finalflightdelayprediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/guddy2005/Mini-Project-Python_2022/blob/main/finalflightdelayprediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "imorting file\n"
      ]
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-bf01155edc50>:4: DtypeWarning: Columns (7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df3 = pd.read_csv('/content/flights.csv')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  IATA_CODE                              AIRPORT         CITY STATE COUNTRY  \\\n",
            "0       ABE  Lehigh Valley International Airport    Allentown    PA     USA   \n",
            "1       ABI             Abilene Regional Airport      Abilene    TX     USA   \n",
            "2       ABQ    Albuquerque International Sunport  Albuquerque    NM     USA   \n",
            "3       ABR            Aberdeen Regional Airport     Aberdeen    SD     USA   \n",
            "4       ABY   Southwest Georgia Regional Airport       Albany    GA     USA   \n",
            "\n",
            "   LATITUDE  LONGITUDE  YEAR  MONTH  DAY  ...  ARRIVAL_TIME ARRIVAL_DELAY  \\\n",
            "0  40.65236  -75.44040  2015      1    1  ...         408.0         -22.0   \n",
            "1  32.41132  -99.68190  2015      1    1  ...         741.0          -9.0   \n",
            "2  35.04022 -106.60919  2015      1    1  ...         811.0           5.0   \n",
            "3  45.44906  -98.42183  2015      1    1  ...         756.0          -9.0   \n",
            "4  31.53552  -84.19447  2015      1    1  ...         259.0         -21.0   \n",
            "\n",
            "   DIVERTED CANCELLED CANCELLATION_REASON AIR_SYSTEM_DELAY  SECURITY_DELAY  \\\n",
            "0         0         0                 NaN              NaN             NaN   \n",
            "1         0         0                 NaN              NaN             NaN   \n",
            "2         0         0                 NaN              NaN             NaN   \n",
            "3         0         0                 NaN              NaN             NaN   \n",
            "4         0         0                 NaN              NaN             NaN   \n",
            "\n",
            "   AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
            "0            NaN                  NaN            NaN  \n",
            "1            NaN                  NaN            NaN  \n",
            "2            NaN                  NaN            NaN  \n",
            "3            NaN                  NaN            NaN  \n",
            "4            NaN                  NaN            NaN  \n",
            "\n",
            "[5 rows x 38 columns]\n"
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        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df2 = pd.read_csv('/content/airports.csv')\n",
        "df3 = pd.read_csv('/content/flights.csv')\n",
        "\n",
        "# Combine the DataFrames horizontally\n",
        "combined_df = pd.concat([df2, df3], axis=1)\n",
        "\n",
        "# Print some info\n",
        "print(combined_df.head())"
      ]
    },
    {
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              "  IATA_CODE                              AIRPORT         CITY STATE COUNTRY  \\\n",
              "0       ABE  Lehigh Valley International Airport    Allentown    PA     USA   \n",
              "1       ABI             Abilene Regional Airport      Abilene    TX     USA   \n",
              "2       ABQ    Albuquerque International Sunport  Albuquerque    NM     USA   \n",
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              "4       ABY   Southwest Georgia Regional Airport       Albany    GA     USA   \n",
              "\n",
              "   LATITUDE  LONGITUDE  YEAR  MONTH  DAY  ...  ARRIVAL_TIME ARRIVAL_DELAY  \\\n",
              "0  40.65236  -75.44040  2015      1    1  ...         408.0         -22.0   \n",
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              "4  31.53552  -84.19447  2015      1    1  ...         259.0         -21.0   \n",
              "\n",
              "   DIVERTED CANCELLED CANCELLATION_REASON AIR_SYSTEM_DELAY  SECURITY_DELAY  \\\n",
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        "outputId": "5cec8cd4-1878-4809-ddf9-430354b58052"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5819079 entries, 0 to 5819078\n",
            "Data columns (total 38 columns):\n",
            " #   Column               Dtype  \n",
            "---  ------               -----  \n",
            " 0   IATA_CODE            object \n",
            " 1   AIRPORT              object \n",
            " 2   CITY                 object \n",
            " 3   STATE                object \n",
            " 4   COUNTRY              object \n",
            " 5   LATITUDE             float64\n",
            " 6   LONGITUDE            float64\n",
            " 7   YEAR                 int64  \n",
            " 8   MONTH                int64  \n",
            " 9   DAY                  int64  \n",
            " 10  DAY_OF_WEEK          int64  \n",
            " 11  AIRLINE              object \n",
            " 12  FLIGHT_NUMBER        int64  \n",
            " 13  TAIL_NUMBER          object \n",
            " 14  ORIGIN_AIRPORT       object \n",
            " 15  DESTINATION_AIRPORT  object \n",
            " 16  SCHEDULED_DEPARTURE  int64  \n",
            " 17  DEPARTURE_TIME       float64\n",
            " 18  DEPARTURE_DELAY      float64\n",
            " 19  TAXI_OUT             float64\n",
            " 20  WHEELS_OFF           float64\n",
            " 21  SCHEDULED_TIME       float64\n",
            " 22  ELAPSED_TIME         float64\n",
            " 23  AIR_TIME             float64\n",
            " 24  DISTANCE             int64  \n",
            " 25  WHEELS_ON            float64\n",
            " 26  TAXI_IN              float64\n",
            " 27  SCHEDULED_ARRIVAL    int64  \n",
            " 28  ARRIVAL_TIME         float64\n",
            " 29  ARRIVAL_DELAY        float64\n",
            " 30  DIVERTED             int64  \n",
            " 31  CANCELLED            int64  \n",
            " 32  CANCELLATION_REASON  object \n",
            " 33  AIR_SYSTEM_DELAY     float64\n",
            " 34  SECURITY_DELAY       float64\n",
            " 35  AIRLINE_DELAY        float64\n",
            " 36  LATE_AIRCRAFT_DELAY  float64\n",
            " 37  WEATHER_DELAY        float64\n",
            "dtypes: float64(18), int64(10), object(10)\n",
            "memory usage: 1.6+ GB\n"
          ]
        }
      ],
      "source": [
        "combined_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgPhY7I0mXNu"
      },
      "source": [
        "handling missing value\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ye5ECD6ymOmr",
        "outputId": "100ab7fa-ddf9-49f4-9a33-4ec7bb66c285"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IATA_CODE              5818757\n",
              "AIRPORT                5818757\n",
              "CITY                   5818757\n",
              "STATE                  5818757\n",
              "COUNTRY                5818757\n",
              "LATITUDE               5818760\n",
              "LONGITUDE              5818760\n",
              "YEAR                         0\n",
              "MONTH                        0\n",
              "DAY                          0\n",
              "DAY_OF_WEEK                  0\n",
              "AIRLINE                      0\n",
              "FLIGHT_NUMBER                0\n",
              "TAIL_NUMBER              14721\n",
              "ORIGIN_AIRPORT               0\n",
              "DESTINATION_AIRPORT          0\n",
              "SCHEDULED_DEPARTURE          0\n",
              "DEPARTURE_TIME           86153\n",
              "DEPARTURE_DELAY          86153\n",
              "TAXI_OUT                 89047\n",
              "WHEELS_OFF               89047\n",
              "SCHEDULED_TIME               6\n",
              "ELAPSED_TIME            105071\n",
              "AIR_TIME                105071\n",
              "DISTANCE                     0\n",
              "WHEELS_ON                92513\n",
              "TAXI_IN                  92513\n",
              "SCHEDULED_ARRIVAL            0\n",
              "ARRIVAL_TIME             92513\n",
              "ARRIVAL_DELAY           105071\n",
              "DIVERTED                     0\n",
              "CANCELLED                    0\n",
              "CANCELLATION_REASON    5729195\n",
              "AIR_SYSTEM_DELAY       4755640\n",
              "SECURITY_DELAY         4755640\n",
              "AIRLINE_DELAY          4755640\n",
              "LATE_AIRCRAFT_DELAY    4755640\n",
              "WEATHER_DELAY          4755640\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>IATA_CODE</th>\n",
              "      <td>5818757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIRPORT</th>\n",
              "      <td>5818757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CITY</th>\n",
              "      <td>5818757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STATE</th>\n",
              "      <td>5818757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COUNTRY</th>\n",
              "      <td>5818757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LATITUDE</th>\n",
              "      <td>5818760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LONGITUDE</th>\n",
              "      <td>5818760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YEAR</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>MONTH</th>\n",
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              "      <th>DAY</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAY_OF_WEEK</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIRLINE</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLIGHT_NUMBER</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAIL_NUMBER</th>\n",
              "      <td>14721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ORIGIN_AIRPORT</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DESTINATION_AIRPORT</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCHEDULED_DEPARTURE</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEPARTURE_TIME</th>\n",
              "      <td>86153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEPARTURE_DELAY</th>\n",
              "      <td>86153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAXI_OUT</th>\n",
              "      <td>89047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WHEELS_OFF</th>\n",
              "      <td>89047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCHEDULED_TIME</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ELAPSED_TIME</th>\n",
              "      <td>105071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIR_TIME</th>\n",
              "      <td>105071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DISTANCE</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WHEELS_ON</th>\n",
              "      <td>92513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAXI_IN</th>\n",
              "      <td>92513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCHEDULED_ARRIVAL</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARRIVAL_TIME</th>\n",
              "      <td>92513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ARRIVAL_DELAY</th>\n",
              "      <td>105071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIVERTED</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CANCELLED</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CANCELLATION_REASON</th>\n",
              "      <td>5729195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIR_SYSTEM_DELAY</th>\n",
              "      <td>4755640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SECURITY_DELAY</th>\n",
              "      <td>4755640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIRLINE_DELAY</th>\n",
              "      <td>4755640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LATE_AIRCRAFT_DELAY</th>\n",
              "      <td>4755640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WEATHER_DELAY</th>\n",
              "      <td>4755640</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "combined_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRmLZlnQnAlG",
        "outputId": "cc298c47-2068-4051-9ca4-a5ffb0fffddb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-96365c0be5a3>:4: DtypeWarning: Columns (7,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df3 = pd.read_csv('/content/flights.csv')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IATA_CODE              0\n",
            "AIRPORT                0\n",
            "CITY                   0\n",
            "STATE                  0\n",
            "COUNTRY                0\n",
            "LATITUDE               0\n",
            "LONGITUDE              0\n",
            "YEAR                   0\n",
            "MONTH                  0\n",
            "DAY                    0\n",
            "DAY_OF_WEEK            0\n",
            "AIRLINE                0\n",
            "FLIGHT_NUMBER          0\n",
            "TAIL_NUMBER            0\n",
            "ORIGIN_AIRPORT         0\n",
            "DESTINATION_AIRPORT    0\n",
            "SCHEDULED_DEPARTURE    0\n",
            "DEPARTURE_TIME         0\n",
            "DEPARTURE_DELAY        0\n",
            "TAXI_OUT               0\n",
            "WHEELS_OFF             0\n",
            "SCHEDULED_TIME         0\n",
            "ELAPSED_TIME           0\n",
            "AIR_TIME               0\n",
            "DISTANCE               0\n",
            "WHEELS_ON              0\n",
            "TAXI_IN                0\n",
            "SCHEDULED_ARRIVAL      0\n",
            "ARRIVAL_TIME           0\n",
            "ARRIVAL_DELAY          0\n",
            "DIVERTED               0\n",
            "CANCELLED              0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df2 = pd.read_csv('/content/airports.csv')\n",
        "df3 = pd.read_csv('/content/flights.csv')\n",
        "\n",
        "# Combine the DataFrames horizontally\n",
        "combined_df = pd.concat([df2, df3], axis=1)\n",
        "\n",
        "# Handling missing values\n",
        "# For columns with a large number of missing values, we might drop them\n",
        "columns_to_drop = ['CANCELLATION_REASON', 'AIR_SYSTEM_DELAY', 'SECURITY_DELAY', 'AIRLINE_DELAY', 'LATE_AIRCRAFT_DELAY', 'WEATHER_DELAY']\n",
        "combined_df = combined_df.drop(columns=columns_to_drop)\n",
        "\n",
        "# For other columns, we can impute missing values based on the data type and context:\n",
        "\n",
        "# Impute numerical columns with the mean or median\n",
        "numerical_columns = ['LATITUDE', 'LONGITUDE', 'YEAR', 'MONTH', 'DAY', 'DAY_OF_WEEK',\n",
        "                    'FLIGHT_NUMBER', 'DEPARTURE_TIME', 'DEPARTURE_DELAY', 'TAXI_OUT',\n",
        "                    'WHEELS_OFF', 'SCHEDULED_TIME', 'ELAPSED_TIME', 'AIR_TIME', 'DISTANCE',\n",
        "                    'WHEELS_ON', 'TAXI_IN', 'SCHEDULED_ARRIVAL', 'ARRIVAL_TIME', 'ARRIVAL_DELAY']\n",
        "for col in numerical_columns:\n",
        "    combined_df[col] = combined_df[col].fillna(combined_df[col].median())  # Using median to avoid outliers\n",
        "\n",
        "# Impute categorical columns with the most frequent value or a placeholder\n",
        "categorical_columns = ['IATA_CODE', 'AIRPORT', 'CITY', 'STATE', 'COUNTRY', 'AIRLINE', 'TAIL_NUMBER',\n",
        "                      'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT']\n",
        "for col in categorical_columns:\n",
        "    combined_df[col] = combined_df[col].fillna(combined_df[col].mode()[0])  # Using mode for categorical data\n",
        "\n",
        "\n",
        "\n",
        "# Print some info to check for missing values\n",
        "print(combined_df.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "imQW7qW4ngvU",
        "outputId": "01eb5a9f-b40d-4c66-b119-1dc16015974e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  IATA_CODE                              AIRPORT         CITY STATE COUNTRY  \\\n",
              "0       ABE  Lehigh Valley International Airport    Allentown    PA     USA   \n",
              "1       ABI             Abilene Regional Airport      Abilene    TX     USA   \n",
              "2       ABQ    Albuquerque International Sunport  Albuquerque    NM     USA   \n",
              "3       ABR            Aberdeen Regional Airport     Aberdeen    SD     USA   \n",
              "4       ABY   Southwest Georgia Regional Airport       Albany    GA     USA   \n",
              "\n",
              "   LATITUDE  LONGITUDE  YEAR  MONTH  DAY  ...  ELAPSED_TIME AIR_TIME  \\\n",
              "0  40.65236  -75.44040  2015      1    1  ...         194.0    169.0   \n",
              "1  32.41132  -99.68190  2015      1    1  ...         279.0    263.0   \n",
              "2  35.04022 -106.60919  2015      1    1  ...         293.0    266.0   \n",
              "3  45.44906  -98.42183  2015      1    1  ...         281.0    258.0   \n",
              "4  31.53552  -84.19447  2015      1    1  ...         215.0    199.0   \n",
              "\n",
              "   DISTANCE WHEELS_ON TAXI_IN SCHEDULED_ARRIVAL  ARRIVAL_TIME  ARRIVAL_DELAY  \\\n",
              "0      1448     404.0     4.0               430         408.0          -22.0   \n",
              "1      2330     737.0     4.0               750         741.0           -9.0   \n",
              "2      2296     800.0    11.0               806         811.0            5.0   \n",
              "3      2342     748.0     8.0               805         756.0           -9.0   \n",
              "4      1448     254.0     5.0               320         259.0          -21.0   \n",
              "\n",
              "   DIVERTED  CANCELLED  \n",
              "0         0          0  \n",
              "1         0          0  \n",
              "2         0          0  \n",
              "3         0          0  \n",
              "4         0          0  \n",
              "\n",
              "[5 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IATA_CODE</th>\n",
              "      <th>AIRPORT</th>\n",
              "      <th>CITY</th>\n",
              "      <th>STATE</th>\n",
              "      <th>COUNTRY</th>\n",
              "      <th>LATITUDE</th>\n",
              "      <th>LONGITUDE</th>\n",
              "      <th>YEAR</th>\n",
              "      <th>MONTH</th>\n",
              "      <th>DAY</th>\n",
              "      <th>...</th>\n",
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              "      <th>AIR_TIME</th>\n",
              "      <th>DISTANCE</th>\n",
              "      <th>WHEELS_ON</th>\n",
              "      <th>TAXI_IN</th>\n",
              "      <th>SCHEDULED_ARRIVAL</th>\n",
              "      <th>ARRIVAL_TIME</th>\n",
              "      <th>ARRIVAL_DELAY</th>\n",
              "      <th>DIVERTED</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ABE</td>\n",
              "      <td>Lehigh Valley International Airport</td>\n",
              "      <td>Allentown</td>\n",
              "      <td>PA</td>\n",
              "      <td>USA</td>\n",
              "      <td>40.65236</td>\n",
              "      <td>-75.44040</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>194.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>1448</td>\n",
              "      <td>404.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>430</td>\n",
              "      <td>408.0</td>\n",
              "      <td>-22.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABI</td>\n",
              "      <td>Abilene Regional Airport</td>\n",
              "      <td>Abilene</td>\n",
              "      <td>TX</td>\n",
              "      <td>USA</td>\n",
              "      <td>32.41132</td>\n",
              "      <td>-99.68190</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>279.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>2330</td>\n",
              "      <td>737.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>750</td>\n",
              "      <td>741.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABQ</td>\n",
              "      <td>Albuquerque International Sunport</td>\n",
              "      <td>Albuquerque</td>\n",
              "      <td>NM</td>\n",
              "      <td>USA</td>\n",
              "      <td>35.04022</td>\n",
              "      <td>-106.60919</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>293.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>2296</td>\n",
              "      <td>800.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>806</td>\n",
              "      <td>811.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ABR</td>\n",
              "      <td>Aberdeen Regional Airport</td>\n",
              "      <td>Aberdeen</td>\n",
              "      <td>SD</td>\n",
              "      <td>USA</td>\n",
              "      <td>45.44906</td>\n",
              "      <td>-98.42183</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>281.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>2342</td>\n",
              "      <td>748.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>805</td>\n",
              "      <td>756.0</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ABY</td>\n",
              "      <td>Southwest Georgia Regional Airport</td>\n",
              "      <td>Albany</td>\n",
              "      <td>GA</td>\n",
              "      <td>USA</td>\n",
              "      <td>31.53552</td>\n",
              "      <td>-84.19447</td>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>215.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>1448</td>\n",
              "      <td>254.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>320</td>\n",
              "      <td>259.0</td>\n",
              "      <td>-21.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d213e4d-9f7a-4fda-97c0-c05b10b6bccb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6d213e4d-9f7a-4fda-97c0-c05b10b6bccb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6d213e4d-9f7a-4fda-97c0-c05b10b6bccb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-199c868f-06bd-4e05-bc02-d62e3821b360\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-199c868f-06bd-4e05-bc02-d62e3821b360')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-199c868f-06bd-4e05-bc02-d62e3821b360 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "combined_df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "combined_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "k7LUIah0of1d"
      },
      "outputs": [],
      "source": [
        "combined_df['DATETIME_STR'] = combined_df['YEAR'].astype(str) + '-' + \\\n",
        "                               combined_df['MONTH'].astype(str) + '-' + \\\n",
        "                               combined_df['DAY'].astype(str) + ' ' + \\\n",
        "                               combined_df['DEPARTURE_TIME'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "V3dS92IAouai"
      },
      "outputs": [],
      "source": [
        "combined_df['DATETIME'] = pd.to_datetime(combined_df['DATETIME_STR'], format='%Y-%m-%d %H%M', errors='coerce')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "beIM1BUwrJ5E"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Create a MinMaxScaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Select the numerical columns to scale\n",
        "numerical_columns = ['LATITUDE', 'LONGITUDE', 'YEAR', 'MONTH', 'DAY', 'DAY_OF_WEEK',\n",
        "                    'FLIGHT_NUMBER', 'DEPARTURE_TIME', 'DEPARTURE_DELAY', 'TAXI_OUT',\n",
        "                    'WHEELS_OFF', 'SCHEDULED_TIME', 'ELAPSED_TIME', 'AIR_TIME', 'DISTANCE',\n",
        "                    'WHEELS_ON', 'TAXI_IN', 'SCHEDULED_ARRIVAL', 'ARRIVAL_TIME', 'ARRIVAL_DELAY']\n",
        "\n",
        "# Fit the scaler to the data and transform it\n",
        "combined_df[numerical_columns] = scaler.fit_transform(combined_df[numerical_columns])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "N_y56TjBsEOJ"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Define bins for time of day\n",
        "bins = [0, 600, 1200, 1800, 2400]  # 0-6am, 6am-12pm, 12pm-6pm, 6pm-12am\n",
        "labels = ['Night', 'Morning', 'Afternoon', 'Evening']\n",
        "\n",
        "# Create a new column 'TIME_OF_DAY'\n",
        "combined_df['TIME_OF_DAY'] = pd.cut(combined_df['DEPARTURE_TIME'], bins=bins, labels=labels, include_lowest=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "HfKzVTAosG4i"
      },
      "outputs": [],
      "source": [
        "combined_df['FLIGHT_DURATION'] = combined_df['ARRIVAL_TIME'] - combined_df['DEPARTURE_TIME']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "7Qfhez57sJ5X"
      },
      "outputs": [],
      "source": [
        "combined_df['SCHEDULED_DELAY'] = combined_df['SCHEDULED_ARRIVAL'] - combined_df['SCHEDULED_TIME']\n",
        "combined_df['ACTUAL_DELAY'] = combined_df['ARRIVAL_TIME'] - combined_df['SCHEDULED_ARRIVAL']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_8BwDL6wUhL"
      },
      "source": [
        "analying data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "kgeMd0eCwXhp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "95bac950-892a-417f-ca07-91f8a4eaf0cf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(combined_df['ARRIVAL_DELAY'], bins=50, kde=True)\n",
        "plt.title('Distribution of Arrival Delays')\n",
        "plt.xlabel('Arrival Delay (minutes)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "PqLFLg1iwsXf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "4dcf33d1-dd08-4fea-cac3-004a7da0a3c9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='AIRLINE', y='ARRIVAL_DELAY', data=combined_df)\n",
        "plt.title('Average Arrival Delay by Airline')\n",
        "plt.xlabel('Airline')\n",
        "plt.ylabel('Average Delay (minutes)')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "buzr0fqZw_Oc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "604a1035-81a8-4733-bf19-44ed69f9ed57"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='TIME_OF_DAY', y='ARRIVAL_DELAY', data=combined_df)\n",
        "plt.title('Arrival Delay by Time of Day')\n",
        "plt.xlabel('Time of Day')\n",
        "plt.ylabel('Arrival Delay (minutes)')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NUsiQhvOxmkb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "0291b570-bd1d-4de5-b4e5-e19067bab1e1"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'combined_df' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-3f60893426de>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Select only numerical features for correlation analysis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnumerical_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcombined_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Calculate correlation matrix using only numerical features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'combined_df' is not defined"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "# Select only numerical features for correlation analysis\n",
        "numerical_features = combined_df.select_dtypes(include=np.number).columns\n",
        "\n",
        "# Calculate correlation matrix using only numerical features\n",
        "correlation_matrix = combined_df[numerical_features].corr()\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix of Features')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vWXd-ysI3Qfd"
      },
      "outputs": [],
      "source": [
        "# Convert the 'ORIGIN_AIRPORT' column to strings before applying Label Encoding\n",
        "combined_df['ORIGIN_AIRPORT'] = combined_df['ORIGIN_AIRPORT'].astype(str)\n",
        "\n",
        "# Create a LabelEncoder object for ORIGIN_AIRPORT\n",
        "label_encoder_origin = LabelEncoder()\n",
        "\n",
        "# Fit and transform the ORIGIN_AIRPORT column\n",
        "combined_df['ORIGIN_AIRPORT_ENCODED'] = label_encoder_origin.fit_transform(combined_df['ORIGIN_AIRPORT'])\n",
        "\n",
        "# Now you can use the 'ORIGIN_AIRPORT_ENCODED' column in your prediction model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wWK2fDu13dNk"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Convert the 'DESTINATION_AIRPORT' column to strings before applying Label Encoding\n",
        "combined_df['DESTINATION_AIRPORT'] = combined_df['DESTINATION_AIRPORT'].astype(str)\n",
        "\n",
        "# Create a LabelEncoder object for DESTINATION_AIRPORT\n",
        "label_encoder_dest = LabelEncoder()  # Use a new LabelEncoder for destination\n",
        "\n",
        "# Fit and transform the DESTINATION_AIRPORT column\n",
        "combined_df['DESTINATION_AIRPORT_ENCODED'] = label_encoder_dest.fit_transform(combined_df['DESTINATION_AIRPORT'])\n",
        "\n",
        "# Now you can use the 'DESTINATION_AIRPORT_ENCODED' column in your prediction model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nOw-aeOK-0Hi"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BLwaHZ5uBVOz"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Create a LabelEncoder object\n",
        "label_encoder_airline = LabelEncoder()\n",
        "\n",
        "# Fit the encoder to the 'AIRLINE' column and transform it\n",
        "combined_df['AIRLINE_ENCODED'] = label_encoder_airline.fit_transform(combined_df['AIRLINE'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OQLsCIW1B4KU"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cl-0w7jADQLc"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "\n",
        "# Define your features and target variable\n",
        "# Remove 'DATETIME' from features and extract relevant features from it\n",
        "features = ['DEPARTURE_TIME', 'ORIGIN_AIRPORT_ENCODED', 'AIRLINE_ENCODED',\n",
        "            'FLIGHT_NUMBER', 'DESTINATION_AIRPORT_ENCODED']\n",
        "target = 'ARRIVAL_DELAY'\n",
        "\n",
        "# Extract the features and target from your DataFrame\n",
        "X = combined_df[features]\n",
        "y = combined_df[target]\n",
        "\n",
        "#  Extract features from the 'DATETIME' column if needed\n",
        "# For example, to get hour and day of week:\n",
        "combined_df['DATETIME_HOUR'] = combined_df['DATETIME'].dt.hour\n",
        "combined_df['DATETIME_DAYOFWEEK'] = combined_df['DATETIME'].dt.dayofweek\n",
        "\n",
        "# Add the new features to your feature list\n",
        "features.extend(['DATETIME_HOUR', 'DATETIME_DAYOFWEEK'])\n",
        "\n",
        "# Update X with the new features\n",
        "X = combined_df[features]\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Random Forest Regressor model\n",
        "model = RandomForestRegressor(random_state=42)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "# Get feature importances\n",
        "feature_importances = model.feature_importances_\n",
        "\n",
        "# Create a DataFrame to display feature importances\n",
        "importance_df = pd.DataFrame({'Feature': features, 'Importance': feature_importances})\n",
        "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Print or visualize the feature importances\n",
        "print(importance_df)\n",
        "# or\n",
        "# sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
        "# plt.title('Feature Importance')\n",
        "# plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOF1CCSuHvgqk7UNnScVPwo",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
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  "nbformat_minor": 0
}