During March 2025, Pranav developed an outlier detection and treatment feature for the TCS-2021/Data-Mining-Project repository, focusing on enhancing the data preprocessing pipeline. He implemented multiple numerical outlier detection methods, including IQR, Z-Score, Modified Z-Score, and Percentile, and provided treatment options such as removing, capping, or replacing outliers with the median. Using Python and Streamlit, Pranav integrated interactive UI components and visual previews, enabling users to review and validate outlier handling decisions. This work improved data quality and user confidence in preprocessing, demonstrating depth in data preprocessing, data visualization, and outlier detection within a production-oriented workflow.