A Two-Level Statistical Model for Big Mart Sales Prediction

Abstract

Sales forecasting is an important aspect of different companies engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to efficiently allocate resources, to estimate achievable sales revenue and to plan a better strategy for future growth of the company. In this paper, prediction of sales of a product from a particular outlet is performed via a two-level approach that produces better predictive performance compared to any of the popular single model predictive learning algorithms. The approach is performed on Big Mart Sales data of the year 2013. Data exploration, data transformation and feature engineering play a vital role in predicting accurate results. The result demonstrated that the two-level statistical approach performed better than a single model approach as the former provided more information that leads to better prediction.