Why Data Preprocessing is Important

Financial datasets often require preprocessing before they can be used for machine learning.

Proper preprocessing improves data quality and enhances prediction accuracy.

Common preprocessing tasks include:

• Removing missing values
• Handling duplicate records
• Feature engineering
• Selecting relevant variables
• Normalizing or scaling numerical data (when required)

High-quality input data enables machine learning models to identify meaningful relationships more effectively.