Machine Learning Approaches for Customer Churn Prediction in the Aquaculture Technology Sector
This study investigates the application of advanced machine learning techniques for customer churn prediction in the rapidly evolving aquaculture technology sector. We employ and compare three distinct models—Logistic Regression, Random Forest, and XGBoost—to analyze a synthesized dataset representative of the industry. The research encompasses comprehensive data preprocessing, feature engineering, and model evaluation using standard performance […]
