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@article{195621,
        author = {VUNA VENKATA VIDYASAGAR and GEDDA TRIVENI and G.V. SUDHEER BABU and GONAPA TIRUMALA and D. RAGHU},
        title = {BIG MART SALES PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2343-2350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195621},
        abstract = {The rapid advancement of artificial intelligence has enabled the development of intelligent systems capable of analyzing retail data, creating new opportunities for data-driven business decision- making. This study presents a Machine Learning-Based Sales Prediction System designed to analyze historical sales data and generate accurate sales forecasts using advanced data mining and machine learning techniques. The system utilizes structured retail datasets containing product and store-level attributes to ensure effective model training and performance. Data preprocessing, normalization, and feature engineering are performed using Python-based libraries, while prediction is implemented using machine learning models such as Support Vector Machine, Random Forest, and Logistic Regression. Furthermore, a Flask-based web application is integrated to provide real-time prediction and interactive user experience. Experimental results demonstrate that the integration of multiple models and feature engineering techniques significantly improves prediction accuracy and system performance. The proposed system provides a scalable and efficient solution for sales forecasting, supporting improved decision-making, optimized inventory management, and enhanced business profitability.},
        keywords = {Sales prediction system, Logistic Regression, Flask, predictive modelling, ARIMA.},
        month = {April},
        }