International Journal on Science and Technology

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Call for Paper Volume 17 Issue 2 April-June 2026 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
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Volume 17 Issue 2
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Predictive Analysis for Big Mart Sales using Machine Learning

Author(s) V MANASA, Ms. S VARSHITHA, Ms. V SIVA KUMARI, Mr. J MAHAMMAD RAFI, CHATTA BALAJI
Country India
Abstract Sales analysis is required for supermarkets to understand the requirements of an increase the sales of product. Feature selection is an important process in sales analysis and this improves the performance of overall analysis. In further prediction of sales are mainly done based on traditional methods such as; data handling, human judgement, basic statical tools. In this system, sales data is analyzed using spreadsheets and simple regression techniques without the support of advanced machine learning algorithm. These methods only focus on recent sales trends and failed to utilize the maximum of historical data and multidimensional data as a result sales prediction is less accurate and cannot efficiently identifying complex relationships. To overcome these problems, we can use advanced machine learning algorithm that is XGBoost. This algorithm is selected because it gives high accuracy and handles big data efficiently. The XGBoost is applied with univariant and bivariant to analyze the future importance of the data set. The system is scalable and can be used for large data sets and multiple stores. It provides accurate and reliable sales predictions. The system can process data in real time for quick decision making, supports better business planning and profit improvement.
Keywords Sales, Big mart, Data sets, Machine learning, Analysis, Prediction, XGBoost
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-03
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