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# 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) |