Update project structure and enhance model persistence

- Added new model and scaler files to .gitignore and output directory.
- Updated Dockerfile to create output/models directory.
- Revised README to include instructions for using a .env file for configuration.
- Enhanced config.py to load database credentials from environment variables.
- Implemented model saving functionality in salary_predictor.py for consistent and inconsistent earners.

salary_analytics/salary_predictor.py

@@ -8,6 +8,7 @@ import matplotlib.pyplot as plt
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from joblib import dump
 from .config import OUTPUT_PATHS
 
 class SalaryPredictor:
@@ -129,6 +130,11 @@ class SalaryPredictor:
             self.model_cons, self.scaler_cons = self.train_model(X_train_cons, y_train_cons, X_test_cons, y_test_cons)
             print("Model trained for consistent salary earners.")
             
+            # Save model and scaler
+            dump(self.model_cons, OUTPUT_PATHS['consistent_model'])
+            dump(self.scaler_cons, OUTPUT_PATHS['consistent_scaler'])
+            print("Saved consistent salary earner model and scaler.")
+            
             # Plot predictions
             X_test_cons_scaled = self.scaler_cons.transform(X_test_cons)
             y_pred = self.model_cons.predict(X_test_cons_scaled)
@@ -147,6 +153,11 @@ class SalaryPredictor:
             print("\nTraining model for inconsistent salary earners...")
             self.model_incons, self.scaler_incons = self.train_model(X_train_incons, y_train_incons, X_test_incons, y_test_incons)
             
+            # Save model and scaler
+            dump(self.model_incons, OUTPUT_PATHS['inconsistent_model'])
+            dump(self.scaler_incons, OUTPUT_PATHS['inconsistent_scaler'])
+            print("Saved inconsistent salary earner model and scaler.")
+            
             # Plot predictions
             X_test_incons_scaled = self.scaler_incons.transform(X_test_incons)
             y_pred = self.model_incons.predict(X_test_incons_scaled)