Create model.py

model.py

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+       import pandas as pd
+       from sklearn.model_selection import train_test_split
+       from sklearn.ensemble import RandomForestRegressor  # Example model
+       from sklearn.metrics import mean_squared_error
+       import joblib
+
+       # 1. Load your data
+       data = pd.read_csv('your_data.csv')  # Replace with your data file
+
+       # 2. Preprocess the data
+       #  (Add your data cleaning, transformation, and feature engineering steps here)
+       # Example:
+       # data['Delay_Days__c'] = data['Delay_Days__c'].fillna(0)
+       # data = pd.get_dummies(data, columns=['Quality_Report__c'])
+
+       # 3. Define features (X) and target (y)
+       #  (Adjust these based on your actual column names)
+       X = data.drop(['Quality_Score__c', 'Timeliness_Score__c', 'Safety_Score__c', 'Communication_Score__c', 'Final_Score_c'], axis=1)
+       y = data[['Quality_Score__c', 'Timeliness_Score__c', 'Safety_Score__c', 'Communication_Score__c', 'Final_Score_c']]
+
+       # 4. Split data into training and testing sets
+       X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+       # 5. Choose a model
+       model = RandomForestRegressor(n_estimators=100, random_state=42)  # Example: Random Forest
+
+       # 6. Train the model
+       model.fit(X_train, y_train)
+
+       # 7. Evaluate the model
+       y_pred = model.predict(X_test)
+       mse = mean_squared_error(y_test, y_pred)
+       print(f'Mean Squared Error: {mse}')
+
+       # 8. Save the model
+       joblib.dump(model, 'your_model.pkl')  # Save the model to a file
+       print('Model saved to your_model.pkl')
+