Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction

Pravar Dilip Mahajan, Abhinav Maurya, Aly Megahed, Alaa Elwany, Ray Strong, Jeanette Blomberg

Abstract: Highlights•We consider a revenue change prediction problem.•We propose a framework that casts the problem as a classification one.•Our method maximizes prediction precision while minimizing sacrifice in accuracy.•Our method treats class imbalance that is typical in datasets of this problem.•We validate our method on real-world datasets and compare it with prior art methods.