چکیده مقاله
With increasing competition among banks and the rising expectations of Generation Z customers for digital services, predicting customer churn has become crucial On the other hand, machine learning models are evolving, and the Random Forest algorithm stands out for its effectiveness in this area In our research, we preprocessed and visualized data to enhance the quality of the input data and to gain initial useful insights We carefully selected key features to speed up the algorithm and used strategies to balance the imbalanced data Implementing the Random Forest model, we achieved an accuracy of about 96%, successfully identifying both loyal customers and potential churners Finally, we optimized the model's performance with Grid Search CV and Randomized Search CV, enhancing its effectiveness
کلیدواژهها
نویسندگان
Behzad Yaghoobi
Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Erfan Hassannayebi
Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Mohammad Hossein Shahmoradi
Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
شیوه ارجاع
Yaghoobi, Behzad and Hassannayebi, Erfan and Shahmoradi, Mohammad Hossein,1403,Enhanced Customer Churn Prediction in the Banking Sector Using Random Forest,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
مجموعه مقالات دهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها28 شهریور 1403 · مشهد