International Journal of Innovative Research in Computer and Communication Engineering

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TITLE Machine Learning–Based Sales Forecasting and Market Trend Evaluation for BigMart
ABSTRACT Retail organizations function in constantly changing markets where variations in demand directly affect inventory handling and financial performance. Accurate sales forecasting has therefore become a critical requirement for effective operational planning. This work describes a data-driven modeling approach developed to estimate product sales and observe demand trends within the BigMart retail chain. Past sales transactions along with item-related details and store-level characteristics were used as inputs for building prediction models, several machine learning techniques, include regression-based and tree-based algorithms, were trained and evaluated to estimate future sales at both item and store levels. Prediction accuracy was examined by computing absolute and squared discrepancies between actual and anticipated values sales figures. The findings indicate that advanced Complex relationships can be captured by learning models within retail data and generate reliable demand forecasts. The outcomes of this study show how predictive analytics can support inventory planning, marketing decisions, and managerial decision-making processes.
AUTHOR SANTHOSH S C, POOJA TARAGAR PG Student, Dept. of MCA, City Engineering College, Bengaluru, India Assistant Professor, Dept. of MCA, City Engineering College, Bengaluru, India
VOLUME 177
DOI DOI: 10.15680/IJIRCCE.2025.1312062
PDF pdf/62_Machine Learning–Based Sales Forecasting and Market Trend Evaluation for BigMart.pdf
KEYWORDS
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