Abstract

The retail industry, particularly the grocery sector, generates massive amounts of data daily. Predicting sales accurately is essential for effective inventory management, supply chain optimization, and revenue maximization. In this research paper, we propose a comprehensive study of various machine learning regression algorithms to predict Big Mart sales. We compare the performance of Linear  Regression, Decision Trees, Random Forests, Gradient Boosting, and Neural Networks to identify the most accurate model for sales prediction.

Keywords: Machine learning, Gradient boosting, Predictive analytics, Sales prediction