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+# below is the code (modal) for solar irradiance using random forest regressor algorithm 
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error
+from sklearn.preprocessing import StandardScaler
+
+# Load your data
+data = pd.read_csv('solar_irradiance_data_1.csv')
+
+# Feature Engineering
+data['day_of_year'] = pd.to_datetime(data[['year', 'month', 'day']]).dt.dayofyear
+
+# Define features and target variable
+X = data[['latitude', 'longitude', 'day_of_year', 'hour']]
+y = data['solar_irradiance']
+
+# Optional: Standardize the features
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Split data into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+# Initialize and train the model
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Evaluate the model
+mae = mean_absolute_error(y_test, y_pred)
+print(f'Mean Absolute Error: {mae}')
+
+# Example prediction
+new_data = pd.DataFrame({
+    'latitude': [-16.5],
+    'longitude': [-68.5],
+    'day_of_year': [205],
+    'hour': [12]
+})
+new_data_scaled = scaler.transform(new_data)
+predicted_irradiance = model.predict(new_data_scaled)
+print(f'Predicted Solar Irradiance: {predicted_irradiance[0]} W/m^2')