Título: Churn Detection in Agro -Industrial Cooperatives Using Machine Learning: A Practical Case Study for Strategic Decision -Making
Autores: Sergio Akio Tanaka, Sergio Kenji Sawasaki Tanaka, Danilo Sapoli Sanches & Hugo Valadares Siqueira
Resumo: This article proposes a predictive model for classifying churn behavior among cooperative members using Decision Tree algorithms. The solution integrates Machine Learning (ML) and data engineering to predict inactivity behaviors, utilizing three datasets: registration information, financial history, and production data of the members. The predictive model achieved 98.36
Palavras-chave: Artificial Intelligence; Machine Learning; CRM; Cooperatives; Churn detection; Decision Tree; Prediction.
Páginas: 4
Código DOI: 10.21528/CBIC2025-1120392
Artigo em PDF: CBIC_2025_paper1120392.pdf
Arquivo BibTeX:
CBIC_2025_1120392.bib