Volume 2 - Issue 2, March - April 2026

Accurate sales forecasting is a critical component of effective decision-making in the retail industry, as it directly affects inventory planning, revenue management, supply chain efficiency, and customer satisfaction. With the widespread adoption of digital billing systems and point-of-sale technologies, retailers now generate massive volumes of transactional data on a daily basis. Traditional forecasting techniques often struggle to analyse such large,highdimensional, and dynamic datasets, which results in limited prediction accuracy and poor adaptability to changing market conditions. This paper presents a detailed journal-oriented analysis of retail sales forecasting using machine learning techniques. The study focuses on supervised learning models that are capable of learning complex patterns from historical sales data. Key factors such as sales trends, seasonal variations, and demand fluctuations are examined to evaluate model performance. The forecasting process includes data preprocessing, feature selection, model training, and performance evaluation using standard error metrics. A comparative analysis is conducted to highlight the strengths and limitations of different machine learning approaches in retail forecasting applications. The results indicate that ensemble-based models provide improved accuracy and stability when compared to traditional statistical and single-model approaches. The findings demonstrate that machine learning-based forecasting models significantly enhance prediction accuracy and decision reliability, making them highly suitable for modern data-driven retail environments.

Dr.N.Kalaivani, Surruthi.S,"Predictive Analysis of Retail Sales Forecasting Using Machine Learning Techniques" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(807-813) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.