PREDICT_FLIGHT_DELAY.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "f0d453a2-b8bf-4e2f-9fe5-874bd0149740",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:26.374658Z",
     "start_time": "2024-11-21T20:21:26.368786Z"
    }
   },
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
    "from sklearn.calibration import calibration_curve\n",
    "import xgboost as xgb\n",
    "import joblib\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ],
   "outputs": [],
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "id": "91368057-c86f-4efd-85a9-6c032eace0eb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:26.618839Z",
     "start_time": "2024-11-21T20:21:26.559249Z"
    }
   },
   "source": [
    "# Step 1: Load the datasets\n",
    "arrivals_data = pd.read_csv(r\"C:\\Users\\dines\\OneDrive\\Desktop\\data\\Detailed_Statistics_Arrivals.csv\")\n",
    "departures_data = pd.read_csv(r\"C:\\Users\\dines\\OneDrive\\Desktop\\data\\Detailed_Statistics_Departures.csv\")\n",
    "weather_arrivals_data = pd.read_csv(r\"C:\\Users\\dines\\OneDrive\\Desktop\\data\\weather_data_arrivals.csv\")\n",
    "weather_departures_data = pd.read_csv(r\"C:\\Users\\dines\\OneDrive\\Desktop\\data\\weather_data_depature.csv\")\n",
    "   \n",
    "# Print dataset\n",
    "print(\"Arrivals Data:\", arrivals_data)\n",
    "print(\"Departures Data :\", departures_data)\n",
    "print(\"Weather Arrivals Data :\", weather_arrivals_data)\n",
    "print(\"Weather Departures Data :\", weather_departures_data)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arrivals Data:      Carrier Code Date (DD/MM/YYYY)  Flight Number Tail Number Origin Airport  \\\n",
      "0              DL        01-08-2024            314      N533DT            BOS   \n",
      "1              DL        01-08-2024            315      N707TW            JFK   \n",
      "2              DL        01-08-2024            340      N813DN            ATL   \n",
      "3              DL        01-08-2024            361      N595NW            DTW   \n",
      "4              DL        01-08-2024            399      N889DN            ATL   \n",
      "...           ...               ...            ...         ...            ...   \n",
      "1210           DL        31-08-2024           2074      N930DZ            MSP   \n",
      "1211           DL        31-08-2024           2078      N928DU            MSP   \n",
      "1212           DL        31-08-2024           2239      N371NW            LAX   \n",
      "1213           DL        31-08-2024           2267      N832DN            LAX   \n",
      "1214           DL        31-08-2024           2985      N133DU            SEA   \n",
      "\n",
      "     Scheduled Arrival Time Actual Arrival Time  Arrival Delay (Minutes)  \\\n",
      "0                  00:47:00            00:17:00                      -30   \n",
      "1                  01:19:00            01:47:00                       28   \n",
      "2                  20:42:00            21:21:00                       39   \n",
      "3                  10:30:00            10:19:00                      -11   \n",
      "4                  00:31:00            01:03:00                       32   \n",
      "...                     ...                 ...                      ...   \n",
      "1210               13:08:00            12:46:00                      -22   \n",
      "1211               11:06:00            10:46:00                      -20   \n",
      "1212               21:38:00            23:11:00                       93   \n",
      "1213               13:29:00            13:16:00                      -13   \n",
      "1214               12:13:00            12:09:00                       -4   \n",
      "\n",
      "      Delay Carrier (Minutes)  Delay Weather (Minutes)  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           4                        0   \n",
      "3                           0                        0   \n",
      "4                          32                        0   \n",
      "...                       ...                      ...   \n",
      "1210                        0                        0   \n",
      "1211                        0                        0   \n",
      "1212                       93                        0   \n",
      "1213                        0                        0   \n",
      "1214                        0                        0   \n",
      "\n",
      "      Delay National Aviation System (Minutes)  Delay Security (Minutes)  \\\n",
      "0                                            0                         0   \n",
      "1                                           17                         0   \n",
      "2                                            0                         0   \n",
      "3                                            0                         0   \n",
      "4                                            0                         0   \n",
      "...                                        ...                       ...   \n",
      "1210                                         0                         0   \n",
      "1211                                         0                         0   \n",
      "1212                                         0                         0   \n",
      "1213                                         0                         0   \n",
      "1214                                         0                         0   \n",
      "\n",
      "      Delay Late Aircraft Arrival (Minutes)  \n",
      "0                                         0  \n",
      "1                                        11  \n",
      "2                                        35  \n",
      "3                                         0  \n",
      "4                                         0  \n",
      "...                                     ...  \n",
      "1210                                      0  \n",
      "1211                                      0  \n",
      "1212                                      0  \n",
      "1213                                      0  \n",
      "1214                                      0  \n",
      "\n",
      "[1215 rows x 13 columns]\n",
      "Departures Data :     Carrier Code Date (DD/MM/YYYY)  Flight Number Tail Number  \\\n",
      "0             DL        01-08-2024            348      N544US   \n",
      "1             DL        01-08-2024            546      N518DQ   \n",
      "2             DL        01-08-2024            548      N538DN   \n",
      "3             DL        01-08-2024            672      N6704Z   \n",
      "4             DL        01-08-2024            791      N580DT   \n",
      "..           ...               ...            ...         ...   \n",
      "840           DL        31-08-2024           2419      N3754A   \n",
      "841           DL        31-08-2024           2444      N399DA   \n",
      "842           DL        31-08-2024           2447      N399DA   \n",
      "843           DL        31-08-2024           2449      N381DN   \n",
      "844           DL        31-08-2024           2553      N367DN   \n",
      "\n",
      "    Destination Airport Scheduled departure time Actual departure time  \\\n",
      "0                   ATL                 10:04:00              10:02:00   \n",
      "1                   JFK                 06:07:00              06:12:00   \n",
      "2                   JFK                 22:59:00              23:07:00   \n",
      "3                   ATL                 15:03:00              15:05:00   \n",
      "4                   JFK                 12:08:00              12:02:00   \n",
      "..                  ...                      ...                   ...   \n",
      "840                 SEA                 11:15:00              11:10:00   \n",
      "841                 SEA                 06:02:00              06:06:00   \n",
      "842                 SEA                 13:56:00              13:59:00   \n",
      "843                 SEA                 15:45:00              15:40:00   \n",
      "844                 MSP                 06:00:00              05:53:00   \n",
      "\n",
      "     Departure delay (Minutes)  Delay Carrier (Minutes)  \\\n",
      "0                           -2                        0   \n",
      "1                            5                        0   \n",
      "2                            8                        0   \n",
      "3                            2                        0   \n",
      "4                           -6                        0   \n",
      "..                         ...                      ...   \n",
      "840                         -5                        0   \n",
      "841                          4                        0   \n",
      "842                          3                        0   \n",
      "843                         -5                        0   \n",
      "844                         -7                        0   \n",
      "\n",
      "     Delay Weather (Minutes)  Delay National Aviation System (Minutes)  \\\n",
      "0                          0                                         0   \n",
      "1                          0                                         0   \n",
      "2                          0                                         0   \n",
      "3                          0                                        20   \n",
      "4                          0                                         0   \n",
      "..                       ...                                       ...   \n",
      "840                        0                                         0   \n",
      "841                        0                                         0   \n",
      "842                        0                                         0   \n",
      "843                        0                                         0   \n",
      "844                        0                                         0   \n",
      "\n",
      "     Delay Security (Minutes)  Delay Late Aircraft Arrival (Minutes)  \n",
      "0                           0                                      0  \n",
      "1                           0                                      0  \n",
      "2                           0                                      0  \n",
      "3                           0                                      0  \n",
      "4                           0                                      0  \n",
      "..                        ...                                    ...  \n",
      "840                         0                                      0  \n",
      "841                         0                                      0  \n",
      "842                         0                                      0  \n",
      "843                         0                                      0  \n",
      "844                         0                                      0  \n",
      "\n",
      "[845 rows x 13 columns]\n",
      "Weather Arrivals Data :         STATION                 DATE REPORT_TYPE  SOURCE  AWND  \\\n",
      "0   99999923272  2024-08-01T23:59:00       SOD         6   NaN   \n",
      "1   99999923272  2024-08-02T23:59:00       SOD         6   NaN   \n",
      "2   99999923272  2024-08-03T23:59:00       SOD         6   NaN   \n",
      "3   99999923272  2024-08-04T23:59:00       SOD         6   NaN   \n",
      "4   99999923272  2024-08-05T23:59:00       SOD         6   NaN   \n",
      "5   99999923272  2024-08-06T23:59:00       SOD         6   NaN   \n",
      "6   99999923272  2024-08-07T23:59:00       SOD         6   NaN   \n",
      "7   99999923272  2024-08-08T23:59:00       SOD         6   NaN   \n",
      "8   99999923272  2024-08-09T23:59:00       SOD         6   NaN   \n",
      "9   99999923272  2024-08-10T23:59:00       SOD         6   NaN   \n",
      "10  99999923272  2024-08-11T23:59:00       SOD         6   NaN   \n",
      "11  99999923272  2024-08-12T23:59:00       SOD         6   NaN   \n",
      "12  99999923272  2024-08-13T23:59:00       SOD         6   NaN   \n",
      "13  99999923272  2024-08-14T23:59:00       SOD         6   NaN   \n",
      "14  99999923272  2024-08-15T23:59:00       SOD         6   NaN   \n",
      "15  99999923272  2024-08-16T23:59:00       SOD         6   NaN   \n",
      "16  99999923272  2024-08-17T23:59:00       SOD         6   NaN   \n",
      "17  99999923272  2024-08-18T23:59:00       SOD         6   NaN   \n",
      "18  99999923272  2024-08-19T23:59:00       SOD         6   NaN   \n",
      "19  99999923272  2024-08-20T23:59:00       SOD         6   NaN   \n",
      "20  99999923272  2024-08-21T23:59:00       SOD         6   NaN   \n",
      "21  99999923272  2024-08-22T23:59:00       SOD         6   NaN   \n",
      "22  99999923272  2024-08-23T23:59:00       SOD         6   NaN   \n",
      "23  99999923272  2024-08-24T09:59:00       FM-15       6   NaN   \n",
      "24  99999923272  2024-08-24T23:59:00       SOD         6   NaN   \n",
      "\n",
      "    BackupDirection  BackupDistance  BackupDistanceUnit  BackupElements  \\\n",
      "0               NaN             NaN                 NaN             NaN   \n",
      "1               NaN             NaN                 NaN             NaN   \n",
      "2               NaN             NaN                 NaN             NaN   \n",
      "3               NaN             NaN                 NaN             NaN   \n",
      "4               NaN             NaN                 NaN             NaN   \n",
      "5               NaN             NaN                 NaN             NaN   \n",
      "6               NaN             NaN                 NaN             NaN   \n",
      "7               NaN             NaN                 NaN             NaN   \n",
      "8               NaN             NaN                 NaN             NaN   \n",
      "9               NaN             NaN                 NaN             NaN   \n",
      "10              NaN             NaN                 NaN             NaN   \n",
      "11              NaN             NaN                 NaN             NaN   \n",
      "12              NaN             NaN                 NaN             NaN   \n",
      "13              NaN             NaN                 NaN             NaN   \n",
      "14              NaN             NaN                 NaN             NaN   \n",
      "15              NaN             NaN                 NaN             NaN   \n",
      "16              NaN             NaN                 NaN             NaN   \n",
      "17              NaN             NaN                 NaN             NaN   \n",
      "18              NaN             NaN                 NaN             NaN   \n",
      "19              NaN             NaN                 NaN             NaN   \n",
      "20              NaN             NaN                 NaN             NaN   \n",
      "21              NaN             NaN                 NaN             NaN   \n",
      "22              NaN             NaN                 NaN             NaN   \n",
      "23              NaN             NaN                 NaN             NaN   \n",
      "24              NaN             NaN                 NaN             NaN   \n",
      "\n",
      "    BackupElevation  ...  ShortDurationPrecipitationValue045  \\\n",
      "0               NaN  ...                                 NaN   \n",
      "1               NaN  ...                                 NaN   \n",
      "2               NaN  ...                                 NaN   \n",
      "3               NaN  ...                                 NaN   \n",
      "4               NaN  ...                                 NaN   \n",
      "5               NaN  ...                                 NaN   \n",
      "6               NaN  ...                                 NaN   \n",
      "7               NaN  ...                                 NaN   \n",
      "8               NaN  ...                                 NaN   \n",
      "9               NaN  ...                                 NaN   \n",
      "10              NaN  ...                                 NaN   \n",
      "11              NaN  ...                                 NaN   \n",
      "12              NaN  ...                                 NaN   \n",
      "13              NaN  ...                                 NaN   \n",
      "14              NaN  ...                                 NaN   \n",
      "15              NaN  ...                                 NaN   \n",
      "16              NaN  ...                                 NaN   \n",
      "17              NaN  ...                                 NaN   \n",
      "18              NaN  ...                                 NaN   \n",
      "19              NaN  ...                                 NaN   \n",
      "20              NaN  ...                                 NaN   \n",
      "21              NaN  ...                                 NaN   \n",
      "22              NaN  ...                                 NaN   \n",
      "23              NaN  ...                                 NaN   \n",
      "24              NaN  ...                                 NaN   \n",
      "\n",
      "    ShortDurationPrecipitationValue060  ShortDurationPrecipitationValue080  \\\n",
      "0                                  NaN                                 NaN   \n",
      "1                                  NaN                                 NaN   \n",
      "2                                  NaN                                 NaN   \n",
      "3                                  NaN                                 NaN   \n",
      "4                                  NaN                                 NaN   \n",
      "5                                  NaN                                 NaN   \n",
      "6                                  NaN                                 NaN   \n",
      "7                                  NaN                                 NaN   \n",
      "8                                  NaN                                 NaN   \n",
      "9                                  NaN                                 NaN   \n",
      "10                                 NaN                                 NaN   \n",
      "11                                 NaN                                 NaN   \n",
      "12                                 NaN                                 NaN   \n",
      "13                                 NaN                                 NaN   \n",
      "14                                 NaN                                 NaN   \n",
      "15                                 NaN                                 NaN   \n",
      "16                                 NaN                                 NaN   \n",
      "17                                 NaN                                 NaN   \n",
      "18                                 NaN                                 NaN   \n",
      "19                                 NaN                                 NaN   \n",
      "20                                 NaN                                 NaN   \n",
      "21                                 NaN                                 NaN   \n",
      "22                                 NaN                                 NaN   \n",
      "23                                 NaN                                 NaN   \n",
      "24                                 NaN                                 NaN   \n",
      "\n",
      "    ShortDurationPrecipitationValue100  ShortDurationPrecipitationValue120  \\\n",
      "0                                  NaN                                 NaN   \n",
      "1                                  NaN                                 NaN   \n",
      "2                                  NaN                                 NaN   \n",
      "3                                  NaN                                 NaN   \n",
      "4                                  NaN                                 NaN   \n",
      "5                                  NaN                                 NaN   \n",
      "6                                  NaN                                 NaN   \n",
      "7                                  NaN                                 NaN   \n",
      "8                                  NaN                                 NaN   \n",
      "9                                  NaN                                 NaN   \n",
      "10                                 NaN                                 NaN   \n",
      "11                                 NaN                                 NaN   \n",
      "12                                 NaN                                 NaN   \n",
      "13                                 NaN                                 NaN   \n",
      "14                                 NaN                                 NaN   \n",
      "15                                 NaN                                 NaN   \n",
      "16                                 NaN                                 NaN   \n",
      "17                                 NaN                                 NaN   \n",
      "18                                 NaN                                 NaN   \n",
      "19                                 NaN                                 NaN   \n",
      "20                                 NaN                                 NaN   \n",
      "21                                 NaN                                 NaN   \n",
      "22                                 NaN                                 NaN   \n",
      "23                                 NaN                                 NaN   \n",
      "24                                 NaN                                 NaN   \n",
      "\n",
      "    ShortDurationPrecipitationValue150  ShortDurationPrecipitationValue180  \\\n",
      "0                                  NaN                                 NaN   \n",
      "1                                  NaN                                 NaN   \n",
      "2                                  NaN                                 NaN   \n",
      "3                                  NaN                                 NaN   \n",
      "4                                  NaN                                 NaN   \n",
      "5                                  NaN                                 NaN   \n",
      "6                                  NaN                                 NaN   \n",
      "7                                  NaN                                 NaN   \n",
      "8                                  NaN                                 NaN   \n",
      "9                                  NaN                                 NaN   \n",
      "10                                 NaN                                 NaN   \n",
      "11                                 NaN                                 NaN   \n",
      "12                                 NaN                                 NaN   \n",
      "13                                 NaN                                 NaN   \n",
      "14                                 NaN                                 NaN   \n",
      "15                                 NaN                                 NaN   \n",
      "16                                 NaN                                 NaN   \n",
      "17                                 NaN                                 NaN   \n",
      "18                                 NaN                                 NaN   \n",
      "19                                 NaN                                 NaN   \n",
      "20                                 NaN                                 NaN   \n",
      "21                                 NaN                                 NaN   \n",
      "22                                 NaN                                 NaN   \n",
      "23                                 NaN                                 NaN   \n",
      "24                                 NaN                                 NaN   \n",
      "\n",
      "    Sunrise  Sunset  WindEquipmentChangeDate  \n",
      "0     514.0  1917.0                      NaN  \n",
      "1     515.0  1916.0                      NaN  \n",
      "2     516.0  1915.0                      NaN  \n",
      "3     516.0  1914.0                      NaN  \n",
      "4     517.0  1913.0                      NaN  \n",
      "5     518.0  1912.0                      NaN  \n",
      "6     519.0  1911.0                      NaN  \n",
      "7     520.0  1910.0                      NaN  \n",
      "8     521.0  1909.0                      NaN  \n",
      "9     522.0  1908.0                      NaN  \n",
      "10    522.0  1906.0                      NaN  \n",
      "11    523.0  1905.0                      NaN  \n",
      "12    524.0  1904.0                      NaN  \n",
      "13    525.0  1903.0                      NaN  \n",
      "14    526.0  1901.0                      NaN  \n",
      "15    527.0  1900.0                      NaN  \n",
      "16    528.0  1859.0                      NaN  \n",
      "17    529.0  1857.0                      NaN  \n",
      "18    529.0  1856.0                      NaN  \n",
      "19    530.0  1855.0                      NaN  \n",
      "20    531.0  1853.0                      NaN  \n",
      "21    532.0  1852.0                      NaN  \n",
      "22    533.0  1851.0                      NaN  \n",
      "23      NaN     NaN                      NaN  \n",
      "24    534.0  1849.0                      NaN  \n",
      "\n",
      "[25 rows x 124 columns]\n",
      "Weather Departures Data :           STATION              DATE REPORT_TYPE  SOURCE  AWND BackupDirection  \\\n",
      "0     72278023183  01-08-2024 00:51       FM-15       7   NaN               N   \n",
      "1     72278023183  01-08-2024 01:51       FM-15       7   NaN               N   \n",
      "2     72278023183  01-08-2024 02:00       FM-12       4   NaN               N   \n",
      "3     72278023183  01-08-2024 02:51       FM-15       7   NaN               N   \n",
      "4     72278023183  01-08-2024 03:51       FM-15       7   NaN               N   \n",
      "...           ...               ...         ...     ...   ...             ...   \n",
      "1080  72278023183  31-08-2024 22:51       FM-15       7   NaN               N   \n",
      "1081  72278023183  31-08-2024 23:00       FM-12       4   NaN               N   \n",
      "1082  72278023183  31-08-2024 23:51       FM-15       7   NaN               N   \n",
      "1083  72278023183  31-08-2024 23:59       SOD         6   NaN               N   \n",
      "1084  72278023183  31-08-2024 23:59       SOM         6   7.4               N   \n",
      "\n",
      "      BackupDistance BackupDistanceUnit BackupElements  BackupElevation  ...  \\\n",
      "0                0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "1                0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "2                0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "3                0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "4                0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "...              ...                ...            ...              ...  ...   \n",
      "1080             0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "1081             0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "1082             0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "1083             0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "1084             0.5                 mi   TEMP, PRECIP             1107  ...   \n",
      "\n",
      "      ShortDurationPrecipitationValue045 ShortDurationPrecipitationValue060  \\\n",
      "0                                    NaN                                NaN   \n",
      "1                                    NaN                                NaN   \n",
      "2                                    NaN                                NaN   \n",
      "3                                    NaN                                NaN   \n",
      "4                                    NaN                                NaN   \n",
      "...                                  ...                                ...   \n",
      "1080                                 NaN                                NaN   \n",
      "1081                                 NaN                                NaN   \n",
      "1082                                 NaN                                NaN   \n",
      "1083                                 NaN                                NaN   \n",
      "1084                                0.11                               0.13   \n",
      "\n",
      "      ShortDurationPrecipitationValue080  ShortDurationPrecipitationValue100  \\\n",
      "0                                    NaN                                 NaN   \n",
      "1                                    NaN                                 NaN   \n",
      "2                                    NaN                                 NaN   \n",
      "3                                    NaN                                 NaN   \n",
      "4                                    NaN                                 NaN   \n",
      "...                                  ...                                 ...   \n",
      "1080                                 NaN                                 NaN   \n",
      "1081                                 NaN                                 NaN   \n",
      "1082                                 NaN                                 NaN   \n",
      "1083                                 NaN                                 NaN   \n",
      "1084                                0.16                                0.16   \n",
      "\n",
      "     ShortDurationPrecipitationValue120  ShortDurationPrecipitationValue150  \\\n",
      "0                                   NaN                                 NaN   \n",
      "1                                   NaN                                 NaN   \n",
      "2                                   NaN                                 NaN   \n",
      "3                                   NaN                                 NaN   \n",
      "4                                   NaN                                 NaN   \n",
      "...                                 ...                                 ...   \n",
      "1080                                NaN                                 NaN   \n",
      "1081                                NaN                                 NaN   \n",
      "1082                                NaN                                 NaN   \n",
      "1083                                NaN                                 NaN   \n",
      "1084                               0.18                                0.18   \n",
      "\n",
      "      ShortDurationPrecipitationValue180  Sunrise  Sunset  \\\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",
      "1080                                 NaN      NaN     NaN   \n",
      "1081                                 NaN      NaN     NaN   \n",
      "1082                                 NaN      NaN     NaN   \n",
      "1083                                 NaN    602.0  1853.0   \n",
      "1084                                0.24      NaN     NaN   \n",
      "\n",
      "      WindEquipmentChangeDate  \n",
      "0            03-04-2007 00:00  \n",
      "1            03-04-2007 00:00  \n",
      "2            03-04-2007 00:00  \n",
      "3            03-04-2007 00:00  \n",
      "4            03-04-2007 00:00  \n",
      "...                       ...  \n",
      "1080         03-04-2007 00:00  \n",
      "1081         03-04-2007 00:00  \n",
      "1082         03-04-2007 00:00  \n",
      "1083         03-04-2007 00:00  \n",
      "1084         03-04-2007 00:00  \n",
      "\n",
      "[1085 rows x 124 columns]\n"
     ]
    }
   ],
   "execution_count": 41
  },
  {
   "cell_type": "code",
   "id": "d93e2a0b-787f-4091-ad62-3362cec4cea0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:26.670517Z",
     "start_time": "2024-11-21T20:21:26.649850Z"
    }
   },
   "source": [
    "# Step 2: Clean and preprocess the flight data\n",
    "# Standardize column names\n",
    "arrivals_data.columns = [col.strip().replace(' ', '_').lower() for col in arrivals_data.columns]\n",
    "departures_data.columns = [col.strip().replace(' ', '_').lower() for col in departures_data.columns]\n",
    "\n",
    "# Parse the date column into datetime\n",
    "arrivals_data['date'] = pd.to_datetime(arrivals_data['date_(dd/mm/yyyy)'], format='%d-%m-%Y', errors='coerce')\n",
    "departures_data['date'] = pd.to_datetime(departures_data['date_(dd/mm/yyyy)'], format='%d-%m-%Y', errors='coerce')\n",
    "\n",
    "# Print preprocessed data\n",
    "print(\"Preprocessed Arrivals Data:\", arrivals_data)\n",
    "print(\"Preprocessed Departures Data:\", departures_data)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessed Arrivals Data:      carrier_code date_(dd/mm/yyyy)  flight_number tail_number origin_airport  \\\n",
      "0              DL        01-08-2024            314      N533DT            BOS   \n",
      "1              DL        01-08-2024            315      N707TW            JFK   \n",
      "2              DL        01-08-2024            340      N813DN            ATL   \n",
      "3              DL        01-08-2024            361      N595NW            DTW   \n",
      "4              DL        01-08-2024            399      N889DN            ATL   \n",
      "...           ...               ...            ...         ...            ...   \n",
      "1210           DL        31-08-2024           2074      N930DZ            MSP   \n",
      "1211           DL        31-08-2024           2078      N928DU            MSP   \n",
      "1212           DL        31-08-2024           2239      N371NW            LAX   \n",
      "1213           DL        31-08-2024           2267      N832DN            LAX   \n",
      "1214           DL        31-08-2024           2985      N133DU            SEA   \n",
      "\n",
      "     scheduled_arrival_time actual_arrival_time  arrival_delay_(minutes)  \\\n",
      "0                  00:47:00            00:17:00                      -30   \n",
      "1                  01:19:00            01:47:00                       28   \n",
      "2                  20:42:00            21:21:00                       39   \n",
      "3                  10:30:00            10:19:00                      -11   \n",
      "4                  00:31:00            01:03:00                       32   \n",
      "...                     ...                 ...                      ...   \n",
      "1210               13:08:00            12:46:00                      -22   \n",
      "1211               11:06:00            10:46:00                      -20   \n",
      "1212               21:38:00            23:11:00                       93   \n",
      "1213               13:29:00            13:16:00                      -13   \n",
      "1214               12:13:00            12:09:00                       -4   \n",
      "\n",
      "      delay_carrier_(minutes)  delay_weather_(minutes)  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           4                        0   \n",
      "3                           0                        0   \n",
      "4                          32                        0   \n",
      "...                       ...                      ...   \n",
      "1210                        0                        0   \n",
      "1211                        0                        0   \n",
      "1212                       93                        0   \n",
      "1213                        0                        0   \n",
      "1214                        0                        0   \n",
      "\n",
      "      delay_national_aviation_system_(minutes)  delay_security_(minutes)  \\\n",
      "0                                            0                         0   \n",
      "1                                           17                         0   \n",
      "2                                            0                         0   \n",
      "3                                            0                         0   \n",
      "4                                            0                         0   \n",
      "...                                        ...                       ...   \n",
      "1210                                         0                         0   \n",
      "1211                                         0                         0   \n",
      "1212                                         0                         0   \n",
      "1213                                         0                         0   \n",
      "1214                                         0                         0   \n",
      "\n",
      "      delay_late_aircraft_arrival_(minutes)       date  \n",
      "0                                         0 2024-08-01  \n",
      "1                                        11 2024-08-01  \n",
      "2                                        35 2024-08-01  \n",
      "3                                         0 2024-08-01  \n",
      "4                                         0 2024-08-01  \n",
      "...                                     ...        ...  \n",
      "1210                                      0 2024-08-31  \n",
      "1211                                      0 2024-08-31  \n",
      "1212                                      0 2024-08-31  \n",
      "1213                                      0 2024-08-31  \n",
      "1214                                      0 2024-08-31  \n",
      "\n",
      "[1215 rows x 14 columns]\n",
      "Preprocessed Departures Data:     carrier_code date_(dd/mm/yyyy)  flight_number tail_number  \\\n",
      "0             DL        01-08-2024            348      N544US   \n",
      "1             DL        01-08-2024            546      N518DQ   \n",
      "2             DL        01-08-2024            548      N538DN   \n",
      "3             DL        01-08-2024            672      N6704Z   \n",
      "4             DL        01-08-2024            791      N580DT   \n",
      "..           ...               ...            ...         ...   \n",
      "840           DL        31-08-2024           2419      N3754A   \n",
      "841           DL        31-08-2024           2444      N399DA   \n",
      "842           DL        31-08-2024           2447      N399DA   \n",
      "843           DL        31-08-2024           2449      N381DN   \n",
      "844           DL        31-08-2024           2553      N367DN   \n",
      "\n",
      "    destination_airport scheduled_departure_time actual_departure_time  \\\n",
      "0                   ATL                 10:04:00              10:02:00   \n",
      "1                   JFK                 06:07:00              06:12:00   \n",
      "2                   JFK                 22:59:00              23:07:00   \n",
      "3                   ATL                 15:03:00              15:05:00   \n",
      "4                   JFK                 12:08:00              12:02:00   \n",
      "..                  ...                      ...                   ...   \n",
      "840                 SEA                 11:15:00              11:10:00   \n",
      "841                 SEA                 06:02:00              06:06:00   \n",
      "842                 SEA                 13:56:00              13:59:00   \n",
      "843                 SEA                 15:45:00              15:40:00   \n",
      "844                 MSP                 06:00:00              05:53:00   \n",
      "\n",
      "     departure_delay_(minutes)  delay_carrier_(minutes)  \\\n",
      "0                           -2                        0   \n",
      "1                            5                        0   \n",
      "2                            8                        0   \n",
      "3                            2                        0   \n",
      "4                           -6                        0   \n",
      "..                         ...                      ...   \n",
      "840                         -5                        0   \n",
      "841                          4                        0   \n",
      "842                          3                        0   \n",
      "843                         -5                        0   \n",
      "844                         -7                        0   \n",
      "\n",
      "     delay_weather_(minutes)  delay_national_aviation_system_(minutes)  \\\n",
      "0                          0                                         0   \n",
      "1                          0                                         0   \n",
      "2                          0                                         0   \n",
      "3                          0                                        20   \n",
      "4                          0                                         0   \n",
      "..                       ...                                       ...   \n",
      "840                        0                                         0   \n",
      "841                        0                                         0   \n",
      "842                        0                                         0   \n",
      "843                        0                                         0   \n",
      "844                        0                                         0   \n",
      "\n",
      "     delay_security_(minutes)  delay_late_aircraft_arrival_(minutes)  \\\n",
      "0                           0                                      0   \n",
      "1                           0                                      0   \n",
      "2                           0                                      0   \n",
      "3                           0                                      0   \n",
      "4                           0                                      0   \n",
      "..                        ...                                    ...   \n",
      "840                         0                                      0   \n",
      "841                         0                                      0   \n",
      "842                         0                                      0   \n",
      "843                         0                                      0   \n",
      "844                         0                                      0   \n",
      "\n",
      "          date  \n",
      "0   2024-08-01  \n",
      "1   2024-08-01  \n",
      "2   2024-08-01  \n",
      "3   2024-08-01  \n",
      "4   2024-08-01  \n",
      "..         ...  \n",
      "840 2024-08-31  \n",
      "841 2024-08-31  \n",
      "842 2024-08-31  \n",
      "843 2024-08-31  \n",
      "844 2024-08-31  \n",
      "\n",
      "[845 rows x 14 columns]\n"
     ]
    }
   ],
   "execution_count": 42
  },
  {
   "cell_type": "code",
   "id": "c6e1c1d9-38ee-4eed-89bd-2841eb9795e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:26.802368Z",
     "start_time": "2024-11-21T20:21:26.774293Z"
    }
   },
   "source": [
    "# Step 3: Preprocess the weather data\n",
    "weather_key_features = [\n",
    "    'STATION',\n",
    "    'DATE',\n",
    "    'DailyAverageDryBulbTemperature',\n",
    "    'DailyPrecipitation',\n",
    "    'HourlyVisibility',\n",
    "    'DailySustainedWindSpeed',\n",
    "    'HourlyWindDirection'\n",
    "]\n",
    "\n",
    "weather_arrivals_data = weather_arrivals_data[weather_key_features]\n",
    "weather_departures_data = weather_departures_data[weather_key_features]\n",
    "\n",
    "# Parse the date column in weather data\n",
    "weather_arrivals_data['DATE'] = pd.to_datetime(weather_arrivals_data['DATE'], errors='coerce')\n",
    "weather_departures_data['DATE'] = pd.to_datetime(weather_departures_data['DATE'], errors='coerce')\n",
    "\n",
    "# Print preprocessed weather data\n",
    "print(\"Preprocessed Weather Arrivals Data:\", weather_arrivals_data)\n",
    "print(\"Preprocessed Weather Departures Data:\", weather_departures_data)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessed Weather Arrivals Data:         STATION                DATE  DailyAverageDryBulbTemperature  \\\n",
      "0   99999923272 2024-08-01 23:59:00                            62.0   \n",
      "1   99999923272 2024-08-02 23:59:00                            61.0   \n",
      "2   99999923272 2024-08-03 23:59:00                            63.0   \n",
      "3   99999923272 2024-08-04 23:59:00                            59.0   \n",
      "4   99999923272 2024-08-05 23:59:00                            58.0   \n",
      "5   99999923272 2024-08-06 23:59:00                            61.0   \n",
      "6   99999923272 2024-08-07 23:59:00                            64.0   \n",
      "7   99999923272 2024-08-08 23:59:00                            58.0   \n",
      "8   99999923272 2024-08-09 23:59:00                            58.0   \n",
      "9   99999923272 2024-08-10 23:59:00                            60.0   \n",
      "10  99999923272 2024-08-11 23:59:00                            58.0   \n",
      "11  99999923272 2024-08-12 23:59:00                            59.0   \n",
      "12  99999923272 2024-08-13 23:59:00                            62.0   \n",
      "13  99999923272 2024-08-14 23:59:00                            60.0   \n",
      "14  99999923272 2024-08-15 23:59:00                            62.0   \n",
      "15  99999923272 2024-08-16 23:59:00                            63.0   \n",
      "16  99999923272 2024-08-17 23:59:00                            65.0   \n",
      "17  99999923272 2024-08-18 23:59:00                            62.0   \n",
      "18  99999923272 2024-08-19 23:59:00                            62.0   \n",
      "19  99999923272 2024-08-20 23:59:00                            65.0   \n",
      "20  99999923272 2024-08-21 23:59:00                            62.0   \n",
      "21  99999923272 2024-08-22 23:59:00                            64.0   \n",
      "22  99999923272 2024-08-23 23:59:00                            64.0   \n",
      "23  99999923272 2024-08-24 09:59:00                             NaN   \n",
      "24  99999923272 2024-08-24 23:59:00                            66.0   \n",
      "\n",
      "    DailyPrecipitation  HourlyVisibility  DailySustainedWindSpeed  \\\n",
      "0                 0.00               NaN                   2237.0   \n",
      "1                 0.00               NaN                   2237.0   \n",
      "2                 0.00               NaN                   2237.0   \n",
      "3                 0.00               NaN                   2237.0   \n",
      "4                 0.00               NaN                   2237.0   \n",
      "5                 0.00               NaN                   2237.0   \n",
      "6                 0.00               NaN                   2237.0   \n",
      "7                 0.00               NaN                   2237.0   \n",
      "8                 0.00               NaN                   2237.0   \n",
      "9                 0.00               NaN                   2237.0   \n",
      "10                0.00               NaN                   2237.0   \n",
      "11                0.00               NaN                   2237.0   \n",
      "12                0.00               NaN                   2237.0   \n",
      "13                0.00               NaN                   2237.0   \n",
      "14                0.00               NaN                   2237.0   \n",
      "15                0.00               NaN                   2237.0   \n",
      "16                0.00               NaN                   2237.0   \n",
      "17                0.00               NaN                   2237.0   \n",
      "18                0.00               NaN                   2237.0   \n",
      "19                0.00               NaN                   2237.0   \n",
      "20                0.00               NaN                   2237.0   \n",
      "21                0.00               NaN                   2237.0   \n",
      "22                0.00               NaN                   2237.0   \n",
      "23                 NaN               NaN                      NaN   \n",
      "24                0.04               NaN                   2237.0   \n",
      "\n",
      "    HourlyWindDirection  \n",
      "0                   NaN  \n",
      "1                   NaN  \n",
      "2                   NaN  \n",
      "3                   NaN  \n",
      "4                   NaN  \n",
      "5                   NaN  \n",
      "6                   NaN  \n",
      "7                   NaN  \n",
      "8                   NaN  \n",
      "9                   NaN  \n",
      "10                  NaN  \n",
      "11                  NaN  \n",
      "12                  NaN  \n",
      "13                  NaN  \n",
      "14                  NaN  \n",
      "15                  NaN  \n",
      "16                  NaN  \n",
      "17                  NaN  \n",
      "18                  NaN  \n",
      "19                  NaN  \n",
      "20                  NaN  \n",
      "21                  NaN  \n",
      "22                  NaN  \n",
      "23                  NaN  \n",
      "24                  NaN  \n",
      "Preprocessed Weather Departures Data:           STATION                DATE  DailyAverageDryBulbTemperature  \\\n",
      "0     72278023183 2024-01-08 00:51:00                             NaN   \n",
      "1     72278023183 2024-01-08 01:51:00                             NaN   \n",
      "2     72278023183 2024-01-08 02:00:00                             NaN   \n",
      "3     72278023183 2024-01-08 02:51:00                             NaN   \n",
      "4     72278023183 2024-01-08 03:51:00                             NaN   \n",
      "...           ...                 ...                             ...   \n",
      "1080  72278023183                 NaT                             NaN   \n",
      "1081  72278023183                 NaT                             NaN   \n",
      "1082  72278023183                 NaT                             NaN   \n",
      "1083  72278023183                 NaT                            98.0   \n",
      "1084  72278023183                 NaT                             NaN   \n",
      "\n",
      "     DailyPrecipitation  HourlyVisibility  DailySustainedWindSpeed  \\\n",
      "0                   NaN             10.00                      NaN   \n",
      "1                   NaN             10.00                      NaN   \n",
      "2                   NaN              9.94                      NaN   \n",
      "3                   NaN             10.00                      NaN   \n",
      "4                   NaN             10.00                      NaN   \n",
      "...                 ...               ...                      ...   \n",
      "1080                NaN             10.00                      NaN   \n",
      "1081                NaN              9.94                      NaN   \n",
      "1082                NaN             10.00                      NaN   \n",
      "1083                  0               NaN                     28.0   \n",
      "1084                NaN               NaN                      NaN   \n",
      "\n",
      "     HourlyWindDirection  \n",
      "0                    170  \n",
      "1                    130  \n",
      "2                    130  \n",
      "3                      0  \n",
      "4                     30  \n",
      "...                  ...  \n",
      "1080                  10  \n",
      "1081                  10  \n",
      "1082                 110  \n",
      "1083                 NaN  \n",
      "1084                 NaN  \n",
      "\n",
      "[1085 rows x 7 columns]\n"
     ]
    }
   ],
   "execution_count": 43
  },
  {
   "cell_type": "code",
   "id": "bc7886a3-0130-4a62-a7b4-86843d63e705",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:26.946771Z",
     "start_time": "2024-11-21T20:21:26.917231Z"
    }
   },
   "source": [
    "# Step 4: Merge flight data with weather data\n",
    "arrivals_data_merged = arrivals_data.merge(weather_arrivals_data, how='left', left_on='date', right_on='DATE')\n",
    "departures_data_merged = departures_data.merge(weather_departures_data, how='left', left_on='date', right_on='DATE')\n",
    "\n",
    "# Combine arrivals and departures data into a unified dataset\n",
    "flight_data = pd.concat([arrivals_data_merged, departures_data_merged], ignore_index=True)\n",
    "\n",
    "# Print merged data\n",
    "print(\"Merged Data:\", flight_data)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merged Data:      carrier_code date_(dd/mm/yyyy)  flight_number tail_number origin_airport  \\\n",
      "0              DL        01-08-2024            314      N533DT            BOS   \n",
      "1              DL        01-08-2024            315      N707TW            JFK   \n",
      "2              DL        01-08-2024            340      N813DN            ATL   \n",
      "3              DL        01-08-2024            361      N595NW            DTW   \n",
      "4              DL        01-08-2024            399      N889DN            ATL   \n",
      "...           ...               ...            ...         ...            ...   \n",
      "2055           DL        31-08-2024           2419      N3754A            NaN   \n",
      "2056           DL        31-08-2024           2444      N399DA            NaN   \n",
      "2057           DL        31-08-2024           2447      N399DA            NaN   \n",
      "2058           DL        31-08-2024           2449      N381DN            NaN   \n",
      "2059           DL        31-08-2024           2553      N367DN            NaN   \n",
      "\n",
      "     scheduled_arrival_time actual_arrival_time  arrival_delay_(minutes)  \\\n",
      "0                  00:47:00            00:17:00                    -30.0   \n",
      "1                  01:19:00            01:47:00                     28.0   \n",
      "2                  20:42:00            21:21:00                     39.0   \n",
      "3                  10:30:00            10:19:00                    -11.0   \n",
      "4                  00:31:00            01:03:00                     32.0   \n",
      "...                     ...                 ...                      ...   \n",
      "2055                    NaN                 NaN                      NaN   \n",
      "2056                    NaN                 NaN                      NaN   \n",
      "2057                    NaN                 NaN                      NaN   \n",
      "2058                    NaN                 NaN                      NaN   \n",
      "2059                    NaN                 NaN                      NaN   \n",
      "\n",
      "      delay_carrier_(minutes)  delay_weather_(minutes)  ...  DATE  \\\n",
      "0                           0                        0  ...   NaT   \n",
      "1                           0                        0  ...   NaT   \n",
      "2                           4                        0  ...   NaT   \n",
      "3                           0                        0  ...   NaT   \n",
      "4                          32                        0  ...   NaT   \n",
      "...                       ...                      ...  ...   ...   \n",
      "2055                        0                        0  ...   NaT   \n",
      "2056                        0                        0  ...   NaT   \n",
      "2057                        0                        0  ...   NaT   \n",
      "2058                        0                        0  ...   NaT   \n",
      "2059                        0                        0  ...   NaT   \n",
      "\n",
      "      DailyAverageDryBulbTemperature  DailyPrecipitation HourlyVisibility  \\\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",
      "2055                             NaN                 NaN              NaN   \n",
      "2056                             NaN                 NaN              NaN   \n",
      "2057                             NaN                 NaN              NaN   \n",
      "2058                             NaN                 NaN              NaN   \n",
      "2059                             NaN                 NaN              NaN   \n",
      "\n",
      "      DailySustainedWindSpeed HourlyWindDirection  destination_airport  \\\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",
      "2055                      NaN                 NaN                  SEA   \n",
      "2056                      NaN                 NaN                  SEA   \n",
      "2057                      NaN                 NaN                  SEA   \n",
      "2058                      NaN                 NaN                  SEA   \n",
      "2059                      NaN                 NaN                  MSP   \n",
      "\n",
      "     scheduled_departure_time  actual_departure_time  \\\n",
      "0                         NaN                    NaN   \n",
      "1                         NaN                    NaN   \n",
      "2                         NaN                    NaN   \n",
      "3                         NaN                    NaN   \n",
      "4                         NaN                    NaN   \n",
      "...                       ...                    ...   \n",
      "2055                 11:15:00               11:10:00   \n",
      "2056                 06:02:00               06:06:00   \n",
      "2057                 13:56:00               13:59:00   \n",
      "2058                 15:45:00               15:40:00   \n",
      "2059                 06:00:00               05:53:00   \n",
      "\n",
      "      departure_delay_(minutes)  \n",
      "0                           NaN  \n",
      "1                           NaN  \n",
      "2                           NaN  \n",
      "3                           NaN  \n",
      "4                           NaN  \n",
      "...                         ...  \n",
      "2055                       -5.0  \n",
      "2056                        4.0  \n",
      "2057                        3.0  \n",
      "2058                       -5.0  \n",
      "2059                       -7.0  \n",
      "\n",
      "[2060 rows x 25 columns]\n"
     ]
    }
   ],
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "id": "3e3135e9-ca6b-49ae-aaaa-8baf379975e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:27.071765Z",
     "start_time": "2024-11-21T20:21:27.055482Z"
    }
   },
   "source": [
    "# Step 5: Handle missing values\n",
    "flight_data.ffill(inplace=True)\n",
    "flight_data = flight_data.infer_objects()\n",
    "\n",
    "# Step 6: Create target variable (delay indicator)\n",
    "flight_data['is_delayed'] = (flight_data['actual_arrival_time'] > flight_data['scheduled_arrival_time']).astype(int)"
   ],
   "outputs": [],
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "id": "3d45eb33-77c1-4321-8306-a96bfd79c385",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:27.197005Z",
     "start_time": "2024-11-21T20:21:27.181287Z"
    }
   },
   "source": [
    "# Step 7: Feature engineering\n",
    "# Add derived weather features\n",
    "flight_data['temp_deviation'] = abs(flight_data['DailyAverageDryBulbTemperature'] - flight_data['DailyPrecipitation'])\n",
    "flight_data['high_wind'] = (flight_data['DailySustainedWindSpeed'] > 15).astype(int)\n",
    "\n",
    "# Convert categorical columns to numeric using Label Encoding\n",
    "categorical_columns = ['carrier_code', 'origin_airport', 'destination_airport']\n",
    "for col in categorical_columns:\n",
    "    le = LabelEncoder()\n",
    "    flight_data[col] = le.fit_transform(flight_data[col])\n",
    "\n",
    "# Convert date-related features to useful numeric features\n",
    "flight_data['day_of_week'] = flight_data['date'].dt.dayofweek\n",
    "flight_data['month'] = flight_data['date'].dt.month\n"
   ],
   "outputs": [],
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "id": "3ae9af9b-92bc-4952-96c2-c9581c723350",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:27.318963Z",
     "start_time": "2024-11-21T20:21:27.297617Z"
    }
   },
   "source": [
    "# Step 8: Remove non-numeric columns\n",
    "X = flight_data.drop([\n",
    "    'is_delayed',\n",
    "    'actual_arrival_time',\n",
    "    'scheduled_arrival_time',\n",
    "    'date',\n",
    "    'DATE',\n",
    "    'date_(dd/mm/yyyy)',\n",
    "    'tail_number',\n",
    "    'scheduled_departure_time',\n",
    "    'actual_departure_time'\n",
    "], axis=1)\n",
    "\n",
    "# Print X \n",
    "print(\"X:\", X)\n",
    "\n",
    "# Explicitly check if there are any non-numeric columns left in X\n",
    "non_numeric_columns = X.select_dtypes(exclude=[np.number]).columns\n",
    "if len(non_numeric_columns) > 0:\n",
    "    print(\"Non-numeric columns found:\", non_numeric_columns)\n",
    "    # Drop the non-numeric columns to avoid issues during model fitting\n",
    "    X = X.drop(non_numeric_columns, axis=1)\n",
    "\n",
    "# Target variable\n",
    "y = flight_data['is_delayed']\n",
    "\n",
    "# Print y \n",
    "print(\"y:\", y)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:       carrier_code  flight_number  origin_airport  arrival_delay_(minutes)  \\\n",
      "0                0            314               1                    -30.0   \n",
      "1                0            315               3                     28.0   \n",
      "2                0            340               0                     39.0   \n",
      "3                0            361               2                    -11.0   \n",
      "4                0            399               0                     32.0   \n",
      "...            ...            ...             ...                      ...   \n",
      "2055             0           2419               6                     -4.0   \n",
      "2056             0           2444               6                     -4.0   \n",
      "2057             0           2447               6                     -4.0   \n",
      "2058             0           2449               6                     -4.0   \n",
      "2059             0           2553               6                     -4.0   \n",
      "\n",
      "      delay_carrier_(minutes)  delay_weather_(minutes)  \\\n",
      "0                           0                        0   \n",
      "1                           0                        0   \n",
      "2                           4                        0   \n",
      "3                           0                        0   \n",
      "4                          32                        0   \n",
      "...                       ...                      ...   \n",
      "2055                        0                        0   \n",
      "2056                        0                        0   \n",
      "2057                        0                        0   \n",
      "2058                        0                        0   \n",
      "2059                        0                        0   \n",
      "\n",
      "      delay_national_aviation_system_(minutes)  delay_security_(minutes)  \\\n",
      "0                                            0                         0   \n",
      "1                                           17                         0   \n",
      "2                                            0                         0   \n",
      "3                                            0                         0   \n",
      "4                                            0                         0   \n",
      "...                                        ...                       ...   \n",
      "2055                                         0                         0   \n",
      "2056                                         0                         0   \n",
      "2057                                         0                         0   \n",
      "2058                                         0                         0   \n",
      "2059                                         0                         0   \n",
      "\n",
      "      delay_late_aircraft_arrival_(minutes)  STATION  ...  DailyPrecipitation  \\\n",
      "0                                         0      NaN  ...                 NaN   \n",
      "1                                        11      NaN  ...                 NaN   \n",
      "2                                        35      NaN  ...                 NaN   \n",
      "3                                         0      NaN  ...                 NaN   \n",
      "4                                         0      NaN  ...                 NaN   \n",
      "...                                     ...      ...  ...                 ...   \n",
      "2055                                      0      NaN  ...                 NaN   \n",
      "2056                                      0      NaN  ...                 NaN   \n",
      "2057                                      0      NaN  ...                 NaN   \n",
      "2058                                      0      NaN  ...                 NaN   \n",
      "2059                                      0      NaN  ...                 NaN   \n",
      "\n",
      "      HourlyVisibility  DailySustainedWindSpeed  HourlyWindDirection  \\\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",
      "2055               NaN                      NaN                  NaN   \n",
      "2056               NaN                      NaN                  NaN   \n",
      "2057               NaN                      NaN                  NaN   \n",
      "2058               NaN                      NaN                  NaN   \n",
      "2059               NaN                      NaN                  NaN   \n",
      "\n",
      "      destination_airport  departure_delay_(minutes)  temp_deviation  \\\n",
      "0                       7                        NaN             NaN   \n",
      "1                       7                        NaN             NaN   \n",
      "2                       7                        NaN             NaN   \n",
      "3                       7                        NaN             NaN   \n",
      "4                       7                        NaN             NaN   \n",
      "...                   ...                        ...             ...   \n",
      "2055                    5                       -5.0             NaN   \n",
      "2056                    5                        4.0             NaN   \n",
      "2057                    5                        3.0             NaN   \n",
      "2058                    5                       -5.0             NaN   \n",
      "2059                    4                       -7.0             NaN   \n",
      "\n",
      "      high_wind  day_of_week  month  \n",
      "0             0            3      8  \n",
      "1             0            3      8  \n",
      "2             0            3      8  \n",
      "3             0            3      8  \n",
      "4             0            3      8  \n",
      "...         ...          ...    ...  \n",
      "2055          0            5      8  \n",
      "2056          0            5      8  \n",
      "2057          0            5      8  \n",
      "2058          0            5      8  \n",
      "2059          0            5      8  \n",
      "\n",
      "[2060 rows x 21 columns]\n",
      "y: 0       0\n",
      "1       1\n",
      "2       1\n",
      "3       0\n",
      "4       1\n",
      "       ..\n",
      "2055    0\n",
      "2056    0\n",
      "2057    0\n",
      "2058    0\n",
      "2059    0\n",
      "Name: is_delayed, Length: 2060, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "id": "ad8052e7-9650-4d74-9df7-7d43ad38c9e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:27.449248Z",
     "start_time": "2024-11-21T20:21:27.418943Z"
    }
   },
   "source": [
    "# Step 9: 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",
    "# Print train and test shapes\n",
    "print(\"X_train :\", X_train)\n",
    "print(\"X_test :\", X_test)\n",
    "print(\"y_train :\", y_train)\n",
    "print(\"y_test :\", y_test)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train :       carrier_code  flight_number  origin_airport  arrival_delay_(minutes)  \\\n",
      "266              0            858               5                      3.0   \n",
      "1133             0           1559               4                    -20.0   \n",
      "1480             0            796               6                     -4.0   \n",
      "1839             0           1070               6                     -4.0   \n",
      "67               0            916               0                    -14.0   \n",
      "...            ...            ...             ...                      ...   \n",
      "1638             0           2447               6                     -4.0   \n",
      "1095             0           1559               4                     20.0   \n",
      "1130             0           1088               7                    -19.0   \n",
      "1294             0           2419               6                     -4.0   \n",
      "860              0            670               3                    -22.0   \n",
      "\n",
      "      delay_carrier_(minutes)  delay_weather_(minutes)  \\\n",
      "266                         0                        0   \n",
      "1133                        0                        0   \n",
      "1480                        0                        0   \n",
      "1839                        0                        0   \n",
      "67                          0                        0   \n",
      "...                       ...                      ...   \n",
      "1638                        0                        0   \n",
      "1095                       12                        0   \n",
      "1130                        0                        0   \n",
      "1294                        0                        0   \n",
      "860                         0                        0   \n",
      "\n",
      "      delay_national_aviation_system_(minutes)  delay_security_(minutes)  \\\n",
      "266                                          0                         0   \n",
      "1133                                         0                         0   \n",
      "1480                                         0                         0   \n",
      "1839                                         0                         0   \n",
      "67                                           0                         0   \n",
      "...                                        ...                       ...   \n",
      "1638                                         0                         0   \n",
      "1095                                         8                         0   \n",
      "1130                                         0                         0   \n",
      "1294                                         0                         0   \n",
      "860                                          0                         0   \n",
      "\n",
      "      delay_late_aircraft_arrival_(minutes)  STATION  ...  DailyPrecipitation  \\\n",
      "266                                       0      NaN  ...                 NaN   \n",
      "1133                                      0      NaN  ...                 NaN   \n",
      "1480                                      0      NaN  ...                 NaN   \n",
      "1839                                      0      NaN  ...                 NaN   \n",
      "67                                        0      NaN  ...                 NaN   \n",
      "...                                     ...      ...  ...                 ...   \n",
      "1638                                      0      NaN  ...                 NaN   \n",
      "1095                                      0      NaN  ...                 NaN   \n",
      "1130                                      0      NaN  ...                 NaN   \n",
      "1294                                      0      NaN  ...                 NaN   \n",
      "860                                       0      NaN  ...                 NaN   \n",
      "\n",
      "      HourlyVisibility  DailySustainedWindSpeed  HourlyWindDirection  \\\n",
      "266                NaN                      NaN                  NaN   \n",
      "1133               NaN                      NaN                  NaN   \n",
      "1480               NaN                      NaN                  NaN   \n",
      "1839               NaN                      NaN                  NaN   \n",
      "67                 NaN                      NaN                  NaN   \n",
      "...                ...                      ...                  ...   \n",
      "1638               NaN                      NaN                  NaN   \n",
      "1095               NaN                      NaN                  NaN   \n",
      "1130               NaN                      NaN                  NaN   \n",
      "1294               NaN                      NaN                  NaN   \n",
      "860                NaN                      NaN                  NaN   \n",
      "\n",
      "      destination_airport  departure_delay_(minutes)  temp_deviation  \\\n",
      "266                     7                        NaN             NaN   \n",
      "1133                    7                        NaN             NaN   \n",
      "1480                    0                        3.0             NaN   \n",
      "1839                    2                       24.0             NaN   \n",
      "67                      7                        NaN             NaN   \n",
      "...                   ...                        ...             ...   \n",
      "1638                    5                        1.0             NaN   \n",
      "1095                    7                        NaN             NaN   \n",
      "1130                    7                        NaN             NaN   \n",
      "1294                    5                       -9.0             NaN   \n",
      "860                     7                        NaN             NaN   \n",
      "\n",
      "      high_wind  day_of_week  month  \n",
      "266           0            2      8  \n",
      "1133          0            3      8  \n",
      "1480          0            5      8  \n",
      "1839          0            4      8  \n",
      "67            0            4      8  \n",
      "...         ...          ...    ...  \n",
      "1638          0            3      8  \n",
      "1095          0            2      8  \n",
      "1130          0            3      8  \n",
      "1294          0            5      8  \n",
      "860           0            3      8  \n",
      "\n",
      "[1648 rows x 21 columns]\n",
      "X_test :       carrier_code  flight_number  origin_airport  arrival_delay_(minutes)  \\\n",
      "1299             0           2563               6                     -4.0   \n",
      "591              0            858               5                     -8.0   \n",
      "1319             0           2274               6                     -4.0   \n",
      "1067             0           2272               4                    -23.0   \n",
      "29               0           1091               7                    112.0   \n",
      "...            ...            ...             ...                      ...   \n",
      "1033             0           2985               6                     -7.0   \n",
      "674              0           1091               7                     16.0   \n",
      "1952             0           2288               6                     -4.0   \n",
      "322              0           2074               5                     48.0   \n",
      "1786             0           1432               6                     -4.0   \n",
      "\n",
      "      delay_carrier_(minutes)  delay_weather_(minutes)  \\\n",
      "1299                        0                        0   \n",
      "591                         0                        0   \n",
      "1319                        0                        0   \n",
      "1067                        0                        0   \n",
      "29                         11                        0   \n",
      "...                       ...                      ...   \n",
      "1033                        0                        0   \n",
      "674                         0                        0   \n",
      "1952                        0                        0   \n",
      "322                         0                        0   \n",
      "1786                        0                        0   \n",
      "\n",
      "      delay_national_aviation_system_(minutes)  delay_security_(minutes)  \\\n",
      "1299                                         0                         0   \n",
      "591                                          0                         0   \n",
      "1319                                         0                         0   \n",
      "1067                                         0                         0   \n",
      "29                                           0                         0   \n",
      "...                                        ...                       ...   \n",
      "1033                                         0                         0   \n",
      "674                                         16                         0   \n",
      "1952                                         0                         0   \n",
      "322                                         48                         0   \n",
      "1786                                         0                         0   \n",
      "\n",
      "      delay_late_aircraft_arrival_(minutes)  STATION  ...  DailyPrecipitation  \\\n",
      "1299                                     25      NaN  ...                 NaN   \n",
      "591                                       0      NaN  ...                 NaN   \n",
      "1319                                      0      NaN  ...                 NaN   \n",
      "1067                                      0      NaN  ...                 NaN   \n",
      "29                                      101      NaN  ...                 NaN   \n",
      "...                                     ...      ...  ...                 ...   \n",
      "1033                                      0      NaN  ...                 NaN   \n",
      "674                                       0      NaN  ...                 NaN   \n",
      "1952                                      0      NaN  ...                 NaN   \n",
      "322                                       0      NaN  ...                 NaN   \n",
      "1786                                     74      NaN  ...                 NaN   \n",
      "\n",
      "      HourlyVisibility  DailySustainedWindSpeed  HourlyWindDirection  \\\n",
      "1299               NaN                      NaN                  NaN   \n",
      "591                NaN                      NaN                  NaN   \n",
      "1319               NaN                      NaN                  NaN   \n",
      "1067               NaN                      NaN                  NaN   \n",
      "29                 NaN                      NaN                  NaN   \n",
      "...                ...                      ...                  ...   \n",
      "1033               NaN                      NaN                  NaN   \n",
      "674                NaN                      NaN                  NaN   \n",
      "1952               NaN                      NaN                  NaN   \n",
      "322                NaN                      NaN                  NaN   \n",
      "1786               NaN                      NaN                  NaN   \n",
      "\n",
      "      destination_airport  departure_delay_(minutes)  temp_deviation  \\\n",
      "1299                    4                       44.0             NaN   \n",
      "591                     7                        NaN             NaN   \n",
      "1319                    4                       -4.0             NaN   \n",
      "1067                    7                        NaN             NaN   \n",
      "29                      7                        NaN             NaN   \n",
      "...                   ...                        ...             ...   \n",
      "1033                    7                        NaN             NaN   \n",
      "674                     7                        NaN             NaN   \n",
      "1952                    4                       -5.0             NaN   \n",
      "322                     7                        NaN             NaN   \n",
      "1786                    6                       86.0             NaN   \n",
      "\n",
      "      high_wind  day_of_week  month  \n",
      "1299          0            5      8  \n",
      "591           0            3      8  \n",
      "1319          0            6      8  \n",
      "1067          0            1      8  \n",
      "29            0            3      8  \n",
      "...         ...          ...    ...  \n",
      "1033          0            0      8  \n",
      "674           0            5      8  \n",
      "1952          0            1      8  \n",
      "322           0            3      8  \n",
      "1786          0            2      8  \n",
      "\n",
      "[412 rows x 21 columns]\n",
      "y_train : 266     1\n",
      "1133    0\n",
      "1480    0\n",
      "1839    0\n",
      "67      0\n",
      "       ..\n",
      "1638    0\n",
      "1095    1\n",
      "1130    0\n",
      "1294    0\n",
      "860     0\n",
      "Name: is_delayed, Length: 1648, dtype: int64\n",
      "y_test : 1299    0\n",
      "591     0\n",
      "1319    0\n",
      "1067    0\n",
      "29      1\n",
      "       ..\n",
      "1033    0\n",
      "674     1\n",
      "1952    0\n",
      "322     1\n",
      "1786    0\n",
      "Name: is_delayed, Length: 412, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "id": "7bbdedd8-dc7b-4b6d-9fe8-81c5d9f4cab5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:27.855844Z",
     "start_time": "2024-11-21T20:21:27.553683Z"
    }
   },
   "source": [
    "# Step 10: Build models\n",
    "# Random Forest model\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Print RF model feature importances\n",
    "print(\"RF Model Feature Importances:\")\n",
    "print(rf_model.feature_importances_)\n",
    "\n",
    "# XGBoost model\n",
    "xgb_model = xgb.XGBClassifier(eval_metric='logloss')\n",
    "xgb_model.fit(X_train, y_train)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF Model Feature Importances:\n",
      "[0.         0.05837284 0.04161226 0.60712375 0.05476268 0.00186042\n",
      " 0.05617233 0.         0.02175758 0.         0.         0.\n",
      " 0.         0.         0.         0.07438871 0.06881904 0.\n",
      " 0.         0.0151304  0.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='logloss',\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)"
      ],
      "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>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "id": "3a7bc02a-9539-4508-8f9e-7d33a935e0fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:28.418727Z",
     "start_time": "2024-11-21T20:21:27.983176Z"
    }
   },
   "source": [
    "# Step 11: Evaluate models\n",
    "# Random Forest predictions and probabilistic predictions\n",
    "rf_predictions = rf_model.predict(X_test)\n",
    "rf_probs = rf_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Classification report for Random Forest\n",
    "print(\"Random Forest Classification Report:\")\n",
    "print(classification_report(y_test, rf_predictions))\n",
    "\n",
    "# ROC Curve for Random Forest\n",
    "fpr, tpr, _ = roc_curve(y_test, rf_probs)\n",
    "plt.plot(fpr, tpr, label='Random Forest (AUC = {:.2f})'.format(roc_auc_score(y_test, rf_probs)))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend()\n",
    "plt.title('ROC Curve')\n",
    "plt.show()\n",
    "\n",
    "# Calibration Curve for Random Forest\n",
    "prob_true, prob_pred = calibration_curve(y_test, rf_probs, n_bins=10)\n",
    "plt.plot(prob_pred, prob_true, label='Random Forest')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', label='Perfectly Calibrated')\n",
    "plt.xlabel('Mean Predicted Probability')\n",
    "plt.ylabel('Fraction of Positives')\n",
    "plt.legend()\n",
    "plt.title('Calibration Curve')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98       315\n",
      "           1       0.92      0.97      0.94        97\n",
      "\n",
      "    accuracy                           0.97       412\n",
      "   macro avg       0.96      0.97      0.96       412\n",
      "weighted avg       0.97      0.97      0.97       412\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 50
  },
  {
   "cell_type": "code",
   "id": "8433d144-9827-4b98-915f-6d115258b28a",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:29.508144Z",
     "start_time": "2024-11-21T20:21:28.592013Z"
    }
   },
   "source": [
    "# Step 12: Incorporate uncertainty\n",
    "# Monte Carlo simulations\n",
    "simulated_uncertainty = []\n",
    "for _ in range(100):\n",
    "    # Simulate 100 variations\n",
    "    perturbed_X = X_test.copy()\n",
    "    perturbed_X['DailyPrecipitation'] += np.random.normal(0, 0.1, size=X_test.shape[0])\n",
    "    simulated_uncertainty.append(rf_model.predict_proba(perturbed_X)[:, 1])\n",
    "\n",
    "# Aggregate Monte Carlo results\n",
    "simulated_uncertainty = np.array(simulated_uncertainty)\n",
    "uncertainty_mean = simulated_uncertainty.mean(axis=0)\n",
    "uncertainty_std = simulated_uncertainty.std(axis=0)\n",
    "\n",
    "# Print uncertainty metrics\n",
    "print(\"Uncertainty Mean:\", uncertainty_mean)\n",
    "print(\"Uncertainty Standard Deviation:\", uncertainty_std)\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncertainty Mean: [0.   0.   0.   0.   0.96 0.67 0.94 0.07 0.   0.57 0.   0.64 0.02 0.\n",
      " 0.01 1.   1.   1.   0.01 0.   0.83 0.   0.94 0.   0.   0.99 0.09 0.97\n",
      " 0.   0.01 0.   0.   0.   0.   0.24 0.   0.   0.   0.   0.01 0.   0.\n",
      " 0.   0.42 0.   0.   0.99 0.   0.   0.   0.96 0.94 0.   0.95 0.91 1.\n",
      " 0.   0.   0.   0.   0.93 0.   0.63 0.   0.   0.   0.   0.   0.01 0.\n",
      " 0.9  0.97 0.   0.   0.98 0.   0.   0.   0.   0.   0.   0.01 0.   0.\n",
      " 0.   0.04 1.   0.01 0.98 0.   0.   0.98 0.   0.02 0.   0.   0.   1.\n",
      " 0.   0.   0.   0.52 0.97 0.91 0.   0.99 0.   0.   0.   0.   0.91 0.\n",
      " 0.   0.   0.99 0.99 0.69 1.   0.   0.   0.   0.01 0.99 0.   0.   0.18\n",
      " 0.   0.   0.98 0.   0.01 0.9  0.   0.   0.   0.   0.   0.   0.   1.\n",
      " 0.03 0.   0.   0.   0.   0.97 0.05 0.99 0.   0.   0.01 1.   0.   0.\n",
      " 0.   0.96 0.01 0.   0.   0.02 0.   0.01 0.   0.   0.91 0.02 0.   0.\n",
      " 0.   0.   0.98 0.   0.   0.   0.   0.01 0.   0.   0.   0.   0.03 0.\n",
      " 0.   0.   0.26 1.   0.87 0.   0.   0.01 0.   0.01 0.01 0.   0.95 0.\n",
      " 0.   0.   0.   0.96 0.   0.   0.   0.   0.96 0.94 0.03 0.99 0.   0.\n",
      " 0.   0.42 0.   0.   1.   0.   1.   0.   0.   0.   0.   0.02 0.   0.99\n",
      " 0.   0.   0.   0.82 0.   0.   0.   0.   0.   0.   0.   0.98 0.98 0.\n",
      " 0.99 0.   0.   0.   1.   0.92 0.   0.96 0.98 0.   0.01 0.   0.99 0.\n",
      " 0.97 0.   0.03 0.   0.   1.   0.   0.88 0.04 0.   0.   0.   0.   0.\n",
      " 0.93 0.   0.01 0.   1.   0.01 0.   0.   0.   0.   0.99 0.   0.   0.99\n",
      " 0.97 0.   0.   0.99 0.   0.   0.   0.   0.   0.   1.   0.   1.   0.01\n",
      " 0.01 0.   0.   0.   0.   0.01 0.   0.94 0.   0.   0.92 0.   0.   0.\n",
      " 0.   0.   0.   0.   0.   0.01 0.   0.01 0.   0.   0.97 0.   0.   0.\n",
      " 0.95 0.75 0.   0.02 0.02 0.   0.   1.   0.96 0.   0.   0.   0.69 1.\n",
      " 0.   0.   0.   0.01 0.   0.   0.   0.03 0.   0.   0.02 0.   0.   0.98\n",
      " 0.15 0.   0.   0.   0.   0.99 0.   0.32 1.   0.87 0.   0.02 0.99 0.\n",
      " 0.83 0.   0.01 0.   0.   0.99 0.99 0.   0.99 0.97 0.98 0.99 0.   0.\n",
      " 0.05 0.   0.   0.   0.96 0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      " 0.   0.   0.02 0.   0.03 0.   0.88 0.   0.01 0.   0.26 0.93 0.   0.\n",
      " 0.   0.14 1.   0.   0.99 0.  ]\n",
      "Uncertainty Standard Deviation: [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 1.55431223e-15 1.22124533e-15 1.22124533e-15 8.32667268e-17\n",
      " 0.00000000e+00 2.22044605e-16 0.00000000e+00 4.44089210e-16\n",
      " 1.38777878e-17 0.00000000e+00 6.93889390e-18 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.93889390e-18 0.00000000e+00\n",
      " 1.22124533e-15 0.00000000e+00 1.22124533e-15 0.00000000e+00\n",
      " 0.00000000e+00 1.44328993e-15 8.32667268e-17 7.77156117e-16\n",
      " 0.00000000e+00 6.93889390e-18 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 3.88578059e-16 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.93889390e-18\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 9.99200722e-16\n",
      " 0.00000000e+00 0.00000000e+00 1.44328993e-15 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 1.55431223e-15 1.22124533e-15\n",
      " 0.00000000e+00 1.77635684e-15 2.10942375e-15 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 1.88737914e-15 0.00000000e+00 1.11022302e-15 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 6.93889390e-18 0.00000000e+00 7.77156117e-16 7.77156117e-16\n",
      " 0.00000000e+00 0.00000000e+00 4.44089210e-16 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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      " 0.00000000e+00 1.44328993e-15 1.44328993e-15 0.00000000e+00\n",
      " 1.44328993e-15 7.77156117e-16 4.44089210e-16 1.44328993e-15\n",
      " 0.00000000e+00 0.00000000e+00 9.71445147e-17 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 1.55431223e-15 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 1.38777878e-17 0.00000000e+00\n",
      " 4.85722573e-17 0.00000000e+00 4.44089210e-16 0.00000000e+00\n",
      " 6.93889390e-18 0.00000000e+00 4.99600361e-16 1.88737914e-15\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.66533454e-16\n",
      " 0.00000000e+00 0.00000000e+00 1.44328993e-15 0.00000000e+00]\n"
     ]
    }
   ],
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "id": "0f14e58c-d316-4106-ad2e-e0151e32f961",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-21T20:21:29.667967Z",
     "start_time": "2024-11-21T20:21:29.612957Z"
    }
   },
   "source": [
    "# Step 13: Save models and results\n",
    "joblib.dump(rf_model, 'random_forest_model.pkl')\n",
    "joblib.dump(xgb_model, 'xgboost_model.pkl')\n",
    "\n",
    "# Save Monte Carlo uncertainty results\n",
    "uncertainty_results = pd.DataFrame({'mean_prob': uncertainty_mean, 'std_dev': uncertainty_std})\n",
    "uncertainty_results.to_csv('uncertainty_results.csv', index=False)"
   ],
   "outputs": [],
   "execution_count": 52
  }
 ],
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