Impact of Machine Learning on Product Sales Forecast

Abstract

Our objective in this study was to identify the most effective techniques that impacted product sales through machine learning algorithms. Given various factors like product price, promotional offerings, and customer ratings, it was determined how these variables affected market performance. Customer reviews were found to have the biggest effect on sales figures with prices and promotional offers having the least impact on comparison. To accurately predict product performance in the future using current data sets, we used The Python programming language in convening with machine learning algorithms like linear regression, crest regression, decision tree regression, and random forest regression. Our findings showed that the decision tree regressor algorithm had the highest degree of accuracy when predicting future revenue generation from products. The implications of this study suggest that businesses may benefit from improving overall customer ratings while simultaneously providing competitive pricing structures to achieve better future sales results.