FlightPrice 4.ipynb

{
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        "id": "9o-z7mOJofon",
        "outputId": "88bc50dc-a052-46b8-c85a-18a03b2fdfea",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting chart_studio\n",
            "  Downloading chart_studio-1.1.0-py3-none-any.whl (64 kB)\n",
            "\u001b[K     |████████████████████████████████| 64 kB 1.9 MB/s \n",
            "\u001b[?25hCollecting retrying>=1.3.3\n",
            "  Downloading retrying-1.3.3.tar.gz (10 kB)\n",
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            "Building wheels for collected packages: retrying\n",
            "  Building wheel for retrying (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for retrying: filename=retrying-1.3.3-py3-none-any.whl size=11447 sha256=2b5429a34f21b77bf2aec620073f89f33d96f32bd3bfe21f26e8023a92d8e9bc\n",
            "  Stored in directory: /root/.cache/pip/wheels/f9/8d/8d/f6af3f7f9eea3553bc2fe6d53e4b287dad18b06a861ac56ddf\n",
            "Successfully built retrying\n",
            "Installing collected packages: retrying, chart-studio\n",
            "Successfully installed chart-studio-1.1.0 retrying-1.3.3\n"
          ]
        }
      ],
      "source": [
        "!pip install chart_studio"
      ]
    },
    {
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      "metadata": {
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        "id": "8zYHZnRdofoq",
        "outputId": "73a4b96a-c7e0-4d80-bb72-000bfb4e693d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "  Downloading jedi-0.18.1-py2.py3-none-any.whl (1.6 MB)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->cufflinks) (0.5.1)\n",
            "Installing collected packages: jedi\n",
            "Successfully installed jedi-0.18.1\n"
          ]
        }
      ],
      "source": [
        "!pip install cufflinks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bSzECB4Kofoq"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from chart_studio.plotly import plot,iplot\n",
        "import cufflinks as cf\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6kapVSRUofos"
      },
      "outputs": [],
      "source": [
        "df=pd.read_excel(\"Data_Train.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V-UAItQhofos",
        "outputId": "502c717c-224f-4478-aa76-58876c2e01a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Airline Date_of_Journey    Source Destination  \\\n",
              "0              IndiGo      24/03/2019  Banglore   New Delhi   \n",
              "1           Air India       1/05/2019   Kolkata    Banglore   \n",
              "2         Jet Airways       9/06/2019     Delhi      Cochin   \n",
              "3              IndiGo      12/05/2019   Kolkata    Banglore   \n",
              "4              IndiGo      01/03/2019  Banglore   New Delhi   \n",
              "5            SpiceJet      24/06/2019   Kolkata    Banglore   \n",
              "6         Jet Airways      12/03/2019  Banglore   New Delhi   \n",
              "7         Jet Airways      01/03/2019  Banglore   New Delhi   \n",
              "8         Jet Airways      12/03/2019  Banglore   New Delhi   \n",
              "9   Multiple carriers      27/05/2019     Delhi      Cochin   \n",
              "10          Air India       1/06/2019     Delhi      Cochin   \n",
              "11             IndiGo      18/04/2019   Kolkata    Banglore   \n",
              "12          Air India      24/06/2019   Chennai     Kolkata   \n",
              "13        Jet Airways       9/05/2019   Kolkata    Banglore   \n",
              "14             IndiGo      24/04/2019   Kolkata    Banglore   \n",
              "15          Air India       3/03/2019     Delhi      Cochin   \n",
              "16           SpiceJet      15/04/2019     Delhi      Cochin   \n",
              "17        Jet Airways      12/06/2019     Delhi      Cochin   \n",
              "18          Air India      12/06/2019     Delhi      Cochin   \n",
              "19        Jet Airways      27/05/2019     Delhi      Cochin   \n",
              "\n",
              "                    Route Dep_Time  Arrival_Time Duration Total_Stops  \\\n",
              "0               BLR → DEL    22:20  01:10 22 Mar   2h 50m    non-stop   \n",
              "1   CCU → IXR → BBI → BLR    05:50         13:15   7h 25m     2 stops   \n",
              "2   DEL → LKO → BOM → COK    09:25  04:25 10 Jun      19h     2 stops   \n",
              "3         CCU → NAG → BLR    18:05         23:30   5h 25m      1 stop   \n",
              "4         BLR → NAG → DEL    16:50         21:35   4h 45m      1 stop   \n",
              "5               CCU → BLR    09:00         11:25   2h 25m    non-stop   \n",
              "6         BLR → BOM → DEL    18:55  10:25 13 Mar  15h 30m      1 stop   \n",
              "7         BLR → BOM → DEL    08:00  05:05 02 Mar   21h 5m      1 stop   \n",
              "8         BLR → BOM → DEL    08:55  10:25 13 Mar  25h 30m      1 stop   \n",
              "9         DEL → BOM → COK    11:25         19:15   7h 50m      1 stop   \n",
              "10        DEL → BLR → COK    09:45         23:00  13h 15m      1 stop   \n",
              "11              CCU → BLR    20:20         22:55   2h 35m    non-stop   \n",
              "12              MAA → CCU    11:40         13:55   2h 15m    non-stop   \n",
              "13        CCU → BOM → BLR    21:10  09:20 10 May  12h 10m      1 stop   \n",
              "14              CCU → BLR    17:15         19:50   2h 35m    non-stop   \n",
              "15  DEL → AMD → BOM → COK    16:40  19:15 04 Mar  26h 35m     2 stops   \n",
              "16        DEL → PNQ → COK    08:45         13:15   4h 30m      1 stop   \n",
              "17        DEL → BOM → COK    14:00  12:35 13 Jun  22h 35m      1 stop   \n",
              "18  DEL → CCU → BOM → COK    20:15  19:15 13 Jun      23h     2 stops   \n",
              "19        DEL → BOM → COK    16:00  12:35 28 May  20h 35m      1 stop   \n",
              "\n",
              "                Additional_Info  Price  \n",
              "0                       No info   3897  \n",
              "1                       No info   7662  \n",
              "2                       No info  13882  \n",
              "3                       No info   6218  \n",
              "4                       No info  13302  \n",
              "5                       No info   3873  \n",
              "6   In-flight meal not included  11087  \n",
              "7                       No info  22270  \n",
              "8   In-flight meal not included  11087  \n",
              "9                       No info   8625  \n",
              "10                      No info   8907  \n",
              "11                      No info   4174  \n",
              "12                      No info   4667  \n",
              "13  In-flight meal not included   9663  \n",
              "14                      No info   4804  \n",
              "15                      No info  14011  \n",
              "16                      No info   5830  \n",
              "17  In-flight meal not included  10262  \n",
              "18                      No info  13381  \n",
              "19  In-flight meal not included  12898  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a05c08dd-787b-44bd-bca4-8d4b5a468714\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>24/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>1/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>9/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>12/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>18:05</td>\n",
              "      <td>23:30</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>01/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → NAG → DEL</td>\n",
              "      <td>16:50</td>\n",
              "      <td>21:35</td>\n",
              "      <td>4h 45m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SpiceJet</td>\n",
              "      <td>24/06/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>09:00</td>\n",
              "      <td>11:25</td>\n",
              "      <td>2h 25m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>12/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>18:55</td>\n",
              "      <td>10:25 13 Mar</td>\n",
              "      <td>15h 30m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>11087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>01/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>08:00</td>\n",
              "      <td>05:05 02 Mar</td>\n",
              "      <td>21h 5m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>22270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>12/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → BOM → DEL</td>\n",
              "      <td>08:55</td>\n",
              "      <td>10:25 13 Mar</td>\n",
              "      <td>25h 30m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>11087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Multiple carriers</td>\n",
              "      <td>27/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>11:25</td>\n",
              "      <td>19:15</td>\n",
              "      <td>7h 50m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>8625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Air India</td>\n",
              "      <td>1/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BLR → COK</td>\n",
              "      <td>09:45</td>\n",
              "      <td>23:00</td>\n",
              "      <td>13h 15m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>8907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>18/04/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>20:20</td>\n",
              "      <td>22:55</td>\n",
              "      <td>2h 35m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>4174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Air India</td>\n",
              "      <td>24/06/2019</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>MAA → CCU</td>\n",
              "      <td>11:40</td>\n",
              "      <td>13:55</td>\n",
              "      <td>2h 15m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>4667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>9/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BOM → BLR</td>\n",
              "      <td>21:10</td>\n",
              "      <td>09:20 10 May</td>\n",
              "      <td>12h 10m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>9663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>24/04/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>17:15</td>\n",
              "      <td>19:50</td>\n",
              "      <td>2h 35m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>4804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Air India</td>\n",
              "      <td>3/03/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → AMD → BOM → COK</td>\n",
              "      <td>16:40</td>\n",
              "      <td>19:15 04 Mar</td>\n",
              "      <td>26h 35m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>14011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>SpiceJet</td>\n",
              "      <td>15/04/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → PNQ → COK</td>\n",
              "      <td>08:45</td>\n",
              "      <td>13:15</td>\n",
              "      <td>4h 30m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>5830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>12/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>14:00</td>\n",
              "      <td>12:35 13 Jun</td>\n",
              "      <td>22h 35m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>10262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Air India</td>\n",
              "      <td>12/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → CCU → BOM → COK</td>\n",
              "      <td>20:15</td>\n",
              "      <td>19:15 13 Jun</td>\n",
              "      <td>23h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>27/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>16:00</td>\n",
              "      <td>12:35 28 May</td>\n",
              "      <td>20h 35m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "      <td>12898</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a05c08dd-787b-44bd-bca4-8d4b5a468714')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a05c08dd-787b-44bd-bca4-8d4b5a468714 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a05c08dd-787b-44bd-bca4-8d4b5a468714');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pq9zAj8nofot"
      },
      "source": [
        "## **Automated EDA**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVfdXU0jsFdN",
        "outputId": "51ee98e7-a5b4-497b-98f5-01cdc54b9094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KMhAn69-ofou",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b2b1087-f49f-402c-9244-939b996deb13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas-profiling in /usr/local/lib/python3.7/dist-packages (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=1.4 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (3.2.2)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (1.15.0)\n",
            "Requirement already satisfied: jinja2>=2.8 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling) (2.11.3)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.8->pandas-profiling) (2.0.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4->pandas-profiling) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4->pandas-profiling) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4->pandas-profiling) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4->pandas-profiling) (1.4.4)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4->pandas-profiling) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4->pandas-profiling) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pandas-profiling) (2022.2.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas-profiling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u93SFdTtofov"
      },
      "outputs": [],
      "source": [
        "import pandas_profiling as pf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJWdVn-cofox"
      },
      "source": [
        "## **Manual EDA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iao5a051ofox",
        "outputId": "bca4c8c0-592a-4038-b8d8-5a4ba91bbe11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10683, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VEt-G51lofoy",
        "outputId": "e08c4f2d-a901-4147-d4f4-673bb46cbda4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline            object\n",
              "Date_of_Journey    object\n",
              "Source             object\n",
              "Destination        object\n",
              "Route              object\n",
              "Dep_Time           object\n",
              "Arrival_Time       object\n",
              "Duration           object\n",
              "Total_Stops        object\n",
              "Additional_Info    object\n",
              "Price               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.dtypes #checking the data types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tf6VzRRiofoy",
        "outputId": "ca8db641-66d0-4224-d94e-7d262544b9c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 15
        }
      ],
      "source": [
        "df.isna().sum() #Checking null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6BPQ2aZAofoz",
        "outputId": "d39ca303-ad47-44b3-8751-a3318b77a6cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              0\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        0\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "#Remove the NaN values from the dataset\n",
        "df.dropna(how='any',inplace=True)\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJfhFsW3ofo0"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## **Feature Engineering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNtrndf6ofo1"
      },
      "source": [
        "Let's convert **Date_of_Journey** to its appropriate format as **datetime** with regards to **day** and **month**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-wZE30Fwofo1",
        "outputId": "c6211188-fd7a-4ebc-8e2d-c5bb26fcd65b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
              "4       IndiGo      01/03/2019  Banglore   New Delhi        BLR → NAG → DEL   \n",
              "\n",
              "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
              "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897  \n",
              "1    05:50         13:15   7h 25m     2 stops         No info   7662  \n",
              "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882  \n",
              "3    18:05         23:30   5h 25m      1 stop         No info   6218  \n",
              "4    16:50         21:35   4h 45m      1 stop         No info  13302  "
            ],
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              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
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              "      <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",
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              "      <td>3897</td>\n",
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              "    <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",
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              "\n",
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              "          const element = document.querySelector('#df-90ff32e8-d930-4b70-a53f-9fb42678f516');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJcLGLepofo2"
      },
      "source": [
        "<br>\n",
        "\n",
        "### Date_of_journey"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QFi8Vzr0ofo2"
      },
      "outputs": [],
      "source": [
        "df['Date_of_Journey']=pd.to_datetime(df['Date_of_Journey'])\n",
        "df['Day_of_Journey']=(df['Date_of_Journey']).dt.day\n",
        "df['Month_of_Journey']=(df['Date_of_Journey']).dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JT2zibTvofo2",
        "outputId": "ff79b0e3-115a-4b61-f093-ed929696e145",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-01-05   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-09-06     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \\\n",
              "0    22:20  01:10 22 Mar   2h 50m    non-stop         No info   3897   \n",
              "1    05:50         13:15   7h 25m     2 stops         No info   7662   \n",
              "2    09:25  04:25 10 Jun      19h     2 stops         No info  13882   \n",
              "\n",
              "   Day_of_Journey  Month_of_Journey  \n",
              "0              24                 3  \n",
              "1               5                 1  \n",
              "2               6                 9  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37423b6d-006a-4eec-9f54-dea598d9cf35\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day_of_Journey</th>\n",
              "      <th>Month_of_Journey</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-01-05</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-09-06</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37423b6d-006a-4eec-9f54-dea598d9cf35 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-37423b6d-006a-4eec-9f54-dea598d9cf35');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ud-qg2iJofo3"
      },
      "source": [
        "<br>\n",
        "Let's do the same for the other relative columns\n",
        "<br>\n",
        "\n",
        "## Dep_time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VNgyPhjhofo4"
      },
      "outputs": [],
      "source": [
        "#covert to datetime\n",
        "df['Dep_hr']=pd.to_datetime(df['Dep_Time']).dt.hour\n",
        "df['Dep_min']=pd.to_datetime(df['Dep_Time']).dt.minute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yo_8d9ajofo4"
      },
      "outputs": [],
      "source": [
        "#we can now drop the 'Dep_Time'\n",
        "\n",
        "df.drop([\"Dep_Time\"],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MNlOHnv9ofo4"
      },
      "source": [
        "<br>\n",
        "\n",
        "## Arrival_time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jng5AadNofo5"
      },
      "outputs": [],
      "source": [
        "\n",
        "df['Arrival_hr']=pd.to_datetime(df['Arrival_Time']).dt.hour\n",
        "df['Arrival_min']=pd.to_datetime(df['Arrival_Time']).dt.minute"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6y6VJ3XJofo5"
      },
      "outputs": [],
      "source": [
        "#we can now drop the 'Arrival_Time'\n",
        "\n",
        "df.drop([\"Arrival_Time\"],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgFusY-fofo5"
      },
      "source": [
        "<br>\n",
        "\n",
        "## Duration Time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WtPbekZiofo5"
      },
      "outputs": [],
      "source": [
        "duration=df['Duration'].str.split(' ',expand=True) #split duration datapoints based on space ' '\n",
        "duration[1].fillna('00m',inplace=True)   #fill all \"NAN\" with '00m'\n",
        "df['duration_hr']=duration[0].apply(lambda x: x[:-1]) #select the item at index o and leave the last one (in this case the 'h')\n",
        "df['duration_min']=duration[1].apply(lambda x: x[:-1]) #select the item at index 1 and leave the last one (in this case the 'm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ooreGWDrofo6"
      },
      "outputs": [],
      "source": [
        "#we can now drop the 'Duration'\n",
        "\n",
        "df.drop([\"Duration\"],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sTYfAuEGofo6",
        "outputId": "3bb74ccf-151c-4442-9f63-a4eb01941f4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Airline    Source Destination                  Route Total_Stops  \\\n",
              "0       IndiGo  Banglore   New Delhi              BLR → DEL    non-stop   \n",
              "1    Air India   Kolkata    Banglore  CCU → IXR → BBI → BLR     2 stops   \n",
              "2  Jet Airways     Delhi      Cochin  DEL → LKO → BOM → COK     2 stops   \n",
              "\n",
              "  Additional_Info  Price  Day_of_Journey  Month_of_Journey  Dep_hr  Dep_min  \\\n",
              "0         No info   3897              24                 3      22       20   \n",
              "1         No info   7662               5                 1       5       50   \n",
              "2         No info  13882               6                 9       9       25   \n",
              "\n",
              "   Arrival_hr  Arrival_min duration_hr duration_min  \n",
              "0           1           10           2           50  \n",
              "1          13           15           7           25  \n",
              "2           4           25          19           00  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day_of_Journey</th>\n",
              "      <th>Month_of_Journey</th>\n",
              "      <th>Dep_hr</th>\n",
              "      <th>Dep_min</th>\n",
              "      <th>Arrival_hr</th>\n",
              "      <th>Arrival_min</th>\n",
              "      <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>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>00</td>\n",
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              "          const element = document.querySelector('#df-5882f4e0-a754-4e83-bd8e-d4f3a2d4b680');\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BakCbvcofo6"
      },
      "source": [
        "<br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JxosidZtofo7",
        "outputId": "cd0f1e8b-5524-436f-de7f-913cfa02ab88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "cf.set_config_file(theme='ggplot',sharing='public',offline=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giiFIVuTofo7"
      },
      "source": [
        "## Count of Airlines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSwUXnW-ofo7",
        "outputId": "3f3699d6-95b6-48af-cccb-81d69f460660",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Jet Airways                          3849\n",
              "IndiGo                               2053\n",
              "Air India                            1751\n",
              "Multiple carriers                    1196\n",
              "SpiceJet                              818\n",
              "Vistara                               479\n",
              "Air Asia                              319\n",
              "GoAir                                 194\n",
              "Multiple carriers Premium economy      13\n",
              "Jet Airways Business                    6\n",
              "Vistara Premium economy                 3\n",
              "Trujet                                  1\n",
              "Name: Airline, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df['Airline'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8LkmtFDofo8"
      },
      "source": [
        "<br>\n",
        "\n",
        "## Airline vs Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r7cd0j5jofo8",
        "outputId": "bc69c93a-4147-497f-9d72-e2fd3d4feb40",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]),\n",
              " <a list of 12 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "Airprices=df.groupby('Airline')['Price'].mean().sort_values(ascending=False)\n",
        "plt.figure(figsize=(15,10))\n",
        "sns.barplot(Airprices.index,Airprices.values)\n",
        "plt.xticks(rotation=270)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bt3H8zKMofo8"
      },
      "source": [
        "**Jet Airways Business has the highest price with Trujet having the lowest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hytrXEMHofo8",
        "outputId": "7497ce29-ef7f-43c9-888c-b2d4b9f3dc80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "plt.figure(figsize=(18,10))\n",
        "sns.boxplot(y='Price',x='Airline',data= df.sort_values('Price',ascending=False))\n",
        "plt.show"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1H72HDgofo9"
      },
      "source": [
        "<br>\n",
        "\n",
        "## Price based on number of stops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u3T1iqEZofo9",
        "outputId": "897a832d-cce6-4b8a-bed4-c2102096d062",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Airline                            Total_Stops\n",
              "Air Asia                           1 stop          7078.325581\n",
              "                                   2 stops         6341.888889\n",
              "                                   non-stop        4492.331492\n",
              "Air India                          1 stop          9041.509259\n",
              "                                   2 stops        12092.779101\n",
              "                                   3 stops        12208.054054\n",
              "                                   4 stops        17686.000000\n",
              "                                   non-stop        5605.326139\n",
              "GoAir                              1 stop          6884.676471\n",
              "                                   non-stop        4726.173913\n",
              "IndiGo                             1 stop          7104.717528\n",
              "                                   2 stops         7507.526316\n",
              "                                   non-stop        4731.174053\n",
              "Jet Airways                        1 stop         12512.741223\n",
              "                                   2 stops        13387.247467\n",
              "                                   non-stop        6175.078652\n",
              "Jet Airways Business               1 stop         62844.250000\n",
              "                                   2 stops        49387.500000\n",
              "Multiple carriers                  1 stop         10711.316157\n",
              "                                   2 stops        14809.395349\n",
              "                                   3 stops        17292.750000\n",
              "Multiple carriers Premium economy  1 stop         11418.846154\n",
              "SpiceJet                           1 stop          6750.560811\n",
              "                                   non-stop        3805.423881\n",
              "Trujet                             1 stop          4140.000000\n",
              "Vistara                            1 stop          9641.148837\n",
              "                                   non-stop        6293.954545\n",
              "Vistara Premium economy            non-stop        8962.333333\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "df.groupby(['Airline','Total_Stops'])['Price'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A1jCOY8Vofo9"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(18,10))\n",
        "ax=sns.barplot(x=df['Airline'],y=df['Price'],hue=df['Total_Stops'],palette=\"Set1\")\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LX1iz-Iofo-"
      },
      "source": [
        "**One stop and two stpos Jet Airways Business is having the highest price**\n",
        "\n",
        "<br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YVZON7k0ofo-",
        "outputId": "87c448df-e5b5-4478-c123-f4705d07b87e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Delhi       4536\n",
              "Kolkata     2871\n",
              "Banglore    2197\n",
              "Mumbai       697\n",
              "Chennai      381\n",
              "Name: Source, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df['Source'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxX8G7Xaofo-"
      },
      "source": [
        "<br>\n",
        "\n",
        "## Source vs Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S0qRUtK2ofo-",
        "outputId": "348220e5-16fe-4716-994d-500dfe6d13d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.barplot(y='Price',x='Source',data=df.sort_values('Price',ascending=False))\n",
        "plt.show"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usb06Ni5ofo_"
      },
      "source": [
        "<br>\n",
        "\n",
        "## Destination vs Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iSNTYkUEofo_",
        "outputId": "2d8cc43c-107e-4afe-d6ae-8bfe5ded0005"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.barplot(y='Price',x='Destination',data=df.sort_values('Price',ascending=False))\n",
        "plt.show"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FAntbYOYofo_",
        "outputId": "c39e0e62-4dcc-4259-ec61-0d60795ca03a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Cochin       4536\n",
              "Banglore     2871\n",
              "Delhi        1265\n",
              "New Delhi     932\n",
              "Hyderabad     697\n",
              "Kolkata       381\n",
              "Name: Destination, dtype: int64"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Destination'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f6ESplThofpA"
      },
      "outputs": [],
      "source": [
        "for i in df:\n",
        "    df.replace('New Delhi','Delhi',inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eMQwbvNBofpA",
        "outputId": "f768012b-52c6-4d65-abc3-5bf4231f04a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Destination'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zurIcax6ofpA",
        "outputId": "7e939cf4-ab5e-48dd-f32f-4a93c7e7adc0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(*args, **kw)>"
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.barplot(y='Price',x='Destination',data=df.sort_values('Price',ascending=False))\n",
        "plt.show"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "diHQiG7pofpA",
        "outputId": "f243a844-4b79-4377-ae55-3cbcecd42e99"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Cochin       4536\n",
              "Banglore     2871\n",
              "Delhi        2197\n",
              "Hyderabad     697\n",
              "Kolkata       381\n",
              "Name: Destination, dtype: int64"
            ]
          },
          "execution_count": 132,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Destination'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWjBqZ99ofpB",
        "outputId": "5f72745f-72fb-4bcf-8850-6f1a00f697ed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1a2b061b90>"
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1656x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(23,12))\n",
        "sns.heatmap(df.corr(),annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qSiP9-VofpB"
      },
      "source": [
        "The features are less correlated which is a good thing for us to avoid **Multicollinearity**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcwU3_V2ofpB"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## Apply Onehot Encoding on the categorical variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VOndopKaofpC"
      },
      "outputs": [],
      "source": [
        "#Handling Categorical Values \n",
        "df['Total_Stops']=df['Total_Stops'].map({'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4})\n",
        "air_dummy=pd.get_dummies(df['Airline'],drop_first=True)\n",
        "# print(df['Source'].value_counts())\n",
        "# print(df['Destination'].value_counts())\n",
        "\n",
        "source_dest_dummy=pd.get_dummies(df[['Source','Destination']],drop_first=True)\n",
        "df=pd.concat([air_dummy,source_dest_dummy,df],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3Xs2b_UofpC"
      },
      "outputs": [],
      "source": [
        "df.drop(['Airline','Source','Destination'],inplace=True,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0_gWQEqZofpC",
        "outputId": "d12b93da-f3e9-4e2f-e80d-dff57a7ea318"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Air India</th>\n",
              "      <th>GoAir</th>\n",
              "      <th>IndiGo</th>\n",
              "      <th>Jet Airways</th>\n",
              "      <th>Jet Airways Business</th>\n",
              "      <th>Multiple carriers</th>\n",
              "      <th>Multiple carriers Premium economy</th>\n",
              "      <th>SpiceJet</th>\n",
              "      <th>Trujet</th>\n",
              "      <th>Vistara</th>\n",
              "      <th>...</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day_of_Journey</th>\n",
              "      <th>Month_of_Journey</th>\n",
              "      <th>Dep_hr</th>\n",
              "      <th>Dep_min</th>\n",
              "      <th>Arrival_hr</th>\n",
              "      <th>Arrival_min</th>\n",
              "      <th>duration_hr</th>\n",
              "      <th>duration_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
              "0          0      0       1            0                     0   \n",
              "1          1      0       0            0                     0   \n",
              "2          0      0       0            1                     0   \n",
              "3          0      0       1            0                     0   \n",
              "4          0      0       1            0                     0   \n",
              "\n",
              "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
              "0                  0                                  0         0       0   \n",
              "1                  0                                  0         0       0   \n",
              "2                  0                                  0         0       0   \n",
              "3                  0                                  0         0       0   \n",
              "4                  0                                  0         0       0   \n",
              "\n",
              "   Vistara  ...  Additional_Info  Price  Day_of_Journey  Month_of_Journey  \\\n",
              "0        0  ...          No info   3897              24                 3   \n",
              "1        0  ...          No info   7662               5                 1   \n",
              "2        0  ...          No info  13882               6                 9   \n",
              "3        0  ...          No info   6218               5                12   \n",
              "4        0  ...          No info  13302               3                 1   \n",
              "\n",
              "   Dep_hr  Dep_min  Arrival_hr  Arrival_min  duration_hr duration_min  \n",
              "0      22       20           1           10            2           50  \n",
              "1       5       50          13           15            7           25  \n",
              "2       9       25           4           25           19           00  \n",
              "3      18        5          23           30            5           25  \n",
              "4      16       50          21           35            4           45  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SQzet9kTofpC",
        "outputId": "80f40a92-3bbf-4ae0-9666-d3919531aa81"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10682, 31)"
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFuXwPafofpD"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQjzogzQofpD"
      },
      "source": [
        "# **Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BCD2jaDrofpD"
      },
      "outputs": [],
      "source": [
        "df_test=pd.read_excel(\"Test_set.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yy5eSI2KofpD",
        "outputId": "bfc1d486-f626-43d4-bb79-d51a9e9e2c4a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>6/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>17:30</td>\n",
              "      <td>04:25 07 Jun</td>\n",
              "      <td>10h 55m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>12/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → MAA → BLR</td>\n",
              "      <td>06:20</td>\n",
              "      <td>10:20</td>\n",
              "      <td>4h</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>21/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>19:15</td>\n",
              "      <td>19:00 22 May</td>\n",
              "      <td>23h 45m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>In-flight meal not included</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Multiple carriers</td>\n",
              "      <td>21/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → BOM → COK</td>\n",
              "      <td>08:00</td>\n",
              "      <td>21:00</td>\n",
              "      <td>13h</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Air Asia</td>\n",
              "      <td>24/06/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>23:55</td>\n",
              "      <td>02:45 25 Jun</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Airline Date_of_Journey    Source Destination            Route  \\\n",
              "0        Jet Airways       6/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
              "1             IndiGo      12/05/2019   Kolkata    Banglore  CCU → MAA → BLR   \n",
              "2        Jet Airways      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
              "3  Multiple carriers      21/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
              "4           Air Asia      24/06/2019  Banglore       Delhi        BLR → DEL   \n",
              "\n",
              "  Dep_Time  Arrival_Time Duration Total_Stops              Additional_Info  \n",
              "0    17:30  04:25 07 Jun  10h 55m      1 stop                      No info  \n",
              "1    06:20         10:20       4h      1 stop                      No info  \n",
              "2    19:15  19:00 22 May  23h 45m      1 stop  In-flight meal not included  \n",
              "3    08:00         21:00      13h      1 stop                      No info  \n",
              "4    23:55  02:45 25 Jun   2h 50m    non-stop                      No info  "
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pgg402dEofpD"
      },
      "source": [
        "<br>\n",
        "\n",
        "### Let's Perform that same steps as we did for train data earlier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jaTSU5K3ofpE"
      },
      "outputs": [],
      "source": [
        "df_test['Date_of_Journey']=pd.to_datetime(df_test['Date_of_Journey'])\n",
        "df_test['Day_of_Journey']=(df_test['Date_of_Journey']).dt.day\n",
        "df_test['Month_of_Journey']=(df_test['Date_of_Journey']).dt.month\n",
        "\n",
        "#Dep_time \n",
        "df_test['Dep_hr']=pd.to_datetime(df_test['Dep_Time']).dt.hour\n",
        "df_test['Dep_min']=pd.to_datetime(df_test['Dep_Time']).dt.minute\n",
        "\n",
        "#Arrival_time\n",
        "df_test['Arrival_hr']=pd.to_datetime(df_test['Arrival_Time']).dt.hour\n",
        "df_test['Arrival_min']=pd.to_datetime(df_test['Arrival_Time']).dt.minute\n",
        "\n",
        "#Splitting duration  time\n",
        "\n",
        "a=df_test['Duration'].str.split(' ',expand=True)\n",
        "a[1].fillna('00m',inplace=True)\n",
        "df_test['dur_hr']=a[0].apply(lambda x: x[:-1])\n",
        "df_test['dur_min']=a[1].apply(lambda x: x[:-1])\n",
        "\n",
        "#dropping the data\n",
        "df_test.drop(['Date_of_Journey','Duration','Arrival_Time','Dep_Time'],inplace=True,axis=1)\n",
        "\n",
        "#Handling Categorical Values \n",
        "df_test['Total_Stops']=df_test['Total_Stops'].map({'non-stop':0, '2 stops':2, '1 stop':1, '3 stops':3, '4 stops':4})\n",
        "\n",
        "air_dummy=pd.get_dummies(df_test['Airline'],drop_first=True)\n",
        "source_dest_dummy=pd.get_dummies(df_test[['Source','Destination']],drop_first=True)\n",
        "df_test=pd.concat([air_dummy,source_dest_dummy,df_test],axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "urbq6ChWofpE"
      },
      "outputs": [],
      "source": [
        "df_test.drop(['Airline','Source','Destination','Additional_Info',\"Route\"],inplace=True,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k_cffBBdofpE",
        "outputId": "43981084-4122-454b-ba22-9f48c93d00c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train_shape (10682, 31)\n",
            "test_shape (2671, 28)\n"
          ]
        }
      ],
      "source": [
        "print('train_shape',df.shape)\n",
        "print('test_shape',df_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5krvn9FiofpF",
        "outputId": "ed51c711-8689-46ea-8bb4-47f6d9760bb5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Air India</th>\n",
              "      <th>GoAir</th>\n",
              "      <th>IndiGo</th>\n",
              "      <th>Jet Airways</th>\n",
              "      <th>Jet Airways Business</th>\n",
              "      <th>Multiple carriers</th>\n",
              "      <th>Multiple carriers Premium economy</th>\n",
              "      <th>SpiceJet</th>\n",
              "      <th>Trujet</th>\n",
              "      <th>Vistara</th>\n",
              "      <th>...</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day_of_Journey</th>\n",
              "      <th>Month_of_Journey</th>\n",
              "      <th>Dep_hr</th>\n",
              "      <th>Dep_min</th>\n",
              "      <th>Arrival_hr</th>\n",
              "      <th>Arrival_min</th>\n",
              "      <th>duration_hr</th>\n",
              "      <th>duration_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
              "0          0      0       1            0                     0   \n",
              "1          1      0       0            0                     0   \n",
              "2          0      0       0            1                     0   \n",
              "3          0      0       1            0                     0   \n",
              "4          0      0       1            0                     0   \n",
              "\n",
              "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
              "0                  0                                  0         0       0   \n",
              "1                  0                                  0         0       0   \n",
              "2                  0                                  0         0       0   \n",
              "3                  0                                  0         0       0   \n",
              "4                  0                                  0         0       0   \n",
              "\n",
              "   Vistara  ...  Additional_Info  Price  Day_of_Journey  Month_of_Journey  \\\n",
              "0        0  ...          No info   3897              24                 3   \n",
              "1        0  ...          No info   7662               5                 1   \n",
              "2        0  ...          No info  13882               6                 9   \n",
              "3        0  ...          No info   6218               5                12   \n",
              "4        0  ...          No info  13302               3                 1   \n",
              "\n",
              "   Dep_hr  Dep_min  Arrival_hr  Arrival_min  duration_hr duration_min  \n",
              "0      22       20           1           10            2           50  \n",
              "1       5       50          13           15            7           25  \n",
              "2       9       25           4           25           19           00  \n",
              "3      18        5          23           30            5           25  \n",
              "4      16       50          21           35            4           45  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 142,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6OtivyfGofpF"
      },
      "outputs": [],
      "source": [
        "x=df.drop(['Route', 'Price','Additional_Info'],axis=1)\n",
        "y=df['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xWcGVWC4ofpF",
        "outputId": "6fa50e16-6447-4194-a8a8-d41bcbae1eb4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Air India</th>\n",
              "      <th>GoAir</th>\n",
              "      <th>IndiGo</th>\n",
              "      <th>Jet Airways</th>\n",
              "      <th>Jet Airways Business</th>\n",
              "      <th>Multiple carriers</th>\n",
              "      <th>Multiple carriers Premium economy</th>\n",
              "      <th>SpiceJet</th>\n",
              "      <th>Trujet</th>\n",
              "      <th>Vistara</th>\n",
              "      <th>...</th>\n",
              "      <th>Destination_Kolkata</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Day_of_Journey</th>\n",
              "      <th>Month_of_Journey</th>\n",
              "      <th>Dep_hr</th>\n",
              "      <th>Dep_min</th>\n",
              "      <th>Arrival_hr</th>\n",
              "      <th>Arrival_min</th>\n",
              "      <th>duration_hr</th>\n",
              "      <th>duration_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>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>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 28 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
              "0          0      0       1            0                     0   \n",
              "1          1      0       0            0                     0   \n",
              "2          0      0       0            1                     0   \n",
              "\n",
              "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
              "0                  0                                  0         0       0   \n",
              "1                  0                                  0         0       0   \n",
              "2                  0                                  0         0       0   \n",
              "\n",
              "   Vistara  ...  Destination_Kolkata  Total_Stops  Day_of_Journey  \\\n",
              "0        0  ...                    0            0              24   \n",
              "1        0  ...                    0            2               5   \n",
              "2        0  ...                    0            2               6   \n",
              "\n",
              "   Month_of_Journey  Dep_hr  Dep_min  Arrival_hr  Arrival_min  duration_hr  \\\n",
              "0                 3      22       20           1           10            2   \n",
              "1                 1       5       50          13           15            7   \n",
              "2                 9       9       25           4           25           19   \n",
              "\n",
              "   duration_min  \n",
              "0            50  \n",
              "1            25  \n",
              "2            00  \n",
              "\n",
              "[3 rows x 28 columns]"
            ]
          },
          "execution_count": 154,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPgWNMVuofpF"
      },
      "source": [
        "<br>\n",
        "<br>\n",
        "\n",
        "## **Model Building and Hyperparameter Tuning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qX2zJQreofpF"
      },
      "source": [
        "### ExtraTreesRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true,
          "source_hidden": true
        },
        "id": "ylGdJdk3ofpG",
        "outputId": "f6c66bf2-b156-42fb-bd28-88ec1749106d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ExtraTreesRegressor(bootstrap=False, 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)"
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "ET_Model=ExtraTreesRegressor()\n",
        "ET_Model.fit(x,y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "jupyter": {
          "source_hidden": true
        },
        "id": "MhdmTcl5ofpG"
      },
      "source": [
        "**Future Importance**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true,
          "source_hidden": true
        },
        "id": "-OEurCnBofpG",
        "outputId": "c82008c7-8008-4dd4-eb6e-f51ed76b60e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1a2e1c8050>"
            ]
          },
          "execution_count": 153,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pd.Series(ET_Model.feature_importances_,index=x.columns).sort_values(ascending=False).plot(kind='bar',figsize=(18,10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "of2XnmPKofpG"
      },
      "outputs": [],
      "source": [
        "#splitting the dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mu56lJ8PofpH",
        "outputId": "e5319422-26de-414b-8bb6-177290167755"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.7682238280133812"
            ]
          },
          "execution_count": 157,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Preparing Extra Tree Regression\n",
        "from sklearn.ensemble import  ExtraTreesRegressor\n",
        "ET_Model=ExtraTreesRegressor(n_estimators = 120)\n",
        "ET_Model.fit(X_train,y_train)\n",
        "y_predict=ET_Model.predict(X_test)\n",
        "\n",
        "\n",
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test,y_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JoVtZNJPofpH",
        "outputId": "9f7eb505-0be5-4444-af8d-5290d4cd5b60"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.792954708381232"
            ]
          },
          "execution_count": 160,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "RF_Model=RandomForestRegressor()\n",
        "RF_Model.fit(X_train,y_train)\n",
        "y_predict=RF_Model.predict(X_test)\n",
        "r2_score(y_test,y_predict)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uYRcU3nNofpH"
      },
      "source": [
        "<br>\n",
        "\n",
        "**Hyperparameter**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TQq0P2AeofpI"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]\n",
        "max_features = ['auto', 'sqrt']\n",
        "max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]\n",
        "min_samples_split = [2, 5, 10, 15, 100]\n",
        "min_samples_leaf = [1, 2, 5, 10]\n",
        "\n",
        "# create random grid\n",
        "\n",
        "rand_grid={'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf}\n",
        "\n",
        "rf=RandomForestRegressor()\n",
        "\n",
        "rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=10,cv=3,random_state=42, n_jobs = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mFgpOInWofpI",
        "outputId": "c5e4f3de-7eb9-46d6-fc8a-95c3586916b8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3, error_score=nan,\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   ccp_alpha=0.0,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   max_samples=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators=100,\n",
              "                                                   n_jobs=None, oob_score=Fals...\n",
              "                   iid='deprecated', n_iter=10, n_jobs=1,\n",
              "                   param_distributions={'max_depth': [6, 15, 25, 35, 45],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
              "                                        'min_samples_split': [2, 5, 10, 15,\n",
              "                                                              100],\n",
              "                                        'n_estimators': [80, 237, 395, 553, 711,\n",
              "                                                         868, 1026, 1184, 1342,\n",
              "                                                         1500]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
              "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
              "                   verbose=0)"
            ]
          },
          "execution_count": 179,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rCV.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ya-wP8q9ofpI",
        "outputId": "23884c7d-ec7a-43c5-b81a-05cdf18fed02"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([12500.2967405 ,  4286.76073494,  6136.50519908, ...,\n",
              "       11657.36838275,  9659.32444484,  8655.77083065])"
            ]
          },
          "execution_count": 180,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_pred=rCV.predict(X_test)\n",
        "rf_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EJcEtHjrofpI",
        "outputId": "363931c3-323e-4dba-dac2-d1c2e77bf425"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE 1156.8110505706231\n",
            "MSE 3974736.1005828027\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "print('MAE',mean_absolute_error(y_test,rf_pred))\n",
        "print('MSE',mean_squared_error(y_test,rf_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QJ8E-vWcofpJ",
        "outputId": "34a93b54-14b8-4ad1-c79f-c6f56cdc3f1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8064284149245494"
            ]
          },
          "execution_count": 182,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "r2_score(y_test,rf_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A52FzOyyofpJ",
        "outputId": "f0a2666d-f38a-45a7-8f46-5110ba3b324a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "  Downloading catboost-0.23.2-cp37-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (10.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.9 MB 140 kB/s eta 0:00:01\n",
            "\u001b[?25hRequirement already satisfied: scipy in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: plotly in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from catboost) (4.8.1)\n",
            "Requirement already satisfied: six in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from catboost) (1.14.0)\n",
            "Collecting graphviz\n",
            "  Downloading graphviz-0.14.1-py2.py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: matplotlib in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from catboost) (1.0.5)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from catboost) (1.18.1)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from matplotlib->catboost) (1.1.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from matplotlib->catboost) (2.4.6)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.24.0->catboost) (2019.3)\n",
            "Requirement already satisfied: setuptools in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (46.0.0.post20200309)\n",
            "Installing collected packages: graphviz, catboost\n",
            "Successfully installed catboost-0.23.2 graphviz-0.14.1\n"
          ]
        }
      ],
      "source": [
        "!pip install catboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HFYudkRBofpJ"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "pIBrhodvofpJ",
        "outputId": "52c5d3fb-ee80-4285-cdb9-0894e4f7099d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.05718\n",
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          ]
        },
        {
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostRegressor at 0x1a53ab8690>"
            ]
          },
          "execution_count": 192,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from catboost import CatBoostRegressor\n",
        "\n",
        "cat=CatBoostRegressor()\n",
        "cat.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RbK5lzXVofpK"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w2qZmoEfofpK"
      },
      "outputs": [],
      "source": [
        "cat_pred=cat.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r7grjYf7ofpK",
        "outputId": "e692ddd4-2899-49d3-a062-aaede59bfe0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8301928561140887"
            ]
          },
          "execution_count": 195,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "r2_score(y_test,cat_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LRaTyQ8IofpK",
        "outputId": "92377db3-be07-4aad-90be-3d0657f3c69b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting lightgbm\n",
            "  Downloading lightgbm-2.3.1-py2.py3-none-macosx_10_9_x86_64.macosx_10_10_x86_64.macosx_10_11_x86_64.macosx_10_12_x86_64.macosx_10_13_x86_64.macosx_10_14_x86_64.macosx_10_15_x86_64.whl (679 kB)\n",
            "\u001b[K     |████████████████████████████████| 679 kB 317 kB/s eta 0:00:01\n",
            "\u001b[?25hRequirement already satisfied: scipy in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from lightgbm) (1.4.1)\n",
            "Requirement already satisfied: numpy in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from lightgbm) (1.18.1)\n",
            "Requirement already satisfied: scikit-learn in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from lightgbm) (0.22.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from scikit-learn->lightgbm) (0.14.1)\n",
            "Installing collected packages: lightgbm\n",
            "Successfully installed lightgbm-2.3.1\n"
          ]
        }
      ],
      "source": [
        "!pip install lightgbm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oOwbsYEXofpK"
      },
      "outputs": [],
      "source": [
        "X_train[['duration_hr','duration_min']]=X_train[['duration_hr','duration_min']].astype(int)\n",
        "X_test[['duration_hr','duration_min']]=X_test[['duration_hr','duration_min']].astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cvMOLSsaofpL",
        "outputId": "b5c4363f-d926-4d0c-f12f-be762f8c8f97"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
              "              importance_type='split', learning_rate=0.1, max_depth=-1,\n",
              "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "              n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
              "              random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
              "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "execution_count": 202,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from lightgbm import LGBMRegressor\n",
        "\n",
        "lgb_model = LGBMRegressor()\n",
        "lgb_model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pxik7txyofpL",
        "outputId": "5693fdf3-5a88-45c0-e983-85f70123e47f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8030064936005559"
            ]
          },
          "execution_count": 204,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lgb_pred=lgb_model.predict(X_test)\n",
        "r2_score(y_test,lgb_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BsWtIerXofpL"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8zUb-meFofpL",
        "outputId": "0b0182a1-a5f6-4cc9-8728-7da3afffa645"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting xgboost\n",
            "  Downloading xgboost-1.1.1-py3-none-macosx_10_13_x86_64.macosx_10_14_x86_64.macosx_10_15_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 857 kB/s eta 0:00:01\n",
            "\u001b[?25hRequirement already satisfied: numpy in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from xgboost) (1.18.1)\n",
            "Requirement already satisfied: scipy in /Users/mybeast/opt/anaconda3/lib/python3.7/site-packages (from xgboost) (1.4.1)\n",
            "Installing collected packages: xgboost\n",
            "Successfully installed xgboost-1.1.1\n"
          ]
        }
      ],
      "source": [
        "!pip install xgboost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EUfsu_viofpM",
        "outputId": "3e6e20a0-661d-494a-b143-f01680e1d6d6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8212496826037233"
            ]
          },
          "execution_count": 209,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import xgboost as xgb\n",
        "xgb_model=xgb.XGBRegressor()\n",
        "xgb_model.fit(X_train,y_train)\n",
        "xgb_pred=xgb_model.predict(X_test)\n",
        "r2_score(y_test,xgb_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RjgU2jlaofpM",
        "outputId": "7f0b783f-4083-41c3-a1ea-6a57533cc9cd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Air India</th>\n",
              "      <th>GoAir</th>\n",
              "      <th>IndiGo</th>\n",
              "      <th>Jet Airways</th>\n",
              "      <th>Jet Airways Business</th>\n",
              "      <th>Multiple carriers</th>\n",
              "      <th>Multiple carriers Premium economy</th>\n",
              "      <th>SpiceJet</th>\n",
              "      <th>Trujet</th>\n",
              "      <th>Vistara</th>\n",
              "      <th>...</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Day_of_Journey</th>\n",
              "      <th>Month_of_Journey</th>\n",
              "      <th>Dep_hr</th>\n",
              "      <th>Dep_min</th>\n",
              "      <th>Arrival_hr</th>\n",
              "      <th>Arrival_min</th>\n",
              "      <th>duration_hr</th>\n",
              "      <th>duration_min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No info</td>\n",
              "      <td>13302</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>50</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Air India  GoAir  IndiGo  Jet Airways  Jet Airways Business  \\\n",
              "0          0      0       1            0                     0   \n",
              "1          1      0       0            0                     0   \n",
              "2          0      0       0            1                     0   \n",
              "3          0      0       1            0                     0   \n",
              "4          0      0       1            0                     0   \n",
              "\n",
              "   Multiple carriers  Multiple carriers Premium economy  SpiceJet  Trujet  \\\n",
              "0                  0                                  0         0       0   \n",
              "1                  0                                  0         0       0   \n",
              "2                  0                                  0         0       0   \n",
              "3                  0                                  0         0       0   \n",
              "4                  0                                  0         0       0   \n",
              "\n",
              "   Vistara  ...  Additional_Info  Price  Day_of_Journey  Month_of_Journey  \\\n",
              "0        0  ...          No info   3897              24                 3   \n",
              "1        0  ...          No info   7662               5                 1   \n",
              "2        0  ...          No info  13882               6                 9   \n",
              "3        0  ...          No info   6218               5                12   \n",
              "4        0  ...          No info  13302               3                 1   \n",
              "\n",
              "   Dep_hr  Dep_min  Arrival_hr  Arrival_min  duration_hr duration_min  \n",
              "0      22       20           1           10            2           50  \n",
              "1       5       50          13           15            7           25  \n",
              "2       9       25           4           25           19           00  \n",
              "3      18        5          23           30            5           25  \n",
              "4      16       50          21           35            4           45  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "execution_count": 210,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l5RX_cnFofpM"
      },
      "outputs": [],
      "source": [
        "# #Use pickle to save our model so that we can use it later\n",
        "\n",
        "# import pickle \n",
        "# # Saving model to disk\n",
        "# pickle.dump(CAT_Model, open('model.pkl','wb'))\n",
        "# model=pickle.load(open('model.pkl','rb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "abe_SEJ1ofpM",
        "outputId": "91c74100-887f-46a9-b41e-c6052cc52b9b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Air India', 'GoAir', 'IndiGo', 'Jet Airways', 'Jet Airways Business',\n",
              "       'Multiple carriers', 'Multiple carriers Premium economy', 'SpiceJet',\n",
              "       'Trujet', 'Vistara', 'Vistara Premium economy', 'Source_Chennai',\n",
              "       'Source_Delhi', 'Source_Kolkata', 'Source_Mumbai', 'Destination_Cochin',\n",
              "       'Destination_Delhi', 'Destination_Hyderabad', 'Destination_Kolkata',\n",
              "       'Route', 'Total_Stops', 'Additional_Info', 'Price', 'Day_of_Journey',\n",
              "       'Month_of_Journey', 'Dep_hr', 'Dep_min', 'Arrival_hr', 'Arrival_min',\n",
              "       'duration_hr', 'duration_min'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 213,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sQ-IoAFsofpN"
      },
      "outputs": [],
      "source": [
        "deploy_df=df.drop(['Route','Additional_Info'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hd8cL-XpofpN"
      },
      "outputs": [],
      "source": [
        "deploy_df.to_csv('deploy_df')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NBhZ0HddofpN"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}