Data Cleaning & preprocessing

Ensured data quality by handling missing values, removing duplicates, and correcting inconsistencies within transaction records. Applied techniques such as outlier detection and normalization to prepare the dataset for accurate analysis and modeling. Focused on maintaining data integrity to enhance downstream machine learning performance.