flight_prediction.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "f5pz0INhQ34A"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "BmFcZqcERLC5"
      },
      "outputs": [],
      "source": [
        "train_data = pd.read_excel(\"Datasets\\Training_set.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "jb7oFYFcCcuc",
        "outputId": "8aa74b2c-6cb2-4168-9475-9a60c9fca8fd"
      },
      "outputs": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>24/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>22:20</td>\n",
              "      <td>01:10 22 Mar</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>1/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>05:50</td>\n",
              "      <td>13:15</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>9/06/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>09:25</td>\n",
              "      <td>04:25 10 Jun</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IndiGo</td>\n",
              "      <td>12/05/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>18:05</td>\n",
              "      <td>23:30</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1 stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>6218</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways       9/06/2019     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "3       IndiGo      12/05/2019   Kolkata    Banglore        CCU → NAG → BLR   \n",
              "\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  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "wTf5ICCvCc2a",
        "outputId": "00ac80e4-af22-429e-bacb-44deea3f7562"
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              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
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              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10679</th>\n",
              "      <td>Air India</td>\n",
              "      <td>27/04/2019</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → BLR</td>\n",
              "      <td>20:45</td>\n",
              "      <td>23:20</td>\n",
              "      <td>2h 35m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>4145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10680</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>27/04/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>08:20</td>\n",
              "      <td>11:20</td>\n",
              "      <td>3h</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>7229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10681</th>\n",
              "      <td>Vistara</td>\n",
              "      <td>01/03/2019</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>11:30</td>\n",
              "      <td>14:10</td>\n",
              "      <td>2h 40m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>12648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10682</th>\n",
              "      <td>Air India</td>\n",
              "      <td>9/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → GOI → BOM → COK</td>\n",
              "      <td>10:55</td>\n",
              "      <td>19:15</td>\n",
              "      <td>8h 20m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>11753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "           Airline Date_of_Journey    Source Destination  \\\n",
              "10679    Air India      27/04/2019   Kolkata    Banglore   \n",
              "10680  Jet Airways      27/04/2019  Banglore       Delhi   \n",
              "10681      Vistara      01/03/2019  Banglore   New Delhi   \n",
              "10682    Air India       9/05/2019     Delhi      Cochin   \n",
              "\n",
              "                       Route Dep_Time Arrival_Time Duration Total_Stops  \\\n",
              "10679              CCU → BLR    20:45        23:20   2h 35m    non-stop   \n",
              "10680              BLR → DEL    08:20        11:20       3h    non-stop   \n",
              "10681              BLR → DEL    11:30        14:10   2h 40m    non-stop   \n",
              "10682  DEL → GOI → BOM → COK    10:55        19:15   8h 20m     2 stops   \n",
              "\n",
              "      Additional_Info  Price  \n",
              "10679         No info   4145  \n",
              "10680         No info   7229  \n",
              "10681         No info  12648  \n",
              "10682         No info  11753  "
            ]
          },
          "execution_count": 4,
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      ],
      "source": [
        "train_data.tail(4)"
      ]
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    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "ofsIrvmPiBGM",
        "outputId": "1d8305b3-4c39-437a-f94e-79c96a6fec70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              1\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        1\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "h4phU7GuiBIg",
        "outputId": "92f764f5-6a9f-4a9f-f638-253c0d55bbbd"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "10678    False\n",
              "10679    False\n",
              "10680    False\n",
              "10681    False\n",
              "10682    False\n",
              "Name: Total_Stops, Length: 10683, dtype: bool"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "train_data['Total_Stops'].isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
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        "id": "XPvZ8i1ijMC4",
        "outputId": "fee0acb4-f2fe-4b16-c990-ebf348de9411"
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              "      <th>Airline</th>\n",
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              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
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              "    <tr>\n",
              "      <th>9039</th>\n",
              "      <td>Air India</td>\n",
              "      <td>6/05/2019</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>NaN</td>\n",
              "      <td>09:45</td>\n",
              "      <td>09:25 07 May</td>\n",
              "      <td>23h 40m</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No info</td>\n",
              "      <td>7480</td>\n",
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            "text/plain": [
              "        Airline Date_of_Journey Source Destination Route Dep_Time  \\\n",
              "9039  Air India       6/05/2019  Delhi      Cochin   NaN    09:45   \n",
              "\n",
              "      Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
              "9039  09:25 07 May  23h 40m         NaN         No info   7480  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
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      ],
      "source": [
        "train_data[train_data['Total_Stops'].isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "szQD59TNjMFW"
      },
      "outputs": [],
      "source": [
        "train_data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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        "id": "Zavb7oo4jMTN",
        "outputId": "22a14633-8bcd-4c6a-c46c-5a7bf1c0ad4f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline            0\n",
              "Date_of_Journey    0\n",
              "Source             0\n",
              "Destination        0\n",
              "Route              0\n",
              "Dep_Time           0\n",
              "Arrival_Time       0\n",
              "Duration           0\n",
              "Total_Stops        0\n",
              "Additional_Info    0\n",
              "Price              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.isnull().sum()"
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      "cell_type": "code",
      "execution_count": 10,
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        "id": "wjzBjIfTj3Zt",
        "outputId": "5218346b-08ab-4411-9266-2531d4b71042"
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      "outputs": [
        {
          "data": {
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              "Airline            object\n",
              "Date_of_Journey    object\n",
              "Source             object\n",
              "Destination        object\n",
              "Route              object\n",
              "Dep_Time           object\n",
              "Arrival_Time       object\n",
              "Duration           object\n",
              "Total_Stops        object\n",
              "Additional_Info    object\n",
              "Price               int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.dtypes"
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    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
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        "outputId": "6b95fbc5-3f67-4ef1-e58f-93515667f77e"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 10682 entries, 0 to 10682\n",
            "Data columns (total 11 columns):\n",
            " #   Column           Non-Null Count  Dtype \n",
            "---  ------           --------------  ----- \n",
            " 0   Airline          10682 non-null  object\n",
            " 1   Date_of_Journey  10682 non-null  object\n",
            " 2   Source           10682 non-null  object\n",
            " 3   Destination      10682 non-null  object\n",
            " 4   Route            10682 non-null  object\n",
            " 5   Dep_Time         10682 non-null  object\n",
            " 6   Arrival_Time     10682 non-null  object\n",
            " 7   Duration         10682 non-null  object\n",
            " 8   Total_Stops      10682 non-null  object\n",
            " 9   Additional_Info  10682 non-null  object\n",
            " 10  Price            10682 non-null  int64 \n",
            "dtypes: int64(1), object(10)\n",
            "memory usage: 7.2 MB\n"
          ]
        }
      ],
      "source": [
        "train_data.info(memory_usage='deep')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "EkD4qMloj-IC"
      },
      "outputs": [],
      "source": [
        "data = train_data.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "z1MtcfiDj-h7",
        "outputId": "4f1cb942-46c4-4733-a536-da11ddf078a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
              "       'Additional_Info', 'Price'],\n",
              "      dtype='object')"
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          "metadata": {},
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        "data.columns"
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      "execution_count": 14,
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        "id": "Pgsw4dg2j-mg",
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
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              "    </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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0     IndiGo      24/03/2019  Banglore   New Delhi              BLR → DEL   \n",
              "1  Air India       1/05/2019   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "\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  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "qM_VNvPzj3gp",
        "outputId": "ea8c41b5-0370-4502-af08-c71666b5ca76"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline            object\n",
              "Date_of_Journey    object\n",
              "Source             object\n",
              "Destination        object\n",
              "Route              object\n",
              "Dep_Time           object\n",
              "Arrival_Time       object\n",
              "Duration           object\n",
              "Total_Stops        object\n",
              "Additional_Info    object\n",
              "Price               int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hlPmBc0Dl-Rx"
      },
      "outputs": [],
      "source": [
        "def change_into_Datetime(col):\n",
        "  data[col] = pd.to_datetime(data[col])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "RUC3kCPhl-X5"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "from warnings import filterwarnings\n",
        "filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi5DE-3im6aZ",
        "outputId": "8ee4ede3-0b6f-404e-e5f7-67b0badb2527"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
              "       'Additional_Info', 'Price'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "qQMirLtdm6co"
      },
      "outputs": [],
      "source": [
        "for feature in ['Dep_Time', 'Arrival_Time', 'Date_of_Journey']:\n",
        "  change_into_Datetime(feature)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "IDML-BZFm6kb",
        "outputId": "da6f7550-27a6-4f58-fa3d-5c6b6860739f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline                    object\n",
              "Date_of_Journey    datetime64[ns]\n",
              "Source                     object\n",
              "Destination                object\n",
              "Route                      object\n",
              "Dep_Time           datetime64[ns]\n",
              "Arrival_Time       datetime64[ns]\n",
              "Duration                   object\n",
              "Total_Stops                object\n",
              "Additional_Info            object\n",
              "Price                       int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "M4TJ5H5rnq14"
      },
      "outputs": [],
      "source": [
        "data[\"Journey_day\"] = data['Date_of_Journey'].dt.day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "FWw-c5wQnq8i"
      },
      "outputs": [],
      "source": [
        "data[\"Journey_month\"] = data['Date_of_Journey'].dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "HIYZDu47nrJG"
      },
      "outputs": [],
      "source": [
        "data[\"Journey_year\"] = data['Date_of_Journey'].dt.year"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "mJXbAeNAn--E",
        "outputId": "de289801-f342-42f0-c838-27b019204cc1"
      },
      "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",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Journey_year</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>2024-08-25 22:20:00</td>\n",
              "      <td>2024-03-22 01:10:00</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2024-08-25 05:50:00</td>\n",
              "      <td>2024-08-25 13:15:00</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>2024-08-25 09:25:00</td>\n",
              "      <td>2024-06-10 04:25:00</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
              "0 2024-08-25 22:20:00 2024-03-22 01:10:00   2h 50m    non-stop   \n",
              "1 2024-08-25 05:50:00 2024-08-25 13:15:00   7h 25m     2 stops   \n",
              "2 2024-08-25 09:25:00 2024-06-10 04:25:00      19h     2 stops   \n",
              "\n",
              "  Additional_Info  Price  Journey_day  Journey_month  Journey_year  \n",
              "0         No info   3897           24              3          2019  \n",
              "1         No info   7662            1              5          2019  \n",
              "2         No info  13882            9              6          2019  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "ERiPOS30n_Au"
      },
      "outputs": [],
      "source": [
        "def extract_hour_min(df, col):\n",
        "  df[col+\"_hour\"] = df[col].dt.hour\n",
        "  df[col+\"_minute\"] = df[col].dt.minute\n",
        "  return df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0-ZePiJn_C8",
        "outputId": "fb6cbc7c-f4ce-439e-f691-25406e1b532d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Dep_Time', 'Arrival_Time', 'Duration', 'Total_Stops',\n",
              "       'Additional_Info', 'Price', 'Journey_day', 'Journey_month',\n",
              "       'Journey_year'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "KjtBWacqpfpl",
        "outputId": "fa6d16b2-c130-48c6-b6c8-c328938ea0c3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .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>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Dep_Time</th>\n",
              "      <th>Arrival_Time</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</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>2024-08-25 22:20:00</td>\n",
              "      <td>2024-03-22 01:10:00</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2024-08-25 05:50:00</td>\n",
              "      <td>2024-08-25 13:15:00</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>2024-08-25 09:25:00</td>\n",
              "      <td>2024-06-10 04:25:00</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
              "0 2024-08-25 22:20:00 2024-03-22 01:10:00   2h 50m    non-stop   \n",
              "1 2024-08-25 05:50:00 2024-08-25 13:15:00   7h 25m     2 stops   \n",
              "2 2024-08-25 09:25:00 2024-06-10 04:25:00      19h     2 stops   \n",
              "\n",
              "  Additional_Info  Price  Journey_day  Journey_month  Journey_year  \\\n",
              "0         No info   3897           24              3          2019   \n",
              "1         No info   7662            1              5          2019   \n",
              "2         No info  13882            9              6          2019   \n",
              "\n",
              "   Dep_Time_hour  Dep_Time_minute  \n",
              "0             22               20  \n",
              "1              5               50  \n",
              "2              9               25  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extract_hour_min(data, \"Dep_Time\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "hJqW2Fp0pfw8",
        "outputId": "486fbf81-1a52-4ec3-ff73-50ea15d961d8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
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              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</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>2024-08-25 22:20:00</td>\n",
              "      <td>2024-03-22 01:10:00</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>2024-08-25 05:50:00</td>\n",
              "      <td>2024-08-25 13:15:00</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>2024-08-25 09:25:00</td>\n",
              "      <td>2024-06-10 04:25:00</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "             Dep_Time        Arrival_Time Duration Total_Stops  \\\n",
              "0 2024-08-25 22:20:00 2024-03-22 01:10:00   2h 50m    non-stop   \n",
              "1 2024-08-25 05:50:00 2024-08-25 13:15:00   7h 25m     2 stops   \n",
              "2 2024-08-25 09:25:00 2024-06-10 04:25:00      19h     2 stops   \n",
              "\n",
              "  Additional_Info  Price  Journey_day  Journey_month  Journey_year  \\\n",
              "0         No info   3897           24              3          2019   \n",
              "1         No info   7662            1              5          2019   \n",
              "2         No info  13882            9              6          2019   \n",
              "\n",
              "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \n",
              "0             22               20                  1                   10  \n",
              "1              5               50                 13                   15  \n",
              "2              9               25                  4                   25  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extract_hour_min(data, \"Arrival_Time\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "_fETWkwNqKnt"
      },
      "outputs": [],
      "source": [
        "cols_to_drop = ['Arrival_Time','Dep_Time']\n",
        "\n",
        "data.drop(cols_to_drop, axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "KOgfCrSIqKp6",
        "outputId": "c01108b8-54c4-42d8-b311-69d6e7332731"
      },
      "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
              "0   2h 50m    non-stop         No info   3897           24              3   \n",
              "1   7h 25m     2 stops         No info   7662            1              5   \n",
              "2      19h     2 stops         No info  13882            9              6   \n",
              "\n",
              "   Journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "2          2019              9               25                  4   \n",
              "\n",
              "   Arrival_Time_minute  \n",
              "0                   10  \n",
              "1                   15  \n",
              "2                   25  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXP6c96yqKry",
        "outputId": "af347337-08ca-4037-8d46-bd8a1a47d5b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10682, 16)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "a-o7LWpMqK4p"
      },
      "outputs": [],
      "source": [
        "def flight_dep_time(x):\n",
        "  if (x>4) and (x<=8):\n",
        "    return \"Early Morning\"\n",
        "\n",
        "  elif (x>8) and (x<=12):\n",
        "    return \"Morning\"\n",
        "\n",
        "  elif (x>12) and (x<=16):\n",
        "    return \"Afternoon\"\n",
        "\n",
        "  elif (x>16) and (x<=20):\n",
        "    return \"Evening\"\n",
        "\n",
        "  elif (x>20) and (x<=24):\n",
        "    return \"Night\"\n",
        "\n",
        "  else:\n",
        "    return \"Late Night\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "xXRnEiHkto_y",
        "outputId": "ce0de21b-9ac4-482a-80cf-65e970a0eec4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline                        object\n",
              "Date_of_Journey        datetime64[ns]\n",
              "Source                         object\n",
              "Destination                    object\n",
              "Route                          object\n",
              "Duration                       object\n",
              "Total_Stops                    object\n",
              "Additional_Info                object\n",
              "Price                           int64\n",
              "Journey_day                     int32\n",
              "Journey_month                   int32\n",
              "Journey_year                    int32\n",
              "Dep_Time_hour                   int32\n",
              "Dep_Time_minute                 int32\n",
              "Arrival_Time_hour               int32\n",
              "Arrival_Time_minute             int32\n",
              "dtype: object"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "iwUs5PWfrne4",
        "outputId": "685da6c4-5ea7-46a0-ad4c-77257d7eefd8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Dep_Time_hour\n",
              "Early Morning    2880\n",
              "Evening          2357\n",
              "Morning          2209\n",
              "Afternoon        1731\n",
              "Night            1040\n",
              "Late Night        465\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Dep_Time_hour'].apply(flight_dep_time).value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "CCRQChFNsZlf",
        "outputId": "8acd9b86-5fb0-4cb2-8a39-c5a74df195b7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Dep_Time_hour'>"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data['Dep_Time_hour'].apply(flight_dep_time).value_counts().plot(kind=\"bar\", color='green')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IH9rLPkjsZn-",
        "outputId": "a116d919-6f4c-44b9-91e9-571fbd7d52b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: plotly in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (5.23.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from plotly) (9.0.0)\n",
            "Requirement already satisfied: packaging in c:\\users\\sam joe silvano\\appdata\\roaming\\python\\python311\\site-packages (from plotly) (23.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install plotly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFRMaLinsZu2",
        "outputId": "6babab3a-737a-4cd1-f90c-3949d36fe037"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: chart_studio in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (1.1.0)\n",
            "Requirement already satisfied: plotly in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from chart_studio) (5.23.0)\n",
            "Requirement already satisfied: requests in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from chart_studio) (2.31.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from chart_studio) (1.3.4)\n",
            "Requirement already satisfied: six in c:\\users\\sam joe silvano\\appdata\\roaming\\python\\python311\\site-packages (from chart_studio) (1.16.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from plotly->chart_studio) (9.0.0)\n",
            "Requirement already satisfied: packaging in c:\\users\\sam joe silvano\\appdata\\roaming\\python\\python311\\site-packages (from plotly->chart_studio) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->chart_studio) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->chart_studio) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->chart_studio) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sam joe silvano\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->chart_studio) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "!pip install chart_studio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "AumyrVb7wshN",
        "outputId": "38ceefc7-826f-42ae-f089-97593aa70e76"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
              },
              "data": [
                {
                  "alignmentgroup": "True",
                  "hovertemplate": "variable=count<br>Dep_Time_hour=%{x}<br>value=%{y}<extra></extra>",
                  "legendgroup": "count",
                  "marker": {
                    "color": "#636efa",
                    "pattern": {
                      "shape": ""
                    }
                  },
                  "name": "count",
                  "offsetgroup": "count",
                  "orientation": "v",
                  "showlegend": true,
                  "textposition": "auto",
                  "type": "bar",
                  "x": [
                    "Early Morning",
                    "Evening",
                    "Morning",
                    "Afternoon",
                    "Night",
                    "Late Night"
                  ],
                  "xaxis": "x",
                  "y": [
                    2880,
                    2357,
                    2209,
                    1731,
                    1040,
                    465
                  ],
                  "yaxis": "y"
                }
              ],
              "layout": {
                "barmode": "relative",
                "legend": {
                  "title": {
                    "text": "variable"
                  },
                  "tracegroupgap": 0
                },
                "margin": {
                  "t": 60
                },
                "template": {
                  "data": {
                    "bar": [
                      {
                        "error_x": {
                          "color": "#2a3f5f"
                        },
                        "error_y": {
                          "color": "#2a3f5f"
                        },
                        "marker": {
                          "line": {
                            "color": "#E5ECF6",
                            "width": 0.5
                          },
                          "pattern": {
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                      "bgcolor": "#E5ECF6",
                      "caxis": {
                        "gridcolor": "white",
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                        "ticks": ""
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                    },
                    "title": {
                      "x": 0.05
                    },
                    "xaxis": {
                      "automargin": true,
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                      "linecolor": "white",
                      "ticks": "",
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                      "zerolinewidth": 2
                    },
                    "yaxis": {
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          },
          "metadata": {},
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      ],
      "source": [
        "import plotly.express as px\n",
        "import plotly.io as pio\n",
        "\n",
        "# Render the plot\n",
        "pio.renderers.default = 'vscode'\n",
        "\n",
        "fig = px.bar(data['Dep_Time_hour'].apply(flight_dep_time).value_counts())\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "RWOMd0KGxg2J",
        "outputId": "5750134a-5315-48ae-fcb8-f4a753201fe6"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2019</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
              "0   2h 50m    non-stop         No info   3897           24              3   \n",
              "1   7h 25m     2 stops         No info   7662            1              5   \n",
              "2      19h     2 stops         No info  13882            9              6   \n",
              "\n",
              "   Journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "2          2019              9               25                  4   \n",
              "\n",
              "   Arrival_Time_minute  \n",
              "0                   10  \n",
              "1                   15  \n",
              "2                   25  "
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "nw5YzCaV2UQ-"
      },
      "outputs": [],
      "source": [
        "def preprocess_duration(x):\n",
        "  if 'h' not in x:\n",
        "    x = '0h' + ' ' + x\n",
        "  elif 'm' not in x:\n",
        "    x = x + ' ' + '0m'\n",
        "  return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "MciOFFAn2UXx"
      },
      "outputs": [],
      "source": [
        "data['Duration'] = data['Duration'].apply(preprocess_duration)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "UrraBSdH4Kbs",
        "outputId": "9c56f4bd-8e7e-4767-b382-a96eabd87b62"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2h 50m\n",
              "1        7h 25m\n",
              "2        19h 0m\n",
              "3        5h 25m\n",
              "4        4h 45m\n",
              "          ...  \n",
              "10678    2h 30m\n",
              "10679    2h 35m\n",
              "10680     3h 0m\n",
              "10681    2h 40m\n",
              "10682    8h 20m\n",
              "Name: Duration, Length: 10682, dtype: object"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Duration']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "vIhzPVAX4KPx",
        "outputId": "feb29b22-d2d1-4557-afe6-4f1ef78b550f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2h 50m\n",
              "1        7h 25m\n",
              "2        19h 0m\n",
              "3        5h 25m\n",
              "4        4h 45m\n",
              "10678    2h 30m\n",
              "10679    2h 35m\n",
              "10680     3h 0m\n",
              "10681    2h 40m\n",
              "10682    8h 20m\n",
              "Name: Duration, dtype: object"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.concat([data['Duration'].head(5), data['Duration'].tail(5)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Yfadoxkcxg8z",
        "outputId": "ff62023e-7ac2-40de-dcc8-01ac9365c7e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2h 50m'"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Duration'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o7xrweqE5he2",
        "outputId": "8b688565-2558-4811-af28-2efd3bcbc9d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['2h', '50m']"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'2h 50m'.split(' ')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "jr6rAof-5hg-",
        "outputId": "6d4ff4b6-e258-400c-ccf9-10a358b903df"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2h'"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'2h 50m'.split(' ')[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OzbFTu585hjJ",
        "outputId": "bd916213-de2e-4a5d-f9bb-2c96dce03a18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2'"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'2h 50m'.split(' ')[0][0:-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7QPi1Xs513j",
        "outputId": "e62fa6c1-1e46-4649-d57b-adaa68d93b5c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type('2h 50m'.split(' ')[0][0:-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDC0n2S_52CA",
        "outputId": "17a75be6-d619-4464-9c42-22c15772c855"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "int('2h 50m'.split(' ')[0][0:-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvN0b6qg5-OB",
        "outputId": "697a94ac-562b-46fa-e433-0b8417f5963d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "int('2h 50m'.split(' ')[1][0:-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "collapsed": true,
        "id": "mVXOYgtz5-QI"
      },
      "outputs": [],
      "source": [
        "data['Duration_hours'] = data['Duration'].apply(lambda x: int(x.split(' ')[0][0:-1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "collapsed": true,
        "id": "IEZo5xrS5-ST"
      },
      "outputs": [],
      "source": [
        "data['Duration_mins'] = data['Duration'].apply(lambda x: int(x.split(' ')[1][0:-1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "v_lG38q-7A2A",
        "outputId": "f7ccc336-6358-4e3c-ccfa-fdfdcfad7e5c"
      },
      "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Journey_year</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0     IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1  Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  Journey_day  Journey_month  \\\n",
              "0   2h 50m    non-stop         No info   3897           24              3   \n",
              "1   7h 25m     2 stops         No info   7662            1              5   \n",
              "\n",
              "   Journey_year  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0          2019             22               20                  1   \n",
              "1          2019              5               50                 13   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  \n",
              "0                   10               2             50  \n",
              "1                   15               7             25  "
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "kUZ6v8bX7A4a",
        "outputId": "47836ab9-40d0-427c-d159-42c1d2873931"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2h 50m\n",
              "1        7h 25m\n",
              "2        19h 0m\n",
              "3        5h 25m\n",
              "4        4h 45m\n",
              "          ...  \n",
              "10678    2h 30m\n",
              "10679    2h 35m\n",
              "10680     3h 0m\n",
              "10681    2h 40m\n",
              "10682    8h 20m\n",
              "Name: Duration, Length: 10682, dtype: object"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Duration']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkVLfXwS7A6e",
        "outputId": "6d876e19-8c97-4dff-ebb1-d2a260e59b47"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "120"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "2*60"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hpotuEaGHcRl",
        "outputId": "8fc7272e-3828-4de0-c185-a8ff222d588e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'2*60'"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'2*60'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y66Y8qefHcY1",
        "outputId": "db198ae1-54e2-4586-a71d-b6e173fc7fdc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "120"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "eval('2*60')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxMnQbsOOY8X"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "qawuwxWmHcgY"
      },
      "outputs": [],
      "source": [
        "data['Duration_total_mins'] = data['Duration'].str.replace('h', '*60').str.replace(' ', '+').str.replace('m', '*1').apply(eval)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "E9gv3BnPIGQF",
        "outputId": "03b8c550-5b30-4629-8b75-37fd96795ec9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         170\n",
              "1         445\n",
              "2        1140\n",
              "3         325\n",
              "4         285\n",
              "         ... \n",
              "10678     150\n",
              "10679     155\n",
              "10680     180\n",
              "10681     160\n",
              "10682     500\n",
              "Name: Duration_total_mins, Length: 10682, dtype: int64"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Duration_total_mins']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oy5QNHpyPwLF",
        "outputId": "668baef3-315c-4b64-93e0-4e30d9c744cd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Duration', 'Total_Stops', 'Additional_Info', 'Price', 'Journey_day',\n",
              "       'Journey_month', 'Journey_year', 'Dep_Time_hour', 'Dep_Time_minute',\n",
              "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
              "       'Duration_mins', 'Duration_total_mins'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "d3Dk7uQOPwNG",
        "outputId": "b093eaa2-c8b0-4556-867b-084e9220a519"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Duration_total_mins', ylabel='Price'>"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x=\"Duration_total_mins\", y=\"Price\", data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "oP6UVqRQIGV7",
        "outputId": "b757e479-e55c-401d-94a6-d9403de3928e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='Duration_total_mins', ylabel='Price'>"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(x=\"Duration_total_mins\", y=\"Price\", hue=\"Total_Stops\", data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "wr5SDgrvIGbt",
        "outputId": "f426b4b5-24cf-4ac5-d089-c4b316876c3b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x1b6d8cc5150>"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.lmplot(x=\"Duration_total_mins\", y=\"Price\", data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Hq3GGzvpQ2Gd",
        "outputId": "f68d424e-3bd7-48ac-fc00-b4499b7de88a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Route\n",
              "CCU → BOM → BLR          930\n",
              "DEL → BOM → COK          875\n",
              "BLR → BOM → DEL          385\n",
              "BLR → DEL                382\n",
              "CCU → DEL → BLR          300\n",
              "BOM → HYD                207\n",
              "DEL → JAI → BOM → COK    207\n",
              "DEL → AMD → BOM → COK    141\n",
              "DEL → IDR → BOM → COK     86\n",
              "DEL → NAG → BOM → COK     61\n",
              "DEL → ATQ → BOM → COK     38\n",
              "DEL → COK                 34\n",
              "DEL → BHO → BOM → COK     29\n",
              "DEL → BDQ → BOM → COK     28\n",
              "DEL → LKO → BOM → COK     25\n",
              "DEL → JDH → BOM → COK     23\n",
              "CCU → GAU → BLR           22\n",
              "DEL → MAA → BOM → COK     16\n",
              "DEL → IXC → BOM → COK     13\n",
              "BLR → MAA → DEL           10\n",
              "BLR → BDQ → DEL            8\n",
              "DEL → UDR → BOM → COK      7\n",
              "BOM → DEL → HYD            5\n",
              "CCU → BOM → PNQ → BLR      4\n",
              "BLR → BOM → JDH → DEL      3\n",
              "DEL → DED → BOM → COK      2\n",
              "BOM → BDQ → DEL → HYD      2\n",
              "DEL → CCU → BOM → COK      1\n",
              "BOM → VNS → DEL → HYD      1\n",
              "BOM → UDR → DEL → HYD      1\n",
              "BOM → JDH → DEL → HYD      1\n",
              "BOM → IDR → DEL → HYD      1\n",
              "BOM → DED → DEL → HYD      1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[data['Airline'] == \"Jet Airways\"].groupby('Route').size().sort_values(ascending = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rB2wHG9vQ1-j",
        "outputId": "e667e4d0-e5d8-473d-8f09-2a26026b75f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Duration', 'Total_Stops', 'Additional_Info', 'Price', 'Journey_day',\n",
              "       'Journey_month', 'Journey_year', 'Dep_Time_hour', 'Dep_Time_minute',\n",
              "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
              "       'Duration_mins', 'Duration_total_mins'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "c5wMgi79Q2Zu",
        "outputId": "ee756d03-d878-46a4-94cb-049fccdfe4ac"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(y=\"Price\", x=\"Airline\", data=data.sort_values(\"Price\", ascending=False))\n",
        "plt.xticks(rotation=\"vertical\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "XWje_g0XTpOM"
      },
      "outputs": [],
      "source": [
        "cat_col = [col for col in data.columns if data[col].dtype=='object']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "zjsUZHwurqr-"
      },
      "outputs": [],
      "source": [
        "num_col = [col for col in data.columns if data[col].dtype!='object']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUPsrj0grq4V",
        "outputId": "9cc3711d-4226-4482-ac23-aff9ef52d827"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Airline',\n",
              " 'Source',\n",
              " 'Destination',\n",
              " 'Route',\n",
              " 'Duration',\n",
              " 'Total_Stops',\n",
              " 'Additional_Info']"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cat_col"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bieokkLcrq-9",
        "outputId": "55f0b601-c975-42c0-aef8-b677c389963f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Banglore', 'Kolkata', 'Delhi', 'Chennai', 'Mumbai'], dtype=object)"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Source'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "6H3ndrKSsYpe"
      },
      "outputs": [],
      "source": [
        "#One-Hot Encoding\n",
        "for sub_category in data['Source'].unique():\n",
        "  data['Source_'+sub_category] = data['Source'].apply(lambda x: 1 if x == sub_category else 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "ESBE_uMfsYsC",
        "outputId": "4ab1d2a0-79de-430b-f7c7-18ffe439b449"
      },
      "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>...</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Duration_total_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</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>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>170</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Air India</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>445</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Jet Airways</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>1140</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0       IndiGo      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1    Air India      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2  Jet Airways      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  Journey_day  ...  \\\n",
              "0   2h 50m    non-stop         No info   3897           24  ...   \n",
              "1   7h 25m     2 stops         No info   7662            1  ...   \n",
              "2   19h 0m     2 stops         No info  13882            9  ...   \n",
              "\n",
              "   Arrival_Time_hour  Arrival_Time_minute  Duration_hours  Duration_mins  \\\n",
              "0                  1                   10               2             50   \n",
              "1                 13                   15               7             25   \n",
              "2                  4                   25              19              0   \n",
              "\n",
              "   Duration_total_mins  Source_Banglore  Source_Kolkata  Source_Delhi  \\\n",
              "0                  170                1               0             0   \n",
              "1                  445                0               1             0   \n",
              "2                 1140                0               0             1   \n",
              "\n",
              "   Source_Chennai  Source_Mumbai  \n",
              "0               0              0  \n",
              "1               0              0  \n",
              "2               0              0  \n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0Mj2-FLsY1i",
        "outputId": "7edb27c2-4365-468c-bab0-af90d5207b42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['IndiGo', 'Air India', 'Jet Airways', 'SpiceJet',\n",
              "       'Multiple carriers', 'GoAir', 'Vistara', 'Air Asia',\n",
              "       'Vistara Premium economy', 'Jet Airways Business',\n",
              "       'Multiple carriers Premium economy', 'Trujet'], dtype=object)"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Airline'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0Ad8Br_rrGV",
        "outputId": "5e0cece5-60b4-418f-e80b-42b2b579e4e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Airline'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "riJ1h80gTpWL"
      },
      "outputs": [],
      "source": [
        "#Applying target-guided encoding instead of one-hot encoding to avoid curse of dimensionality from one-hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "KCD9AVWLvEQh",
        "outputId": "b88001be-36db-4d1f-e586-13efef4cb40c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Airline\n",
              "Trujet                                4140.000000\n",
              "SpiceJet                              4338.284841\n",
              "Air Asia                              5590.260188\n",
              "IndiGo                                5673.682903\n",
              "GoAir                                 5861.056701\n",
              "Vistara                               7796.348643\n",
              "Vistara Premium economy               8962.333333\n",
              "Air India                             9612.427756\n",
              "Multiple carriers                    10902.678094\n",
              "Multiple carriers Premium economy    11418.846154\n",
              "Jet Airways                          11643.923357\n",
              "Jet Airways Business                 58358.666667\n",
              "Name: Price, dtype: float64"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.groupby(['Airline'])['Price'].mean().sort_values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "d3YhH-R0vETJ"
      },
      "outputs": [],
      "source": [
        "airlines = data.groupby(['Airline'])['Price'].mean().sort_values().index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GF2JkZlAvEVw",
        "outputId": "eec1d77c-fddb-459b-9284-44a3d1e196ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Trujet', 'SpiceJet', 'Air Asia', 'IndiGo', 'GoAir', 'Vistara',\n",
              "       'Vistara Premium economy', 'Air India', 'Multiple carriers',\n",
              "       'Multiple carriers Premium economy', 'Jet Airways',\n",
              "       'Jet Airways Business'],\n",
              "      dtype='object', name='Airline')"
            ]
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "airlines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "VnHxGtigvEYI"
      },
      "outputs": [],
      "source": [
        "dict_airlines = {key:index for index, key in enumerate(airlines, 0)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDkjXoTGwKDx",
        "outputId": "6304fe94-5364-43be-d6a0-2d879a48d5bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Trujet': 0,\n",
              " 'SpiceJet': 1,\n",
              " 'Air Asia': 2,\n",
              " 'IndiGo': 3,\n",
              " 'GoAir': 4,\n",
              " 'Vistara': 5,\n",
              " 'Vistara Premium economy': 6,\n",
              " 'Air India': 7,\n",
              " 'Multiple carriers': 8,\n",
              " 'Multiple carriers Premium economy': 9,\n",
              " 'Jet Airways': 10,\n",
              " 'Jet Airways Business': 11}"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dict_airlines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "Zdn-KDv0wKmI"
      },
      "outputs": [],
      "source": [
        "data['Airline'] = data['Airline'].map(dict_airlines)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "xPwiZsXwwGCe",
        "outputId": "8a9aa243-0d9b-4f20-da4e-94360c09faf3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         3\n",
              "1         7\n",
              "2        10\n",
              "3         3\n",
              "4         3\n",
              "         ..\n",
              "10678     2\n",
              "10679     7\n",
              "10680    10\n",
              "10681     5\n",
              "10682     7\n",
              "Name: Airline, Length: 10682, dtype: int64"
            ]
          },
          "execution_count": 82,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Airline']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "BGG_8gJYwGEs",
        "outputId": "e761e0a8-5b4d-4fca-969a-bdcaa7498988"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Date_of_Journey</th>\n",
              "      <th>Source</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Duration</th>\n",
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              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>...</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Duration_total_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>170</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>445</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>Cochin</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>1140</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline Date_of_Journey    Source Destination                  Route  \\\n",
              "0        3      2019-03-24  Banglore   New Delhi              BLR → DEL   \n",
              "1        7      2019-05-01   Kolkata    Banglore  CCU → IXR → BBI → BLR   \n",
              "2       10      2019-06-09     Delhi      Cochin  DEL → LKO → BOM → COK   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  Journey_day  ...  \\\n",
              "0   2h 50m    non-stop         No info   3897           24  ...   \n",
              "1   7h 25m     2 stops         No info   7662            1  ...   \n",
              "2   19h 0m     2 stops         No info  13882            9  ...   \n",
              "\n",
              "   Arrival_Time_hour  Arrival_Time_minute  Duration_hours  Duration_mins  \\\n",
              "0                  1                   10               2             50   \n",
              "1                 13                   15               7             25   \n",
              "2                  4                   25              19              0   \n",
              "\n",
              "   Duration_total_mins  Source_Banglore  Source_Kolkata  Source_Delhi  \\\n",
              "0                  170                1               0             0   \n",
              "1                  445                0               1             0   \n",
              "2                 1140                0               0             1   \n",
              "\n",
              "   Source_Chennai  Source_Mumbai  \n",
              "0               0              0  \n",
              "1               0              0  \n",
              "2               0              0  \n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nH03ZMgMwGJb",
        "outputId": "b793aca6-e9d2-42be-8674-56f060db2da3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['New Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Delhi', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Destination'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "4U3OfzKbxbEe"
      },
      "outputs": [],
      "source": [
        "data['Destination'].replace('New Delhi', 'Delhi', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLx4mF5oxbVL",
        "outputId": "175b0f65-1e5e-4aa2-f238-71c7cc3ee0a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Delhi', 'Banglore', 'Cochin', 'Kolkata', 'Hyderabad'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Destination'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "bwyj795Lxbs6"
      },
      "outputs": [],
      "source": [
        "dest = data.groupby(['Destination'])['Price'].mean().sort_values().index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQP_G5h7xb3Z",
        "outputId": "9c7a996e-753d-4332-b6b8-eec4f64a8b78"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Kolkata', 'Hyderabad', 'Delhi', 'Banglore', 'Cochin'], dtype='object', name='Destination')"
            ]
          },
          "execution_count": 88,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "Pr97yskwxcRd"
      },
      "outputs": [],
      "source": [
        "dict_dest = {key:index for index, key in enumerate(dest, 0)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9B6FhTJwGLd",
        "outputId": "338c4229-ea09-4149-fded-568152a0d1bb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Kolkata': 0, 'Hyderabad': 1, 'Delhi': 2, 'Banglore': 3, 'Cochin': 4}"
            ]
          },
          "execution_count": 90,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dict_dest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "1RNv1wrewGT9"
      },
      "outputs": [],
      "source": [
        "data['Destination'] = data['Destination'].map(dict_dest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "eGjcQrhSzUqa",
        "outputId": "ff7e92cc-d126-4b43-c84d-af462dbe637a"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        2\n",
              "1        3\n",
              "2        4\n",
              "3        3\n",
              "4        2\n",
              "        ..\n",
              "10678    3\n",
              "10679    3\n",
              "10680    2\n",
              "10681    2\n",
              "10682    4\n",
              "Name: Destination, Length: 10682, dtype: int64"
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          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "data['Destination']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
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          "height": 435
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        "id": "nEQqulkCzU8a",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>2</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>non-stop</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>170</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>3</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>445</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>2019-06-09</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>4</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2 stops</td>\n",
              "      <td>No info</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>1140</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline Date_of_Journey    Source  Destination                  Route  \\\n",
              "0        3      2019-03-24  Banglore            2              BLR → DEL   \n",
              "1        7      2019-05-01   Kolkata            3  CCU → IXR → BBI → BLR   \n",
              "2       10      2019-06-09     Delhi            4  DEL → LKO → BOM → COK   \n",
              "\n",
              "  Duration Total_Stops Additional_Info  Price  Journey_day  ...  \\\n",
              "0   2h 50m    non-stop         No info   3897           24  ...   \n",
              "1   7h 25m     2 stops         No info   7662            1  ...   \n",
              "2   19h 0m     2 stops         No info  13882            9  ...   \n",
              "\n",
              "   Arrival_Time_hour  Arrival_Time_minute  Duration_hours  Duration_mins  \\\n",
              "0                  1                   10               2             50   \n",
              "1                 13                   15               7             25   \n",
              "2                  4                   25              19              0   \n",
              "\n",
              "   Duration_total_mins  Source_Banglore  Source_Kolkata  Source_Delhi  \\\n",
              "0                  170                1               0             0   \n",
              "1                  445                0               1             0   \n",
              "2                 1140                0               0             1   \n",
              "\n",
              "   Source_Chennai  Source_Mumbai  \n",
              "0               0              0  \n",
              "1               0              0  \n",
              "2               0              0  \n",
              "\n",
              "[3 rows x 24 columns]"
            ]
          },
          "execution_count": 93,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "oz2iIf3izVKt",
        "outputId": "6c038da4-3ea3-4eab-bf56-e9b48c921ce7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        non-stop\n",
              "1         2 stops\n",
              "2         2 stops\n",
              "3          1 stop\n",
              "4          1 stop\n",
              "           ...   \n",
              "10678    non-stop\n",
              "10679    non-stop\n",
              "10680    non-stop\n",
              "10681    non-stop\n",
              "10682     2 stops\n",
              "Name: Total_Stops, Length: 10682, dtype: object"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Total_Stops']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ln7UoT71zVbM",
        "outputId": "24f1e6a7-6d1e-4077-e442-e2fe2763ef5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['non-stop', '2 stops', '1 stop', '3 stops', '4 stops'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Total_Stops'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "Iqa76kwJzVo_"
      },
      "outputs": [],
      "source": [
        "# data['Total_Stops'] has ordinal data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "IFpaHtlHzV5H"
      },
      "outputs": [],
      "source": [
        "stop = {'non-stop':0, '1 stop':1, '2 stops':2, '3 stops':3, '4 stops':4}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "h8E6wKQHzWHz"
      },
      "outputs": [],
      "source": [
        "data['Total_Stops'] = data['Total_Stops'].map(stop)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "nOrA92Xi0-fK",
        "outputId": "ce477ef2-26ae-47f7-9455-f18bb91e57a4"
      },
      "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>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Additional_Info</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>...</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Duration_total_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>Banglore</td>\n",
              "      <td>2</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>0</td>\n",
              "      <td>No info</td>\n",
              "      <td>3897</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>170</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline Date_of_Journey    Source  Destination      Route Duration  \\\n",
              "0        3      2019-03-24  Banglore            2  BLR → DEL   2h 50m   \n",
              "\n",
              "   Total_Stops Additional_Info  Price  Journey_day  ...  Arrival_Time_hour  \\\n",
              "0            0         No info   3897           24  ...                  1   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Duration_total_mins  \\\n",
              "0                   10               2             50                  170   \n",
              "\n",
              "   Source_Banglore  Source_Kolkata  Source_Delhi  Source_Chennai  \\\n",
              "0                1               0             0               0   \n",
              "\n",
              "   Source_Mumbai  \n",
              "0              0  \n",
              "\n",
              "[1 rows x 24 columns]"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5y7XJDaB1Ocu",
        "outputId": "5b683d5f-f605-4d76-82a6-0e796450882d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Duration', 'Total_Stops', 'Additional_Info', 'Price', 'Journey_day',\n",
              "       'Journey_month', 'Journey_year', 'Dep_Time_hour', 'Dep_Time_minute',\n",
              "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
              "       'Duration_mins', 'Duration_total_mins', 'Source_Banglore',\n",
              "       'Source_Kolkata', 'Source_Delhi', 'Source_Chennai', 'Source_Mumbai'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 100,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "tsA0TCAp1QOU",
        "outputId": "2386e0a3-878f-4733-a835-ad9d7f9240f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Additional_Info\n",
              "No info                         78.112713\n",
              "In-flight meal not included     18.554578\n",
              "No check-in baggage included     2.995694\n",
              "1 Long layover                   0.177869\n",
              "Change airports                  0.065531\n",
              "Business class                   0.037446\n",
              "No Info                          0.028085\n",
              "1 Short layover                  0.009362\n",
              "Red-eye flight                   0.009362\n",
              "2 Long layover                   0.009362\n",
              "Name: count, dtype: float64"
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Additional_Info'].value_counts()/len(data)*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uff3c4M62JDg",
        "outputId": "1e4bc3c6-7cab-4775-a807-043381483845"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Date_of_Journey', 'Source', 'Destination', 'Route',\n",
              "       'Duration', 'Total_Stops', 'Additional_Info', 'Price', 'Journey_day',\n",
              "       'Journey_month', 'Journey_year', 'Dep_Time_hour', 'Dep_Time_minute',\n",
              "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
              "       'Duration_mins', 'Duration_total_mins', 'Source_Banglore',\n",
              "       'Source_Kolkata', 'Source_Delhi', 'Source_Chennai', 'Source_Mumbai'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOX8Gzbn22yx",
        "outputId": "610f0c7b-33dc-420f-bdc9-938fe71960c5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([2019])"
            ]
          },
          "execution_count": 103,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['Journey_year'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "6iWVMDyR2JWi"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=['Date_of_Journey', 'Additional_Info', 'Duration_total_mins', 'Source', 'Journey_year'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Shiom5la3Ebc",
        "outputId": "ff7428fa-64e8-44be-cb35-e3eb2171c13f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Airline', 'Destination', 'Route', 'Duration', 'Total_Stops', 'Price',\n",
              "       'Journey_day', 'Journey_month', 'Dep_Time_hour', 'Dep_Time_minute',\n",
              "       'Arrival_Time_hour', 'Arrival_Time_minute', 'Duration_hours',\n",
              "       'Duration_mins', 'Source_Banglore', 'Source_Kolkata', 'Source_Delhi',\n",
              "       'Source_Chennai', 'Source_Mumbai'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 105,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "nxAOtLbX3g7D",
        "outputId": "3ff144e1-8a5d-47e6-b9be-5d2fe7e26ddc"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Airline</th>\n",
              "      <th>Destination</th>\n",
              "      <th>Route</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Total_Stops</th>\n",
              "      <th>Price</th>\n",
              "      <th>Journey_day</th>\n",
              "      <th>Journey_month</th>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <th>Duration_hours</th>\n",
              "      <th>Duration_mins</th>\n",
              "      <th>Source_Banglore</th>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <th>Source_Delhi</th>\n",
              "      <th>Source_Chennai</th>\n",
              "      <th>Source_Mumbai</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>BLR → DEL</td>\n",
              "      <td>2h 50m</td>\n",
              "      <td>0</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>CCU → IXR → BBI → BLR</td>\n",
              "      <td>7h 25m</td>\n",
              "      <td>2</td>\n",
              "      <td>7662</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>50</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>DEL → LKO → BOM → COK</td>\n",
              "      <td>19h 0m</td>\n",
              "      <td>2</td>\n",
              "      <td>13882</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>25</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>CCU → NAG → BLR</td>\n",
              "      <td>5h 25m</td>\n",
              "      <td>1</td>\n",
              "      <td>6218</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Airline  Destination                  Route Duration  Total_Stops  Price  \\\n",
              "0        3            2              BLR → DEL   2h 50m            0   3897   \n",
              "1        7            3  CCU → IXR → BBI → BLR   7h 25m            2   7662   \n",
              "2       10            4  DEL → LKO → BOM → COK   19h 0m            2  13882   \n",
              "3        3            3        CCU → NAG → BLR   5h 25m            1   6218   \n",
              "\n",
              "   Journey_day  Journey_month  Dep_Time_hour  Dep_Time_minute  \\\n",
              "0           24              3             22               20   \n",
              "1            1              5              5               50   \n",
              "2            9              6              9               25   \n",
              "3           12              5             18                5   \n",
              "\n",
              "   Arrival_Time_hour  Arrival_Time_minute  Duration_hours  Duration_mins  \\\n",
              "0                  1                   10               2             50   \n",
              "1                 13                   15               7             25   \n",
              "2                  4                   25              19              0   \n",
              "3                 23                   30               5             25   \n",
              "\n",
              "   Source_Banglore  Source_Kolkata  Source_Delhi  Source_Chennai  \\\n",
              "0                1               0             0               0   \n",
              "1                0               1             0               0   \n",
              "2                0               0             1               0   \n",
              "3                0               1             0               0   \n",
              "\n",
              "   Source_Mumbai  \n",
              "0              0  \n",
              "1              0  \n",
              "2              0  \n",
              "3              0  "
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head(4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "GnJCchTt3kUw"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=['Route'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
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        },
        "id": "eQ_YG5LK3xmY",
        "outputId": "0370514f-68f0-4594-888f-968b5b400bbe"
      },
      "outputs": [
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              "   Airline  Destination Duration  Total_Stops  Price  Journey_day  \\\n",
              "0        3            2   2h 50m            0   3897           24   \n",
              "1        7            3   7h 25m            2   7662            1   \n",
              "2       10            4   19h 0m            2  13882            9   \n",
              "\n",
              "   Journey_month  Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  \\\n",
              "0              3             22               20                  1   \n",
              "1              5              5               50                 13   \n",
              "2              6              9               25                  4   \n",
              "\n",
              "   Arrival_Time_minute  Duration_hours  Duration_mins  Source_Banglore  \\\n",
              "0                   10               2             50                1   \n",
              "1                   15               7             25                0   \n",
              "2                   25              19              0                0   \n",
              "\n",
              "   Source_Kolkata  Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0               0             0               0              0  \n",
              "1               1             0               0              0  \n",
              "2               0             1               0              0  "
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        "data.head(3)"
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    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "Kqj9ho-p3zTA"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=['Duration'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
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        },
        "id": "qAVTh9D1342X",
        "outputId": "fa24afd8-4f7f-47b7-a116-98cba3035f10"
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            "text/plain": [
              "   Airline  Destination  Total_Stops  Price  Journey_day  Journey_month  \\\n",
              "0        3            2            0   3897           24              3   \n",
              "1        7            3            2   7662            1              5   \n",
              "2       10            4            2  13882            9              6   \n",
              "\n",
              "   Dep_Time_hour  Dep_Time_minute  Arrival_Time_hour  Arrival_Time_minute  \\\n",
              "0             22               20                  1                   10   \n",
              "1              5               50                 13                   15   \n",
              "2              9               25                  4                   25   \n",
              "\n",
              "   Duration_hours  Duration_mins  Source_Banglore  Source_Kolkata  \\\n",
              "0               2             50                1               0   \n",
              "1               7             25                0               1   \n",
              "2              19              0                0               0   \n",
              "\n",
              "   Source_Delhi  Source_Chennai  Source_Mumbai  \n",
              "0             0               0              0  \n",
              "1             0               0              0  \n",
              "2             1               0              0  "
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          "metadata": {},
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      "source": [
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      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "z00BaFNWJ5yH"
      },
      "outputs": [],
      "source": [
        "def plot(df, col):\n",
        "  fig, (ax1, ax2, ax3) = plt.subplots(3,1)\n",
        "\n",
        "  sns.displot(df[col], ax=ax1, kde=True)\n",
        "  sns.boxplot(df[col], ax=ax2)\n",
        "  sns.histplot(df[col], ax=ax3, kde=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 944
        },
        "id": "ZDMlpP9ZKzTg",
        "outputId": "17ee69f5-4370-4a43-a6d2-bace7c839559"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot(data, 'Price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "rC12GfG836vq"
      },
      "outputs": [],
      "source": [
        "q1 = data['Price'].quantile(0.25)\n",
        "q3 = data['Price'].quantile(0.75)\n",
        "iqr = q3-q1\n",
        "maximum = q3 + (1.5*iqr)\n",
        "minimum = q1 - (1.5*iqr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__KI46ZZbGYF",
        "outputId": "9186029c-1d70-489c-ffc6-1223f9cbe707"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "23017.0\n"
          ]
        }
      ],
      "source": [
        "print(maximum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gp25pH0NbGyJ",
        "outputId": "949561e6-377f-4e5f-959a-12a9e41e5a11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-5367.0\n"
          ]
        }
      ],
      "source": [
        "print(minimum)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZ1xEimVbHHg",
        "outputId": "5278ae79-76df-46f1-d537-4baad928410b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[27430, 36983, 26890, 26890, 25139, 27210, 52229, 26743, 26890, 25735, 27992, 26890, 26890, 23583, 26890, 23533, 24115, 25735, 54826, 31783, 27992, 26890, 26890, 25430, 36235, 27210, 26890, 25735, 54826, 26890, 35185, 79512, 28097, 27992, 26890, 25735, 26092, 31825, 25913, 25735, 27992, 31825, 23267, 62427, 54826, 31825, 25430, 26890, 36235, 23843, 26890, 25735, 28322, 25735, 25735, 31825, 26890, 27992, 34273, 46490, 29528, 26890, 26890, 26890, 34503, 26890, 27992, 26890, 26890, 23170, 24528, 26890, 27992, 25735, 34608, 25703, 26890, 23528, 31825, 27282, 25735, 27992, 52285, 24017, 31945, 26890, 24318, 23677, 27992, 24210, 57209, 26890, 31825, 26480]\n"
          ]
        }
      ],
      "source": [
        "print([price for price in data['Price'] if price > maximum or price < minimum])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qee2mjfmbHcg",
        "outputId": "64450527-2f7f-480c-9b2c-ed172103ac0c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "94"
            ]
          },
          "execution_count": 117,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len([price for price in data['Price'] if price > maximum or price < minimum])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "BROUudq4bHyG"
      },
      "outputs": [],
      "source": [
        "data['Price'] = np.where(data['Price']>=35000, data['Price'].median(), data['Price'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 944
        },
        "id": "4tJuVYL5MlP6",
        "outputId": "48c15b06-1c09-45e2-b3db-6d29910d2b12"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot(data, 'Price')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "5yU72EGnMlXz"
      },
      "outputs": [],
      "source": [
        "X = data.drop(['Price'], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "AqgbASEEO34l"
      },
      "outputs": [],
      "source": [
        "y = data['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "bqnUijavO3-t"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_selection import mutual_info_regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "7Ae3H8k5O4HU"
      },
      "outputs": [],
      "source": [
        "imp = mutual_info_regression(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PF8Fwaa2RLeZ",
        "outputId": "32dac529-3c13-4eaa-b846-437ee2579955"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1.32643837, 1.0628162 , 0.79073663, 0.36733158, 0.6308097 ,\n",
              "       0.92825328, 0.76072517, 1.13940212, 0.90703284, 1.10817021,\n",
              "       0.66525849, 0.38660671, 0.46153434, 0.5124327 , 0.12668471,\n",
              "       0.2014453 ])"
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "imp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "-G8bcVKSUjZn"
      },
      "outputs": [],
      "source": [
        "imp_df = pd.DataFrame(imp, index=X.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "IxDk54c5Ujt6"
      },
      "outputs": [],
      "source": [
        "imp_df.columns = ['Importance']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "Zeq9IaJiUjw-",
        "outputId": "49388eb3-741b-45d9-a72d-68569f4dfd2d"
      },
      "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>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Airline</th>\n",
              "      <td>1.326438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination</th>\n",
              "      <td>1.062816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Stops</th>\n",
              "      <td>0.790737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journey_day</th>\n",
              "      <td>0.367332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journey_month</th>\n",
              "      <td>0.630810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <td>0.928253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <td>0.760725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <td>1.139402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <td>0.907033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_hours</th>\n",
              "      <td>1.108170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_mins</th>\n",
              "      <td>0.665258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Banglore</th>\n",
              "      <td>0.386607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <td>0.461534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Delhi</th>\n",
              "      <td>0.512433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Chennai</th>\n",
              "      <td>0.126685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Mumbai</th>\n",
              "      <td>0.201445</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Importance\n",
              "Airline                1.326438\n",
              "Destination            1.062816\n",
              "Total_Stops            0.790737\n",
              "Journey_day            0.367332\n",
              "Journey_month          0.630810\n",
              "Dep_Time_hour          0.928253\n",
              "Dep_Time_minute        0.760725\n",
              "Arrival_Time_hour      1.139402\n",
              "Arrival_Time_minute    0.907033\n",
              "Duration_hours         1.108170\n",
              "Duration_mins          0.665258\n",
              "Source_Banglore        0.386607\n",
              "Source_Kolkata         0.461534\n",
              "Source_Delhi           0.512433\n",
              "Source_Chennai         0.126685\n",
              "Source_Mumbai          0.201445"
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "imp_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "KyorL0XHUjzk",
        "outputId": "d7b7ddca-d962-474d-bb22-79ef2d86b1b2"
      },
      "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>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Airline</th>\n",
              "      <td>1.326438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival_Time_hour</th>\n",
              "      <td>1.139402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_hours</th>\n",
              "      <td>1.108170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Destination</th>\n",
              "      <td>1.062816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dep_Time_hour</th>\n",
              "      <td>0.928253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival_Time_minute</th>\n",
              "      <td>0.907033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Stops</th>\n",
              "      <td>0.790737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dep_Time_minute</th>\n",
              "      <td>0.760725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duration_mins</th>\n",
              "      <td>0.665258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journey_month</th>\n",
              "      <td>0.630810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Delhi</th>\n",
              "      <td>0.512433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Kolkata</th>\n",
              "      <td>0.461534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Banglore</th>\n",
              "      <td>0.386607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journey_day</th>\n",
              "      <td>0.367332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Mumbai</th>\n",
              "      <td>0.201445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_Chennai</th>\n",
              "      <td>0.126685</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Importance\n",
              "Airline                1.326438\n",
              "Arrival_Time_hour      1.139402\n",
              "Duration_hours         1.108170\n",
              "Destination            1.062816\n",
              "Dep_Time_hour          0.928253\n",
              "Arrival_Time_minute    0.907033\n",
              "Total_Stops            0.790737\n",
              "Dep_Time_minute        0.760725\n",
              "Duration_mins          0.665258\n",
              "Journey_month          0.630810\n",
              "Source_Delhi           0.512433\n",
              "Source_Kolkata         0.461534\n",
              "Source_Banglore        0.386607\n",
              "Journey_day            0.367332\n",
              "Source_Mumbai          0.201445\n",
              "Source_Chennai         0.126685"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "imp_df.sort_values(by='Importance', ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "WVNYjgTrUj2M"
      },
      "outputs": [],
      "source": [
        "#Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "id": "-YpuvjWNUj_E"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "wTOlw2PkRLne"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "uoQ_JRPhRLwc"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "50e-LbC3xjaC"
      },
      "outputs": [],
      "source": [
        "ml_model = RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "zTvj4ZVjxjoC",
        "outputId": "cf44b32e-b758-467e-c708-8d504f8b77e7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ml_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "id": "8isGHaIXxjqU"
      },
      "outputs": [],
      "source": [
        "y_pred = ml_model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whqOQ7Z1x4zN",
        "outputId": "047cdd4f-03de-4e2e-9a96-101637b1bd1d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([16812.35,  5310.47,  8803.84, ...,  3513.81,  6232.57,  6855.84])"
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "id": "393kDUROx46q"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xX6U1i5jx5uW",
        "outputId": "796dda30-cc14-485f-a88a-abc4d05f7cf3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8118939596918583"
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.r2_score(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "9QCvFDp5yGiO"
      },
      "outputs": [],
      "source": [
        "#Model dumping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "aw-spA5VyGp9"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "nt-GfMK2yG0J"
      },
      "outputs": [],
      "source": [
        "file = open(r'Datasets\\rf_random.pkl', 'wb')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "omyZenQxyG_B"
      },
      "outputs": [],
      "source": [
        "pickle.dump(ml_model, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "id": "CarzPdlXzc8i"
      },
      "outputs": [],
      "source": [
        "file.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "id": "fJTd8xPhy-Ld"
      },
      "outputs": [],
      "source": [
        "model = open(r'Datasets\\rf_random.pkl', 'rb')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "id": "GDo3EAlhy-Xm"
      },
      "outputs": [],
      "source": [
        "forest = pickle.load(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "metadata": {
        "id": "IkYEVY_yy-gm"
      },
      "outputs": [],
      "source": [
        "y_pred2 = forest.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 152,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBx_bCQszqg4",
        "outputId": "f82dc824-40f5-4930-cf49-4202e0c31a0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8118939596918583"
            ]
          },
          "execution_count": 152,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "metrics.r2_score(y_test, y_pred2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "id": "lI7wyGxxzqnG"
      },
      "outputs": [],
      "source": [
        "#Mean Absolute Percentage Error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 154,
      "metadata": {
        "id": "8wtiUIsSvSqp"
      },
      "outputs": [],
      "source": [
        "def mape(y_true, y_pred):\n",
        "  y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "  return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 155,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHls_ULrvStL",
        "outputId": "c5259215-6711-4dad-a35d-3e1e6fed3d86"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "13.187443203990185"
            ]
          },
          "execution_count": 155,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mape(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "_EZemVMDx5zn"
      },
      "outputs": [],
      "source": [
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "R2iccHVPvSwX"
      },
      "outputs": [],
      "source": [
        "def predict(ml_model):\n",
        "  model = ml_model.fit(X_train, y_train)\n",
        "  print('Training score : {}'.format(model.score(X_train, y_train)))\n",
        "  y_prediction = model.predict(X_test)\n",
        "  print('predictions are : {}'.format(y_prediction))\n",
        "  print('\\n')\n",
        "  r2_score = metrics.r2_score(y_test, y_prediction)\n",
        "  print('r2_score is : {}'.format(r2_score))\n",
        "  print('Mean Absolute Error is : {}'.format(metrics.mean_absolute_error(y_test, y_prediction)))\n",
        "  print('Mean Squared Error is : {}'.format(metrics.mean_squared_error(y_test, y_prediction)))\n",
        "  print('Root Mean Squared Error is : {}'.format(np.sqrt(metrics.mean_squared_error(y_test, y_prediction))))\n",
        "  print('Mean Absolute Percentage Error is : {}'.format(mape(y_test, y_prediction)))\n",
        "  sns.distplot(y_test-y_prediction)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "NNq1U82VytE6",
        "outputId": "ab877b3f-c664-4a7b-8f4a-ff35bd50dc26"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training score : 0.951653097189252\n",
            "predictions are : [16841.26  5461.06  8792.77 ...  3502.73  6314.74  6878.15]\n",
            "\n",
            "\n",
            "r2_score is : 0.8128680628305923\n",
            "Mean Absolute Error is : 1175.301922406798\n",
            "Mean Squared Error is : 3643009.6374304546\n",
            "Root Mean Squared Error is : 1908.6669791848065\n",
            "Mean Absolute Percentage Error is : 13.181680043321728\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "predict(RandomForestRegressor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "id": "eJLew-j-ytHR"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "VuA7WjcbzLNt",
        "outputId": "cace3ab1-4878-4e2f-80fd-3ff3b9ca18da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training score : 0.966591628243878\n",
            "predictions are : [16840.  4959.  8610. ...  3419.  5797.  6442.]\n",
            "\n",
            "\n",
            "r2_score is : 0.6777484760551487\n",
            "Mean Absolute Error is : 1402.7067640084863\n",
            "Mean Squared Error is : 6273463.659733142\n",
            "Root Mean Squared Error is : 2504.6883358480236\n",
            "Mean Absolute Percentage Error is : 15.466409799399534\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "predict(DecisionTreeRegressor())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "IbnWt0Y2zLVS"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "w5gqweJtzLbP"
      },
      "outputs": [],
      "source": [
        "reg_rf = RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CcaQqtJzLjU",
        "outputId": "2f9c1450-79b0-40c2-93da-bc67d14e45b9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([ 100.,  320.,  540.,  760.,  980., 1200.])"
            ]
          },
          "execution_count": 164,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.linspace(start=100, stop=1200, num=6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "O_HrhK018gcG"
      },
      "outputs": [],
      "source": [
        "n_estimators = [int(x) for x in np.linspace(start=100, stop=1200, num=6)]\n",
        "\n",
        "max_features = ['auto', 'sqrt']\n",
        "\n",
        "max_depth = [int(x) for x in np.linspace(start=5, stop=30, num=4)]\n",
        "\n",
        "min_samples_split = [5, 10, 15, 100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "N7hT7EYf8gei"
      },
      "outputs": [],
      "source": [
        "random_grid = {\n",
        "    'n_estimators': n_estimators,\n",
        "    'max_features': max_features,\n",
        "    'max_depth': max_depth,\n",
        "    'min_samples_split': min_samples_split\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8omOY55I9gsw",
        "outputId": "d4de4235-e047-4709-a536-1b463ff1d88b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'n_estimators': [100, 320, 540, 760, 980, 1200],\n",
              " 'max_features': ['auto', 'sqrt'],\n",
              " 'max_depth': [5, 13, 21, 30],\n",
              " 'min_samples_split': [5, 10, 15, 100]}"
            ]
          },
          "execution_count": 167,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "random_grid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "d424Vf459gxm"
      },
      "outputs": [],
      "source": [
        "rf_random = RandomizedSearchCV(estimator=reg_rf, param_distributions=random_grid, cv=3, verbose=2, n_jobs=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "7GbSivHf-LSV",
        "outputId": "11cdf47e-5521-4aac-9768-7a89b2176247"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=100, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=100, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=100, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=sqrt, min_samples_split=100, n_estimators=320; total time=   0.8s\n",
            "[CV] END max_depth=13, max_features=sqrt, min_samples_split=100, n_estimators=320; total time=   0.8s\n",
            "[CV] END max_depth=13, max_features=sqrt, min_samples_split=100, n_estimators=320; total time=   0.7s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_split=10, n_estimators=100; total time=   0.3s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_split=10, n_estimators=100; total time=   0.3s\n",
            "[CV] END max_depth=30, max_features=sqrt, min_samples_split=10, n_estimators=100; total time=   0.3s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END max_depth=5, max_features=sqrt, min_samples_split=10, n_estimators=100; total time=   0.1s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=100, n_estimators=1200; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=100, n_estimators=1200; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=100, n_estimators=1200; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=auto, min_samples_split=15, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=auto, min_samples_split=15, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=auto, min_samples_split=15, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=auto, min_samples_split=10, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=auto, min_samples_split=10, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=13, max_features=auto, min_samples_split=10, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=21, max_features=sqrt, min_samples_split=10, n_estimators=1200; total time=   5.1s\n",
            "[CV] END max_depth=21, max_features=sqrt, min_samples_split=10, n_estimators=1200; total time=   4.9s\n",
            "[CV] END max_depth=21, max_features=sqrt, min_samples_split=10, n_estimators=1200; total time=   5.3s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=15, n_estimators=100; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=15, n_estimators=100; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=15, n_estimators=100; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=10, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=10, n_estimators=540; total time=   0.0s\n",
            "[CV] END max_depth=5, max_features=auto, min_samples_split=10, n_estimators=540; total time=   0.0s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [5, 13, 21, 30],\n",
              "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                                        &#x27;min_samples_split&#x27;: [5, 10, 15, 100],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 320, 540, 760,\n",
              "                                                         980, 1200]},\n",
              "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=1,\n",
              "                   param_distributions={&#x27;max_depth&#x27;: [5, 13, 21, 30],\n",
              "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                                        &#x27;min_samples_split&#x27;: [5, 10, 15, 100],\n",
              "                                        &#x27;n_estimators&#x27;: [100, 320, 540, 760,\n",
              "                                                         980, 1200]},\n",
              "                   verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: RandomForestRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=21, max_features=&#x27;sqrt&#x27;, min_samples_split=10,\n",
              "                      n_estimators=1200)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=21, max_features=&#x27;sqrt&#x27;, min_samples_split=10,\n",
              "                      n_estimators=1200)</pre></div> </div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_jobs=1,\n",
              "                   param_distributions={'max_depth': [5, 13, 21, 30],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_split': [5, 10, 15, 100],\n",
              "                                        'n_estimators': [100, 320, 540, 760,\n",
              "                                                         980, 1200]},\n",
              "                   verbose=2)"
            ]
          },
          "execution_count": 169,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_random.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Avi_r5AH-Lb5",
        "outputId": "74913898-ca59-4624-8998-1340a6ea874e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'n_estimators': 1200,\n",
              " 'min_samples_split': 10,\n",
              " 'max_features': 'sqrt',\n",
              " 'max_depth': 21}"
            ]
          },
          "execution_count": 176,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_random.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "eyCnH0lI-mIP",
        "outputId": "46a744a5-329a-4f75-f26c-04995de301fb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=21, max_features=&#x27;sqrt&#x27;, min_samples_split=10,\n",
              "                      n_estimators=1200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=21, max_features=&#x27;sqrt&#x27;, min_samples_split=10,\n",
              "                      n_estimators=1200)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestRegressor(max_depth=21, max_features='sqrt', min_samples_split=10,\n",
              "                      n_estimators=1200)"
            ]
          },
          "execution_count": 177,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_random.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4Zw4N4x-riF",
        "outputId": "8708e0e0-d1bb-4f53-c176-019f7446fbc5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8039542734019532"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_random.best_score_"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}