Small Retailers Empowered to Boost Profits through Cutting-Edge Sales Forecasting
The article explores using machine learning to predict sales in a small supermarket. They tested three methods: linear regression, random forest, and gradient boosting. Results show that linear regression had more errors, while random forest and gradient boosting performed similarly, with gradient boosting slightly better. This study demonstrates that small retailers can use machine learning for sales forecasting with limited data and provides suggestions for improving predictions.
