Machine Learning Approaches for Customer Churn Prediction in the Aquaculture Technology Sector
Journal Title: International Journal of Current Science Research and Review - Year 2024, Vol 7, Issue 08
Abstract
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 metrics. Our findings demonstrate the superior performance of XGBoost, achieving 88% accuracy in predicting customer churn. Through feature importance analysis, we identify key churn predictors, with the difference between a customer’s last order amount and their mean order amount emerging as the most significant factor. Additionally, we utilize SHAP (SHapley Additive exPlanations) analysis to interpret model outcomes, revealing nuanced relationships between features and churn probability. The study highlights the critical role of consistent engagement, proactive customer support, and personalized retention strategies in reducing churn. Our research contributes to the growing body of knowledge on churn prediction in specialized technology sectors and provides actionable insights for improving customer retention strategies in the aquaculture industry. The paper concludes with recommendations for future research, including the integration of external data sources and exploration of deep learning approaches for temporal dependency analysis in customer behaviour.
Authors and Affiliations
Ni Luh Putu Asri Cahyani,
Keywords
Related Articles
- EP ID EP743007
- DOI 10.47191/ijcsrr/V7-i8-74
- Views 35
- Downloads 0
Determining of Well Drilling Sequence for Investment Analysis using Monte Carlo Simulation in Upstream Oil and Gas Company in East Kalimantan, Indonesia
Electrical construction company that experienced in making Well Head Control Panels (WHCP) has difficulties to decide acceptance of investment in WHCP contracts raised with their client in upstream oil and gas company. T...
Study of the Biodemographic Parameters of Two Genetically Distinct Strains of Callosobruchus maculatus from Two agroecological Zones of Senegal
During this study, we determined different biological parameters of Callosobruchus maculatus populations from two agro-ecological zones of Senegal (the ZSP and the ZVFS). To do this, strains of C. maculatus were collecte...
Adapting the Future VET Curriculum in Response to Emerging Challenges: A Model for Evaluating the Impact of AI Integration in VET for Armed Conflicts and Warfare
This article examines the profound implications and the transformative impact of artificial intelligence (AI) on vocational education and training (VET) within military contexts, underscoring the imperative for curriculu...
Teaching Mathematics with the Assistance of an AI Chatbot to Enhance Mathematical Thinking Skills for High School Students
The paper introduces the concepts of thinking and mathematical thinking, emphasizing the significance of mathematical thinking in problem-solving and the development of mathematical skills. It elaborates on the process o...
Predictive Analysis for Inventory Management of Coconut Warehouse (Case Study: Banio Lahewa)
Inventory management plays a pivotal role in the coconut farming business, directly influencing sales and income. An essential component of this management is warehousing, which not only affect revenue but also involves...