Academy & Industry Research Collaboration Center (AIRCC)

Volume 10, Number 14, November 2020

An Automated Data-Driven Prediction of Product Pricing Based on Covid-19 Case Number
using Data Mining and Machine Learning


Zhuoyang Han1, Ang Li2 and Yu Sun3, 1University of California, USA, 2California State University, USA, 3California State Polytechnic University, USA


In early 2020, a global outbreak of Corona Disease Virus 2019 (Covid-19) emerged as an acute respiratory infectious Disease with high infectivity and incidence. China imposed a blockade on the worst affected city of Wuhan at the end of January 2020, and over time, covid19 spread rapidly around the world and was designated pandemic by the World Health Organization on March 11. As the epidemic spread, the number of confirmed cases and the number of deaths in countries around the world are changing day by day. Correspondingly, the price of face masks, as important epidemic prevention materials, is also changing with each passing day in international trade. In this project, we used machine learning to solve this problem. The project used python to find algorithms to fit daily confirmed cases in China, daily deaths, daily confirmed cases in the world, and daily deaths in the world, the recorded mask price was used to predict the effect of the number of cases on the mask price. Under such circumstances, the demand for face masks in the international trade market is enormous, and because the epidemic changes from day to day, the prices of face masks fluctuate from day to day and are very unstable. We would like to provide guidance to traders and the general public on the purchase of face masks by forecasting face mask prices.


Corona Virus, Machine Learning, Price Prediction, Linear Regression, Poly Regression, Data Cleaning.