Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study
Author:
Publisher
The Science and Information Organization
Subject
General Computer Science
Link
http://thesai.org/Downloads/Volume9No11/Paper_96-Predicting_Potential_Banking_Customer.pdf
Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Big Data-Driven Hybrid Model for Enhancing Streaming Service Customer Retention Through Churn Prediction Integrated With Explainable AI;IEEE Access;2024
2. Decision Tree with Genetic Algorithm for Bank Customer Churn Prediction;2023 IEEE 21st Student Conference on Research and Development (SCOReD);2023-12-13
3. FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports;The Third International Conference on Artificial Intelligence and Machine Learning Systems;2023-10-25
4. Mining Customer Churns for Banking Industry using K-means and Multi-layer Perceptron;2023 IEEE Conference on Computer Applications (ICCA);2023-02-27
5. Bank Customer Churn Prediction Based on Correlation Analysis and Multiple Linear Regression;Applied Economics and Policy Studies;2023
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