Predictive model based on machine learning for the prevention of overstock in a footwear company

Méndez K.M.A.; Torres A.L.M.; Ovalle C. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2023)

Resumen

Sales forecasting is an essential process for companies as it allows them to plan and make appropriate decisions about their workforce, cash flow and resources. The objective of sales forecasting is to predict future sales from the previous period's sales information. For the retail industry, it is very important because accurate forecasts can help companies maximize their investments, reduce inventory costs, increase sales and profits, and reduce risks. The most recent and effective method for forecasting future data is Machine Learning. Likewise, in the present work, the logistic regression algorithm and decision tree have been applied to determine the best-selling products and categories. The logistic regression algorithm had an accuracy of 97%, with a confusion matrix of 98.1% and 94.4% of true positives and true negatives respectively. The accuracy metric was 97% and the completeness metric was 96%. The decision tree algorithm had an accuracy of 85%, with a confusion matrix of 86.6% and 83% of true positives and true negatives respectively. The accuracy metric was 87% and the completeness metric was 84%. It could be determined that the balerina category is the most sold category with 84.3%, and that the spring and summer seasons are the most sold.

Instituciones: Universidad Privada del Norte