• Version
• Download 12
• File Size 282.70 KB
• File Count 1
• Create Date 10 May 2025
• Last Updated 10 May 2025

SALES FORECASTING USING MACHINE LEARNING

Authors:

Ms.NANDHINI K, Ms.NANDHINI K, Mr.SACHIN M, Mr. ROHIT.R

Mrs.Revathi M (MENTOR)

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

SRI SHAKTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY

Abstract - Sales forecasting remains a critical component in the strategic planning and operational efficiency of retail enterprises. This study presents a data-driven forecasting model tailored for retail sales prediction, using a real-world dataset comprising historical sales records, outlet characteristics, and product features. The methodology emphasizes robust data preprocessing, including handling of missing values, outlier treatment, and categorical encoding, followed by the application of ensemble-based regression. Among various algorithms evaluated, the Extreme Gradient Boosting Random Forest Regressor (XGBRFRegressor) demonstrated superior predictive performance, achieving consistent accuracy across cross-validation folds. To enhance practical applicability, the forecasting system was integrated into an interactive interface, enabling real-time prediction based on user-defined input parameters. The proposed approach offers a scalable and reliable framework for informed decision-making in retail operations, particularly in inventory management, demand planning, and revenue optimization.

Key Words: predicting, forecasting, demand planning, sales