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Enhancing Customer Churn Prediction in the Banking Sector through Hybrid Segmented Models with Model-Agnostic Interpretability Techniques
6 references·0 citing·25 second hop
Enhancing Customer Churn Prediction in the Banking Sector through Hybrid Segmented Models with Model-Agnostic Interpretability Techniques (2024)Enhancing Customer Churn Prediction in th…Customer churn prediction in imbalanced datasets with resampling methods: A comparative study (2024)Customer churn prediction in …Analysis of customer churn prediction using machine learning and deep learning algorithms (2022)Analysis of customer churn pr…Optimized Deep Neural Network Based Predictive Model for Customer Attrition Analysis in the Banking Sector (2019)Optimized Deep Neural Network…Cross-linguistic Transfer (from L1 to L2, L2 to L1, and L2 to L3) of Reading Strategies in a Multicompetent Mind (2013)Cross-linguistic Transfer (fr…Combining schema and instance information for integrating heterogeneous data sources (2006)Combining schema and instance…The Use of L1 Reading Strategies in L2 Reading (1990)The Use of L1 Reading Strateg…A Theory for Record Linkage (1969)Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem (1998)Learning object identification rules for information integration (2001)INTERLANGUAGE (1972)Language Transfer (1989)Teaching by Principles: An Interactive Approach to Language Pedagogy (2001)Individual differences in working memory and reading (1980)Handbook of research on teaching (1986)Handbook of reading research (1991)Second language reading research: Problems and possibilities (1994)Strategies for Language Learning and for Language Use: Revising the Theoretical Framework (2006)Reading and Language Learning: Crosslinguistic Constraints on Second Language Reading Development (2007)Developing a prediction model for customer churn from electronic banking services using data mining (2016)Predicting Customer Churn in Banking Industry using Neural Networks (2016)An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing (2017)Customer churn prediction in telecom using machine learning in big data platform (2019)Churn Prediction in Telecommunication using Logistic Regression and Logit Boost (2020)Customer churn prediction system: a machine learning approach (2021)Predicting Consumption Intention of Consumer Relationship Management Users Using Deep Learning Techniques: A Review (2022)Thirteen Ways to Look at the Correlation Coefficient (1988)Customer churn prediction using improved balanced random forests (2008)Handling class imbalance in customer churn prediction (2008)Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning (2024)Intensified Customer Churn Prediction: Connectivity with Weighted Multi-Layer Perceptron and Enhanced Multipath Back Propagation (2024)Mitigating class imbalance in churn prediction with ensemble methods and SMOTE (2025)
this work references cited by second hop

References

  • 1990The Use of L1 Reading Strategies in L2 Reading130 cited
  • 2006Combining schema and instance information for integrating heterogeneous data sources47 cited
  • 2024Customer churn prediction in imbalanced datasets with resampling methods: A comparative study44 cited
  • 2013Cross-linguistic Transfer (from L1 to L2, L2 to L1, and L2 to L3) of Reading Strategies in a Multicompetent Mind23 cited
  • 2022Analysis of customer churn prediction using machine learning and deep learning algorithms15 cited
  • 2019Optimized Deep Neural Network Based Predictive Model for Customer Attrition Analysis in the Banking Sector6 cited

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