Comparative Study of Various Machine Learning Algorithms for Pharmaceutical Drug Sales Prediction

Abstract

For any business, it is essential to predict the sales of future months for proper stock maintenance. Especially, in the pharmaceutical drug business, it is crucial to restrict the wastage of drugs due to expiry dates. As of now, most pharmacists and drug sellers predict future drug sales manually on their own sales experience. However, artificial intelligence and machine learning can play a vital role here by predicting drug sales using past sales records. In this study, we are employing machine learning algorithms such as Linear Regression, Random Forest, Support Vector Machine, and XGBoost on the sales data to predict future sales and compare the accuracy of different algorithms on some specific kinds of most used drugs globally. The dataset which was used consisted of drug sales from various drugs such as antipyretics, antihistamines, etc. The dataset consisted of hourly, weekly, monthly, and yearly sales data. After pre-processing the data, the four machine learning algorithms were used to predict future sales. According to our findings, The XGboost Model performed well compared to the other three models used to predict sales. The results are shown using graphs and tables.