Walmart Sales Prediction Using Multiple Linear Regression

Abstract

The sales of Walmart are often considered to be related to many factors, and indeed this is the case. This study aims to examine the impact of objectively observable independent variables on Walmart's weekly sales. The paper employs the Z-score standardization technique to normalize the dimensions and utilizes linear regression analysis to process data from 6,435 weekly sales records of Walmart. The model is derived after preprocessing and standardizing the data in this paper. The findings reveal that most variables have either a minor or significant effect on sales. Notably, temperature and Fuel Price exert a certain influence on sales, while Holiday, Consumer Price Index (CPI), and Unemployment rates have a substantial impact. This investigation into the objective factors affecting Walmart's sales will benefit the management by providing insights to refine strategic planning through environmental surveys, thereby aiding in the enhancement of the shopping atmosphere and customer experience, ultimately aiming to boost sales revenues.