Predictive analysis of wholesale customer purchases using machine learning models

To forecast future purchases across several product categories—including fresh milk, groceries, frozen foods, detergents, paper, and delicatessen—this study looks at the purchasing patterns of wholesale clients using machine learning models. Support Vector Machines, Generalised Linear Models, and Linear Regression were among the predictive models used. RMSE, MAPE, and R² metrics were used to assess each model's performance and ascertain its correctness. The results show that when it came to forecasting consumer purchases, GLM performed better than other models, including LR, SVM, RT, and ER.