2% more accuracy ill take it

dataset_update.py

@@ -18,19 +18,19 @@ end_date = datetime.now().strftime("%Y-%m-%d")  # Today's date
 # WD10M           MERRA-2 Wind Direction at 10 Meters (Degrees)
 # more at https://power.larc.nasa.gov/data-access-viewer/
 weather_params = [
-    "T2M",
-    "T2M_MAX",
-    "T2M_MIN",
-    "T2MDEW",
-    "PRECTOTCORR",
+    # "T2M",
+    # "T2M_MAX",
+    # "T2M_MIN",
+    # "T2MDEW",
+    # "PRECTOTCORR",
     "QV2M",
-    "RH2M",
-    "PS",
-    "CLOUD_AMT",
-    "WS10M",
-    "WD10M",
-    "ALLSKY_KT",
-    "ALLSKY_SFC_SW_DWN",
+    # "RH2M",
+    # "PS",
+    # "CLOUD_AMT",
+    # "WS10M",
+    # "WD10M",
+    # "ALLSKY_KT",
+    # "ALLSKY_SFC_SW_DWN",
 ]
 
 params = {

sklearn_port.py

@@ -49,12 +49,11 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02, shuffl
 
 # 5. Train linear regression model
 model = RandomForestRegressor(
-        n_estimators=1000,       # Number of trees
-        max_features='sqrt',    # Features considered at each split
-        max_depth=100,           # Maximum depth of each tree
+        n_estimators=200,       # Number of trees
+        # max_features='sqrt',    # Features considered at each split
+        # max_depth=30,           # Maximum depth of each tree
         # n_jobs=-1,              # Use all CPU cores
         random_state=42         # Reproducibility
-
 )
 model.fit(X_train, y_train)