| # Template for data preprocessing script for {{phase_name}} | |
| import pandas as pd | |
| # Add other necessary imports | |
| def preprocess_data(raw_data_path, processed_data_path): | |
| """ | |
| Reads raw data, preprocesses it, and saves the processed data. | |
| """ | |
| try: | |
| # Load raw data (replace with your actual data loading) | |
| data = pd.read_csv(raw_data_path) # Example: CSV loading | |
| print("Data loaded successfully. Starting preprocessing...") | |
| # --- Data Preprocessing Steps --- | |
| # Example steps (customize based on your data and project) | |
| # 1. Handle missing values | |
| data = data.fillna(0) # Example: fill NaN with 0 | |
| # 2. Feature engineering (example: create a new feature) | |
| data['feature_length'] = data['text_column'].str.len() # Example: length of text column | |
| # 3. Text cleaning (if applicable - example: lowercasing) | |
| if 'text_column' in data.columns: | |
| data['text_column'] = data['text_column'].str.lower() | |
| # --- End of Preprocessing Steps --- | |
| # Save processed data | |
| data.to_csv(processed_data_path, index=False) | |
| print(f"Processed data saved to {processed_data_path}") | |
| except FileNotFoundError: | |
| print(f"Error: Raw data file not found at {raw_data_path}") | |
| except Exception as e: | |
| print(f"Error during data preprocessing: {e}") | |
| if __name__ == "__main__": | |
| raw_data_filepath = "data/raw_dataset.csv" # Replace with your raw data path | |
| processed_data_filepath = "data/processed_dataset.csv" # Replace with your desired output path | |
| preprocess_data(raw_data_filepath, processed_data_filepath) |