International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| 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/62_Machine Learning–Based Sales Forecasting and Market Trend Evaluation for BigMart.pdf | |
| KEYWORDS | |
| References | [1]. P. Nagendra and R. S. Patil, “Sales Forecasting Making Use of Machine Learning Techniques: A Review,” Journal of Computer Applications International, vol. 180, no. 45, pp. 1–7, 2020. [2]. A. Sharma and M. Kumar, “Predictive Analytics in Retail: Machine Learning Approaches for Sales Forecasting,” Journal of Retail Analytics, vol. 6, no. 2, pp. 12–25, 2021. [3]. R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. OTexts, 2021. [Online]. [4]. Ching Wu Chu and Guoqiang Peter Zhang, “A comparative study of linear and nonlinear models for aggregate retails sales forecasting”, Int. Journal Production Economics, vol. 86, pp. 217- 231, 2003. [5]. Wang, Haoxiang. "Sustainable development and management in consumer electronics using soft computation." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 56.- 2. Suma, V., and Shavige Malleshwara Hills. "Data Mining based Prediction of D. [6]. Giuseppe Nunnari, Valeria Nunnari, “Forecasting Monthly Sales Retail Time Series: A Case Study”, Proc. of IEEE Conf. on Business Informatics (CBI), July 2017. [7]. X. Yua, Z. Qi, Y. Zhao, Support Vector Regression for Newspaper/Magazine Sales Forecasting, Procedia Computer Science 17 ( 2013) 1055–1062. [8]. E. Hadavandi, H. Shavandi, A. Ghanbari, An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: a Case study of the printed circuit board, Expert Systems with Applications 38 (2011) 9392–9399. [9]. Fantazzini, Z. Toktamysova, Forecasting German car sales using Google data and multivariate models, Int. J. Production Economics 170 (2015) 97-135. [10]. Castillo et al. (2017) demonstrated enhanced forecasting accuracy and useful decision assistance for editorial management by using artificial intelligence approaches to forecast sales of recently published books in an actual publishing company. |