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Churn Prediction for Savings Bank Customers: A Machine Learning Approach · Study

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ArticleChurn Prediction for Savings Bank Customers: A Machine Learning Approach

Unknown author2020

Article2020

Churn Prediction for Savings Bank Customers: A Machine Learning Approach

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20 citations·0/0 files stored·Journal of Statistics Applications & Probability

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Customer churn and segmentation Customer Service Quality and Loyalty Financial Distress and Bankruptcy Prediction

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References

1921Measurement of Inequality of Incomes 2008Handling class imbalance in customer churn prediction 2010Marketing metrics: the definitive guide to measuring marketing performance 2008Customer churn prediction using improved balanced random forests 1998When Customers Are Members: Customer Retention in Paid Membership Contexts 2014Prediction of Customer Attrition of Commercial Banks based on SVM Model 2015A novel cost-sensitive framework for customer churn predictive modeling 2008Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example

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2022Enhancing Customer Churn Prediction in Digital Banking using Ensemble Modeling 2025Optimized churn prediction using ensemble-based feature selection via second-order cone programming

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2017An empirical comparison of techniques for the class imbalance problem in churn predictionarticle · 3 shared references 2014Prediction of Customer Attrition of Commercial Banks based on SVM Modelother · 3 shared references 2014Managing B2B customer churn, retention and profitabilityarticle · 3 shared references 2017Benchmarking sampling techniques for imbalance learning in churn predictionarticle · 3 shared references 2010Data Mining Applications in Customer Churn Managementother · 3 shared references 2020Churning of Bank Customers Using Supervised Learningchapter · 3 shared references

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Year2020

Typearticle

Published2020-10-31

VenueJournal of Statistics Applications & Probability

ISSN-L2090-8423

Languageen

Citations20

Files0 public / 0

ShelfCustomer churn and segmentation

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doi10.18576/jsap/090310

openalex_workW3112878960

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