRegressor() \n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9bRx7OvaaON",
        "outputId": "4e3a99b0-9f41-4816-a8cf-a8cda87c81ba"
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>4126.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>6553.380000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>7348.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>4780.465446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>13831.799333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>3135.113844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>4609.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>17879.447929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>9464.864345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>12627.004000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   4126.200000\n",
              "1977    8032   6553.380000\n",
              "9107    5192   7348.340000\n",
              "9597    4340   4780.465446\n",
              "9050   10844  13831.799333\n",
              "...      ...           ...\n",
              "4007    4049   3135.113844\n",
              "9148    5989   4609.120000\n",
              "3925   15135  17879.447929\n",
              "7692   12681   9464.864345\n",
              "8031    8529  12627.004000\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQG8cCCbaaOO",
        "outputId": "754f6133-1048-4659-b755-963b935144f1"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 1335.2773812860562\n",
            "Mean Squared Error: 5113215.237076077\n",
            "Root Mean Squared Error: 2261.2419678300853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmzePD3lb8MN"
      },
      "source": [
        "### **Gradient Boosting Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZF4d_Ldb8MX",
        "outputId": "4b6e7170-fe13-4dbf-9fa2-16582489041a"
      },
      "source": [
        "clf = GradientBoostingRegressor() \n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
              "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
              "                          max_features=None, max_leaf_nodes=None,\n",
              "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                          min_samples_leaf=1, min_samples_split=2,\n",
              "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                          n_iter_no_change=None, presort='deprecated',\n",
              "                          random_state=None, subsample=1.0, tol=0.0001,\n",
              "                          validation_fraction=0.1, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2awIbp3b8MY",
        "outputId": "2793e6a7-cadc-4c77-be73-36b4c47a87dd"
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "pred_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
        "pred_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Actual</th>\n",
              "      <th>Predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2395</th>\n",
              "      <td>3898</td>\n",
              "      <td>4473.062081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>8032</td>\n",
              "      <td>8368.794642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9107</th>\n",
              "      <td>5192</td>\n",
              "      <td>8446.642267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9597</th>\n",
              "      <td>4340</td>\n",
              "      <td>5311.754907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9050</th>\n",
              "      <td>10844</td>\n",
              "      <td>11675.810819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4007</th>\n",
              "      <td>4049</td>\n",
              "      <td>3113.849820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9148</th>\n",
              "      <td>5989</td>\n",
              "      <td>4433.108655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3925</th>\n",
              "      <td>15135</td>\n",
              "      <td>14988.540355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7692</th>\n",
              "      <td>12681</td>\n",
              "      <td>11510.562778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>8529</td>\n",
              "      <td>11836.351497</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2895 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Actual     Predicted\n",
              "2395    3898   4473.062081\n",
              "1977    8032   8368.794642\n",
              "9107    5192   8446.642267\n",
              "9597    4340   5311.754907\n",
              "9050   10844  11675.810819\n",
              "...      ...           ...\n",
              "4007    4049   3113.849820\n",
              "9148    5989   4433.108655\n",
              "3925   15135  14988.540355\n",
              "7692   12681  11510.562778\n",
              "8031    8529  11836.351497\n",
              "\n",
              "[2895 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqlKpoTWb8MY",
        "outputId": "11d38d16-1d9a-44ed-f5b6-e7313988fb7e"
      },
      "source": [
        "print('Mean Absolute Error:', mean_absolute_error(y_test, y_pred))\n",
        "print('Mean Squared Error:', mean_squared_error(y_test, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 1505.3244473716152\n",
            "Mean Squared Error: 5142350.341134013\n",
            "Root Mean Squared Error: 2267.6750960254453\n"
          ]
        }
      ]
    }
  ]
}