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@article{198835,
        author = {M. Yogesh Kumar and B. Abinaya},
        title = {Predictive analysis for BigMart sale using machine learning algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12236-12240},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198835},
        abstract = {Retail sales forecasting is a critical challenge for modern businesses that operate across multiple outlets. This paper presents a machine learning-based predictive system for BigMart sales data, leveraging a Random Forest Classifier to predict product sales across various store locations. The proposed system integrates a web-based front-end developed using HTML, CSS, JavaScript, and Bootstrap, with a Python-based back-end that handles data preprocessing, model training, and real-time prediction. Experimental results demonstrate that the Random Forest model achieves high accuracy in classifying sales performance levels, enabling retail managers to make data-driven inventory and marketing decisions. The system provides an intuitive user interface for inputting product and store parameters and returning instant sales predictions.},
        keywords = {BigMart, Sales Prediction, Machine Learning, Random Forest Classifier, Python, Retail Analytics, Predictive Modelling},
        month = {April},
        }