Improving customer churn prediction using machine learning
Introduction This thesis explores the topic of customer churn prediction. It is a significant challenge faced by businesses across various industries like telecommunications and e-commerce.
Jagini, Achyut
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Churn Prediction of Bank Customers
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Customer Churn Prediction Analysis
Mrugendra Rahevar, Fenil D., Jainam D.
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An integrated explainable AI framework with counterfactual optimization for decision support. [PDF]
Jahan N, Maua J, Rozario U, Shoaib HA.
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Online game player churn prediction based on multiplex social influence through network embedding. [PDF]
Guo M, Liu Y.
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A new perspective on data leakage prevention and adaptive attention in telecom churn prediction. [PDF]
Le DT, Nguyen MT.
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Classification of customer retention using hybrid SVC-SDNN to enhance customer relationship management. [PDF]
Ishaq M +5 more
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Neural network approach enhancing churn prediction with categorical encoding and standard scaling. [PDF]
Bhattacharjee B +7 more
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Combining predictive accuracy and interpretability: a data-driven approach to telecom churn analysis. [PDF]
Hooda P +4 more
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Integrating Business Intelligence and CRM Systems With a Machine Learning Approach for Predictive Customer Retention in E-Commerce. [PDF]
Zeinali M, Ramezani Asli L, Khalili MA.
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