# 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)