Arrive_delay_train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3bd0f61c-48cd-427c-aedd-69150cac4cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, learning_curve\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay, accuracy_score\n",
    "from category_encoders import TargetEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6c910c6-86f6-4b78-ad4c-a65cab04fe46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>4765277</th>\n",
       "      <th>4760013</th>\n",
       "      <th>4766621</th>\n",
       "      <th>4761588</th>\n",
       "      <th>4768673</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YEAR</th>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAY_OF_MONTH</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL_DATE</th>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>2024-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_UNIQUE_CARRIER</th>\n",
       "      <td>UA</td>\n",
       "      <td>F9</td>\n",
       "      <td>WN</td>\n",
       "      <td>UA</td>\n",
       "      <td>WN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_CARRIER_FL_NUM</th>\n",
       "      <td>728</td>\n",
       "      <td>2451</td>\n",
       "      <td>225</td>\n",
       "      <td>1055</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_CITY_NAME</th>\n",
       "      <td>Columbus, OH</td>\n",
       "      <td>Charlotte, NC</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Newark, NJ</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEST_CITY_NAME</th>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>Tampa, FL</td>\n",
       "      <td>Boston, MA</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DEP_TIME</th>\n",
       "      <td>900</td>\n",
       "      <td>2145</td>\n",
       "      <td>750</td>\n",
       "      <td>1400</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_TIME</th>\n",
       "      <td>854.0</td>\n",
       "      <td>2129.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_DELAY</th>\n",
       "      <td>-6.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ARR_TIME</th>\n",
       "      <td>1015</td>\n",
       "      <td>2332</td>\n",
       "      <td>1120</td>\n",
       "      <td>1516</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_TIME</th>\n",
       "      <td>955.0</td>\n",
       "      <td>2314.0</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>1452.0</td>\n",
       "      <td>817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARR_DELAY</th>\n",
       "      <td>-20.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CANCELLED</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_ELAPSED_TIME</th>\n",
       "      <td>195.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL_ELAPSED_TIME</th>\n",
       "      <td>181.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIR_TIME</th>\n",
       "      <td>153.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE</th>\n",
       "      <td>1154.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDelay</th>\n",
       "      <td>-14.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_code</th>\n",
       "      <td>UA728</td>\n",
       "      <td>F92451</td>\n",
       "      <td>WN225</td>\n",
       "      <td>UA1055</td>\n",
       "      <td>WN510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepHour</th>\n",
       "      <td>8.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepMinute</th>\n",
       "      <td>54.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepFraction</th>\n",
       "      <td>8.9</td>\n",
       "      <td>21.483333</td>\n",
       "      <td>7.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>7.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime_sin</th>\n",
       "      <td>0.725374</td>\n",
       "      <td>-0.612217</td>\n",
       "      <td>0.891007</td>\n",
       "      <td>-0.477159</td>\n",
       "      <td>0.96363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime_cos</th>\n",
       "      <td>-0.688355</td>\n",
       "      <td>0.79069</td>\n",
       "      <td>-0.45399</td>\n",
       "      <td>-0.878817</td>\n",
       "      <td>-0.267238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepHour</th>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepMinute</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepFraction</th>\n",
       "      <td>9.0</td>\n",
       "      <td>21.75</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepTime_sin</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.55557</td>\n",
       "      <td>0.887011</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.965926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRS_DepTime_cos</th>\n",
       "      <td>-0.707107</td>\n",
       "      <td>0.83147</td>\n",
       "      <td>-0.461749</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.258819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DISTANCE_new</th>\n",
       "      <td>1144_1598</td>\n",
       "      <td>417_541</td>\n",
       "      <td>956_1144</td>\n",
       "      <td>31_224</td>\n",
       "      <td>325_417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEP_DELAY_new</th>\n",
       "      <td>-7_-5</td>\n",
       "      <td>-96_-9</td>\n",
       "      <td>-4_-2</td>\n",
       "      <td>-7_-5</td>\n",
       "      <td>-1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlightDelay_new</th>\n",
       "      <td>-15_-13</td>\n",
       "      <td>-3_0</td>\n",
       "      <td>-3_0</td>\n",
       "      <td>-20_-15</td>\n",
       "      <td>-20_-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MKT_UNIQUE_CARRIER_new</th>\n",
       "      <td>UA</td>\n",
       "      <td>F9</td>\n",
       "      <td>WN</td>\n",
       "      <td>UA</td>\n",
       "      <td>WN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORIGIN_CITY_NAME_new</th>\n",
       "      <td>Columbus, OH</td>\n",
       "      <td>Charlotte, NC</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>Newark, NJ</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEST_CITY_NAME_new</th>\n",
       "      <td>Denver, CO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>Tampa, FL</td>\n",
       "      <td>Boston, MA</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             4765277        4760013      4766621     4761588  \\\n",
       "YEAR                            2024           2024         2024        2024   \n",
       "MONTH                              9              9            9           9   \n",
       "DAY_OF_MONTH                      30             30           30          30   \n",
       "FL_DATE                   2024-09-30     2024-09-30   2024-09-30  2024-09-30   \n",
       "MKT_UNIQUE_CARRIER                UA             F9           WN          UA   \n",
       "MKT_CARRIER_FL_NUM               728           2451          225        1055   \n",
       "ORIGIN_CITY_NAME        Columbus, OH  Charlotte, NC  Chicago, IL  Newark, NJ   \n",
       "DEST_CITY_NAME            Denver, CO    Orlando, FL    Tampa, FL  Boston, MA   \n",
       "CRS_DEP_TIME                     900           2145          750        1400   \n",
       "DEP_TIME                       854.0         2129.0        748.0      1354.0   \n",
       "DEP_DELAY                       -6.0          -16.0         -2.0        -6.0   \n",
       "CRS_ARR_TIME                    1015           2332         1120        1516   \n",
       "ARR_TIME                       955.0         2314.0       1118.0      1452.0   \n",
       "ARR_DELAY                      -20.0          -18.0         -2.0       -24.0   \n",
       "CANCELLED                        0.0            0.0          0.0         0.0   \n",
       "CRS_ELAPSED_TIME               195.0          107.0        150.0        76.0   \n",
       "ACTUAL_ELAPSED_TIME            181.0          105.0        150.0        58.0   \n",
       "AIR_TIME                       153.0           65.0        136.0        38.0   \n",
       "DISTANCE                      1154.0          468.0        997.0       200.0   \n",
       "FlightDelay                    -14.0           -2.0          0.0       -18.0   \n",
       "flight_code                    UA728         F92451        WN225      UA1055   \n",
       "Day                           Monday         Monday       Monday      Monday   \n",
       "DepHour                          8.0           21.0          7.0        13.0   \n",
       "DepMinute                       54.0           29.0         48.0        54.0   \n",
       "DepFraction                      8.9      21.483333          7.8        13.9   \n",
       "DepTime_sin                 0.725374      -0.612217     0.891007   -0.477159   \n",
       "DepTime_cos                -0.688355        0.79069     -0.45399   -0.878817   \n",
       "CRS_DepHour                        9             21            7          14   \n",
       "CRS_DepMinute                      0             45           50           0   \n",
       "CRS_DepFraction                  9.0          21.75     7.833333        14.0   \n",
       "CRS_DepTime_sin             0.707107       -0.55557     0.887011        -0.5   \n",
       "CRS_DepTime_cos            -0.707107        0.83147    -0.461749   -0.866025   \n",
       "DISTANCE_new               1144_1598        417_541     956_1144      31_224   \n",
       "DEP_DELAY_new                  -7_-5         -96_-9        -4_-2       -7_-5   \n",
       "FlightDelay_new              -15_-13           -3_0         -3_0     -20_-15   \n",
       "MKT_UNIQUE_CARRIER_new            UA             F9           WN          UA   \n",
       "ORIGIN_CITY_NAME_new    Columbus, OH  Charlotte, NC  Chicago, IL  Newark, NJ   \n",
       "DEST_CITY_NAME_new        Denver, CO    Orlando, FL    Tampa, FL  Boston, MA   \n",
       "\n",
       "                                  4768673  \n",
       "YEAR                                 2024  \n",
       "MONTH                                   9  \n",
       "DAY_OF_MONTH                           30  \n",
       "FL_DATE                        2024-09-30  \n",
       "MKT_UNIQUE_CARRIER                     WN  \n",
       "MKT_CARRIER_FL_NUM                    510  \n",
       "ORIGIN_CITY_NAME        San Francisco, CA  \n",
       "DEST_CITY_NAME            Los Angeles, CA  \n",
       "CRS_DEP_TIME                          700  \n",
       "DEP_TIME                            702.0  \n",
       "DEP_DELAY                             2.0  \n",
       "CRS_ARR_TIME                          830  \n",
       "ARR_TIME                            817.0  \n",
       "ARR_DELAY                           -13.0  \n",
       "CANCELLED                             0.0  \n",
       "CRS_ELAPSED_TIME                     90.0  \n",
       "ACTUAL_ELAPSED_TIME                  75.0  \n",
       "AIR_TIME                             56.0  \n",
       "DISTANCE                            337.0  \n",
       "FlightDelay                         -15.0  \n",
       "flight_code                         WN510  \n",
       "Day                                Monday  \n",
       "DepHour                               7.0  \n",
       "DepMinute                             2.0  \n",
       "DepFraction                      7.033333  \n",
       "DepTime_sin                       0.96363  \n",
       "DepTime_cos                     -0.267238  \n",
       "CRS_DepHour                             7  \n",
       "CRS_DepMinute                           0  \n",
       "CRS_DepFraction                       7.0  \n",
       "CRS_DepTime_sin                  0.965926  \n",
       "CRS_DepTime_cos                 -0.258819  \n",
       "DISTANCE_new                      325_417  \n",
       "DEP_DELAY_new                        -1_3  \n",
       "FlightDelay_new                   -20_-15  \n",
       "MKT_UNIQUE_CARRIER_new                 WN  \n",
       "ORIGIN_CITY_NAME_new    San Francisco, CA  \n",
       "DEST_CITY_NAME_new        Los Angeles, CA  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset\n",
    "df = pd.read_csv('2024_clean.csv')\n",
    "\n",
    "df.sample(5).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7b658dd-7d1d-4be9-a94e-724de1d81457",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define main label and features\n",
    "main_label = 'ARR_DELAY'\n",
    "selected_features = ['MKT_UNIQUE_CARRIER', 'ORIGIN_CITY_NAME', 'DEST_CITY_NAME', 'DEP_DELAY', 'DISTANCE', 'Day', 'DepTime_sin', 'DepTime_cos']\n",
    "# Split features into numerical and categorical\n",
    "numerical_features = df[selected_features].select_dtypes(include=['float64', 'int64']).columns.tolist()\n",
    "categorical_features = df[selected_features].select_dtypes(include=['object']).columns.tolist()\n",
    "\n",
    "# Automatically distinguish high-cardinality categorical features\n",
    "low_cardinality_features = [col for col in categorical_features if df[col].nunique() <= 20]\n",
    "high_cardinality_features = [col for col in categorical_features if df[col].nunique() > 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f80a971-a509-45bd-b250-b0c33907bd2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training LinearRegression...\n",
      "\n",
      "LinearRegression Evaluation Metrics:\n",
      "MAE: 5.86\n",
      "MSE: 56.69\n",
      "RMSE: 7.53\n",
      "Training RandomForestRegressor...\n",
      "\n",
      "RandomForestRegressor Evaluation Metrics:\n",
      "MAE: 6.13\n",
      "MSE: 61.56\n",
      "RMSE: 7.85\n",
      "Training XGBRegressor...\n",
      "\n",
      "XGBRegressor Evaluation Metrics:\n",
      "MAE: 5.80\n",
      "MSE: 55.92\n",
      "RMSE: 7.48\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split data into train and test\n",
    "X = df[selected_features]\n",
    "y = df[main_label]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Preprocessing for numerical, low-cardinality, and high-cardinality features\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numerical_features),\n",
    "        ('low_cat', OneHotEncoder(handle_unknown='ignore'), low_cardinality_features),\n",
    "        ('high_cat', OneHotEncoder(handle_unknown='ignore'), high_cardinality_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Define regression models\n",
    "models = {\n",
    "    'LinearRegression': Pipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('regressor', LinearRegression())\n",
    "    ]),\n",
    "    'RandomForestRegressor': Pipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('regressor', RandomForestRegressor(n_estimators=100))\n",
    "    ]),\n",
    "    'XGBRegressor': Pipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('regressor', XGBRegressor(objective='reg:squarederror'))\n",
    "    ])\n",
    "}\n",
    "\n",
    "# Train and evaluate models\n",
    "results = {}\n",
    "for name, model in models.items():\n",
    "    print(f\"Training {name}...\")\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate evaluation metrics\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    results[name] = {'MAE': mae, 'MSE': mse, 'RMSE': rmse}\n",
    "    \n",
    "    # Save the model\n",
    "    joblib.dump(model, f'{name}_AD_model.pkl')\n",
    "\n",
    "    # Print evaluation metrics\n",
    "    print(f\"\\n{name} Evaluation Metrics:\")\n",
    "    print(f\"MAE: {mae:.2f}\")\n",
    "    print(f\"MSE: {mse:.2f}\")\n",
    "    print(f\"RMSE: {rmse:.2f}\")\n",
    "\n",
    "# Compare model performances\n",
    "metrics_df = pd.DataFrame(results).T\n",
    "metrics_df.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title(\"Model Performance Comparison AD\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c552da5a-68d6-4ee0-ac3b-49694f9af73d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting learning curve for LinearRegression...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting learning curve for RandomForestRegressor...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting learning curve for XGBRegressor...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot learning curve for each model\n",
    "def plot_learning_curve(estimator, X, y, title):\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=5, scoring='neg_mean_squared_error', n_jobs=-1\n",
    "    )\n",
    "    train_scores_mean = -np.mean(train_scores, axis=1)\n",
    "    test_scores_mean = -np.mean(test_scores, axis=1)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(train_sizes, train_scores_mean, label=\"Training Error\")\n",
    "    plt.plot(train_sizes, test_scores_mean, label=\"Validation Error\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Training Size\")\n",
    "    plt.ylabel(\"Mean Squared Error\")\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "# Generate learning curves for all models\n",
    "for name, model in models.items():\n",
    "    print(f\"Plotting learning curve for {name}...\")\n",
    "    plot_learning_curve(model, X, y, f\"Learning Curve - {name}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d54216b8-47db-40b3-860b-091ac069fd12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:14.56347\tvalidation_1-rmse:14.17824\n",
      "[1]\tvalidation_0-rmse:11.69479\tvalidation_1-rmse:11.46053\n",
      "[2]\tvalidation_0-rmse:9.93399\tvalidation_1-rmse:9.87158\n",
      "[3]\tvalidation_0-rmse:8.88382\tvalidation_1-rmse:8.96226\n",
      "[4]\tvalidation_0-rmse:8.27915\tvalidation_1-rmse:8.47756\n",
      "[5]\tvalidation_0-rmse:7.91651\tvalidation_1-rmse:8.19403\n",
      "[6]\tvalidation_0-rmse:7.68566\tvalidation_1-rmse:8.02402\n",
      "[7]\tvalidation_0-rmse:7.54891\tvalidation_1-rmse:7.92681\n",
      "[8]\tvalidation_0-rmse:7.45093\tvalidation_1-rmse:7.86535\n",
      "[9]\tvalidation_0-rmse:7.33412\tvalidation_1-rmse:7.80292\n",
      "[10]\tvalidation_0-rmse:7.26119\tvalidation_1-rmse:7.76332\n",
      "[11]\tvalidation_0-rmse:7.20746\tvalidation_1-rmse:7.74393\n",
      "[12]\tvalidation_0-rmse:7.16443\tvalidation_1-rmse:7.72328\n",
      "[13]\tvalidation_0-rmse:7.11516\tvalidation_1-rmse:7.71593\n",
      "[14]\tvalidation_0-rmse:7.08607\tvalidation_1-rmse:7.69140\n",
      "[15]\tvalidation_0-rmse:7.02488\tvalidation_1-rmse:7.66584\n",
      "[16]\tvalidation_0-rmse:6.98632\tvalidation_1-rmse:7.63337\n",
      "[17]\tvalidation_0-rmse:6.96751\tvalidation_1-rmse:7.62207\n",
      "[18]\tvalidation_0-rmse:6.92379\tvalidation_1-rmse:7.62787\n",
      "[19]\tvalidation_0-rmse:6.89743\tvalidation_1-rmse:7.62417\n",
      "[20]\tvalidation_0-rmse:6.85753\tvalidation_1-rmse:7.60259\n",
      "[21]\tvalidation_0-rmse:6.81725\tvalidation_1-rmse:7.60329\n",
      "[22]\tvalidation_0-rmse:6.80175\tvalidation_1-rmse:7.59987\n",
      "[23]\tvalidation_0-rmse:6.75852\tvalidation_1-rmse:7.59038\n",
      "[24]\tvalidation_0-rmse:6.72985\tvalidation_1-rmse:7.58384\n",
      "[25]\tvalidation_0-rmse:6.68939\tvalidation_1-rmse:7.57449\n",
      "[26]\tvalidation_0-rmse:6.67085\tvalidation_1-rmse:7.57186\n",
      "[27]\tvalidation_0-rmse:6.65713\tvalidation_1-rmse:7.56371\n",
      "[28]\tvalidation_0-rmse:6.62064\tvalidation_1-rmse:7.56259\n",
      "[29]\tvalidation_0-rmse:6.60632\tvalidation_1-rmse:7.55703\n",
      "[30]\tvalidation_0-rmse:6.56849\tvalidation_1-rmse:7.53384\n",
      "[31]\tvalidation_0-rmse:6.54896\tvalidation_1-rmse:7.54189\n",
      "[32]\tvalidation_0-rmse:6.53647\tvalidation_1-rmse:7.54361\n",
      "[33]\tvalidation_0-rmse:6.52907\tvalidation_1-rmse:7.54051\n",
      "[34]\tvalidation_0-rmse:6.50833\tvalidation_1-rmse:7.54497\n",
      "[35]\tvalidation_0-rmse:6.49107\tvalidation_1-rmse:7.54033\n",
      "[36]\tvalidation_0-rmse:6.48025\tvalidation_1-rmse:7.53876\n",
      "[37]\tvalidation_0-rmse:6.46882\tvalidation_1-rmse:7.53858\n",
      "[38]\tvalidation_0-rmse:6.44288\tvalidation_1-rmse:7.53745\n",
      "[39]\tvalidation_0-rmse:6.40702\tvalidation_1-rmse:7.52916\n",
      "[40]\tvalidation_0-rmse:6.39275\tvalidation_1-rmse:7.52327\n",
      "[41]\tvalidation_0-rmse:6.37093\tvalidation_1-rmse:7.52418\n",
      "[42]\tvalidation_0-rmse:6.36551\tvalidation_1-rmse:7.52243\n",
      "[43]\tvalidation_0-rmse:6.35318\tvalidation_1-rmse:7.52094\n",
      "[44]\tvalidation_0-rmse:6.34063\tvalidation_1-rmse:7.51647\n",
      "[45]\tvalidation_0-rmse:6.33233\tvalidation_1-rmse:7.51926\n",
      "[46]\tvalidation_0-rmse:6.32055\tvalidation_1-rmse:7.52167\n",
      "[47]\tvalidation_0-rmse:6.31096\tvalidation_1-rmse:7.51608\n",
      "[48]\tvalidation_0-rmse:6.27380\tvalidation_1-rmse:7.52481\n",
      "[49]\tvalidation_0-rmse:6.25624\tvalidation_1-rmse:7.52571\n",
      "[50]\tvalidation_0-rmse:6.23754\tvalidation_1-rmse:7.51903\n",
      "[51]\tvalidation_0-rmse:6.22961\tvalidation_1-rmse:7.52331\n",
      "[52]\tvalidation_0-rmse:6.21091\tvalidation_1-rmse:7.52736\n",
      "[53]\tvalidation_0-rmse:6.20005\tvalidation_1-rmse:7.52142\n",
      "[54]\tvalidation_0-rmse:6.19341\tvalidation_1-rmse:7.52046\n",
      "[55]\tvalidation_0-rmse:6.18270\tvalidation_1-rmse:7.51681\n",
      "[56]\tvalidation_0-rmse:6.15362\tvalidation_1-rmse:7.51506\n",
      "[57]\tvalidation_0-rmse:6.13521\tvalidation_1-rmse:7.50964\n",
      "[58]\tvalidation_0-rmse:6.12490\tvalidation_1-rmse:7.51000\n",
      "[59]\tvalidation_0-rmse:6.11143\tvalidation_1-rmse:7.50300\n",
      "[60]\tvalidation_0-rmse:6.10781\tvalidation_1-rmse:7.50221\n",
      "[61]\tvalidation_0-rmse:6.09445\tvalidation_1-rmse:7.50719\n",
      "[62]\tvalidation_0-rmse:6.06311\tvalidation_1-rmse:7.48924\n",
      "[63]\tvalidation_0-rmse:6.03507\tvalidation_1-rmse:7.48743\n",
      "[64]\tvalidation_0-rmse:6.03141\tvalidation_1-rmse:7.48856\n",
      "[65]\tvalidation_0-rmse:6.02619\tvalidation_1-rmse:7.49288\n",
      "[66]\tvalidation_0-rmse:6.01802\tvalidation_1-rmse:7.49144\n",
      "[67]\tvalidation_0-rmse:6.00641\tvalidation_1-rmse:7.49259\n",
      "[68]\tvalidation_0-rmse:5.99799\tvalidation_1-rmse:7.49103\n",
      "[69]\tvalidation_0-rmse:5.99011\tvalidation_1-rmse:7.49126\n",
      "[70]\tvalidation_0-rmse:5.97809\tvalidation_1-rmse:7.48911\n",
      "[71]\tvalidation_0-rmse:5.97224\tvalidation_1-rmse:7.48637\n",
      "[72]\tvalidation_0-rmse:5.96603\tvalidation_1-rmse:7.48377\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\General\\Lib\\site-packages\\xgboost\\sklearn.py:793: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[73]\tvalidation_0-rmse:5.95522\tvalidation_1-rmse:7.47713\n",
      "[74]\tvalidation_0-rmse:5.94607\tvalidation_1-rmse:7.47922\n",
      "[75]\tvalidation_0-rmse:5.92870\tvalidation_1-rmse:7.47407\n",
      "[76]\tvalidation_0-rmse:5.92025\tvalidation_1-rmse:7.47044\n",
      "[77]\tvalidation_0-rmse:5.91638\tvalidation_1-rmse:7.47014\n",
      "[78]\tvalidation_0-rmse:5.91112\tvalidation_1-rmse:7.46993\n",
      "[79]\tvalidation_0-rmse:5.90733\tvalidation_1-rmse:7.47388\n",
      "[80]\tvalidation_0-rmse:5.89836\tvalidation_1-rmse:7.47704\n",
      "[81]\tvalidation_0-rmse:5.89512\tvalidation_1-rmse:7.47705\n",
      "[82]\tvalidation_0-rmse:5.88958\tvalidation_1-rmse:7.47479\n",
      "[83]\tvalidation_0-rmse:5.87839\tvalidation_1-rmse:7.47597\n",
      "[84]\tvalidation_0-rmse:5.86064\tvalidation_1-rmse:7.47344\n",
      "[85]\tvalidation_0-rmse:5.85535\tvalidation_1-rmse:7.47386\n",
      "[86]\tvalidation_0-rmse:5.84878\tvalidation_1-rmse:7.47541\n",
      "[87]\tvalidation_0-rmse:5.84495\tvalidation_1-rmse:7.47487\n",
      "[88]\tvalidation_0-rmse:5.81276\tvalidation_1-rmse:7.46979\n",
      "[89]\tvalidation_0-rmse:5.80852\tvalidation_1-rmse:7.46976\n",
      "[90]\tvalidation_0-rmse:5.78814\tvalidation_1-rmse:7.46161\n",
      "[91]\tvalidation_0-rmse:5.77043\tvalidation_1-rmse:7.47006\n",
      "[92]\tvalidation_0-rmse:5.76547\tvalidation_1-rmse:7.46892\n",
      "[93]\tvalidation_0-rmse:5.75622\tvalidation_1-rmse:7.46710\n",
      "[94]\tvalidation_0-rmse:5.74728\tvalidation_1-rmse:7.46778\n",
      "[95]\tvalidation_0-rmse:5.74410\tvalidation_1-rmse:7.46840\n",
      "[96]\tvalidation_0-rmse:5.73283\tvalidation_1-rmse:7.46355\n",
      "[97]\tvalidation_0-rmse:5.69690\tvalidation_1-rmse:7.47511\n",
      "[98]\tvalidation_0-rmse:5.68800\tvalidation_1-rmse:7.47414\n",
      "[99]\tvalidation_0-rmse:5.68569\tvalidation_1-rmse:7.47798\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Combine the preprocessor and XGBoost model into a Pipeline\n",
    "xgb_pipeline = Pipeline([\n",
    "    ('preprocessor', preprocessor),  # Use ColumnTransformer for preprocessing\n",
    "    ('regressor', XGBRegressor(objective='reg:squarederror', eval_metric='rmse'))\n",
    "])\n",
    "\n",
    "# Train the model with the pipeline and enable evaluation monitoring\n",
    "xgb_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# Get preprocessed data (optional, useful for manual evaluation)\n",
    "X_train_processed = preprocessor.transform(X_train)\n",
    "X_test_processed = preprocessor.transform(X_test)\n",
    "\n",
    "# Manually call XGBoost's eval_set to monitor performance during training\n",
    "xgb_model = xgb_pipeline.named_steps['regressor']\n",
    "xgb_model.fit(\n",
    "    X_train_processed, y_train,\n",
    "    eval_set=[(X_train_processed, y_train), (X_test_processed, y_test)],\n",
    "    early_stopping_rounds=50,  # Stop if no improvement for 50 rounds\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "# Print evaluation results\n",
    "results = xgb_model.evals_result()\n",
    "\n",
    "# Visualize training curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(results['validation_0']['rmse'], label='Training RMSE')\n",
    "plt.plot(results['validation_1']['rmse'], label='Validation RMSE')\n",
    "plt.title('Training Curve AD - XGBoost')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('RMSE')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "724c751a-63f7-43ec-bf0c-da52dfd26a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing cross-validation for LinearRegression...\n",
      "LinearRegression - Mean MSE: 61.47, Std: 12.56\n",
      "Performing cross-validation for RandomForestRegressor...\n",
      "RandomForestRegressor - Mean MSE: 65.71, Std: 13.03\n",
      "Performing cross-validation for XGBRegressor...\n",
      "XGBRegressor - Mean MSE: 61.58, Std: 13.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import cross_val_score, cross_validate\n",
    "\n",
    "# Define the scoring metric\n",
    "scoring = 'neg_mean_squared_error'  # Or other regression metrics, such as 'r2' or 'neg_mean_absolute_error', \"neg_mean_squared_error\"\n",
    "\n",
    "# Perform cross-validation for all models\n",
    "cv_results = {}\n",
    "for name, model in models.items():\n",
    "    print(f\"Performing cross-validation for {name}...\")\n",
    "    scores = cross_val_score(model, X, y, cv=5, scoring=scoring, n_jobs=-1)\n",
    "    cv_results[name] = {\n",
    "        'Mean Score': -np.mean(scores),  # Negate to convert negative MSE to positive MSE\n",
    "        'Std Deviation': np.std(scores)\n",
    "    }\n",
    "    print(f\"{name} - Mean MSE: {-np.mean(scores):.2f}, Std: {np.std(scores):.2f}\")\n",
    "\n",
    "# Display results\n",
    "cv_df = pd.DataFrame(cv_results).T\n",
    "cv_df.plot(kind='bar', figsize=(10, 6), y='Mean Score', yerr='Std Deviation', capsize=4)\n",
    "plt.title(\"Cross-Validation AD Results (MSE)\")\n",
    "plt.ylabel(\"Mean Squared Error (Lower is Better)\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d2f39b2-e9d1-4897-8900-81f6032c44b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing cross-validation for LinearRegression...\n",
      "LinearRegression - Mean R2: 0.79, Std: 0.03\n",
      "Performing cross-validation for RandomForestRegressor...\n",
      "RandomForestRegressor - Mean R2: 0.78, Std: 0.03\n",
      "Performing cross-validation for XGBRegressor...\n",
      "XGBRegressor - Mean R2: 0.79, Std: 0.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Train and evaluate models with R2\n",
    "cv_results = {}\n",
    "for name, model in models.items():\n",
    "    print(f\"Performing cross-validation for {name}...\")\n",
    "    scores = cross_val_score(model, X, y, cv=5, scoring='r2', n_jobs=-1)  # Use R2 as scoring metric\n",
    "    cv_results[name] = {\n",
    "        'Mean R2': np.mean(scores),\n",
    "        'Std Deviation': np.std(scores)\n",
    "    }\n",
    "    print(f\"{name} - Mean R2: {np.mean(scores):.2f}, Std: {np.std(scores):.2f}\")\n",
    "\n",
    "# Visualize R2 results\n",
    "cv_df = pd.DataFrame(cv_results).T\n",
    "cv_df.plot(kind='bar', figsize=(10, 6), y='Mean R2', yerr='Std Deviation', capsize=4)\n",
    "plt.title(\"Cross-Validation AD Results (R2)\")\n",
    "plt.ylabel(\"R2 (Higher is Better)\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6177269b-6477-4404-8373-e366d8cf2b91",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}