Walmart Sales Prediction By Using Machine Learning Algorithms

Sales forecasting is essential for today’s businesses as it’s a key factor to improve it. “Sales Forecasting” is the process in which a person uses different techniques to predict the sales of upcoming weeks, months, or years. In this research paper, the literature review is about the machine learning algorithms applied by different researchers to predict Walmart sales. The different algorithms researchers use are Neural Network, OLS Regression, XGBoost, SVM, Lasso Regression, Random Forest, Extra Tree Regression, KNN, and Linear Regression. From all the algorithms applied, Extra Tree Regression performed well and got 98.20% accuracy. Finally, we compared random forest, extra tree regression, XGBoost algorithms, and KNN regression models for Walmart sales. XGBoost came out to be at the top among those, showing the highest accuracy of 98.24%. This study proves the potential of machine learning in this field of sales forecasting and has opened up a wide scope of future research to increase accuracy.