Machine Learning Price Prediction During and Before COVID-19 and Consumer Buying Behavior

Abstract

In the unprecedented situation of COVID-19, the global economy has turned upside down. This has led to sudden and unprecedented pressures on products demand and price forecasting. The study utilized regression techniques to predict product prices during and before COVID-19 using multiple influencing factors such as increase of COVID-19 positive cases on daily basis, number of deaths on a particular day, and government restrictions level. The data was gathered from worldometers website and combined with local store on sales based on the date. The results were eye opening as the product sold in the months of Mach, April, May and June 2020 were different than last year. This means the customers buying habits were totally altered due to many reasons such as job loss, wages reduction due to remote working, or promotions. Moreover, these products prices were directly proportional to increase of new COVID-19 cases, rise of daily deaths and government restriction levels imposed during the pandemic. The study uses machine learning data mining algorithms such as Logistic regression (LR), Decision Tree, Random Forest and K-Nearest Neighbor. Decision Tree and Random Forest works best in the pandemic situation to predict product price as compared to Logistic Regression and KNN. However, different outcomes were recorded when comparing the sales during pandemic and before pandemic.