SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
Data-Centric AI to Improve Churn Prediction with Synthetic Data
4 references·1 citing·20 second hop
Data-Centric AI to Improve Churn Prediction with Synthetic Data (2023)Data-Centric AI to Improve Churn Predicti…A survey on machine learning methods for churn prediction (2022)A survey on machine learning …What can Data-Centric AI Learn from Data and ML Engineering? (2021)What can Data-Centric AI Lear…Churn Prediction using Ensemble Learning (2020)Churn Prediction using Ensemb…synthpop : Bespoke Creation of Synthetic Data in R (2016)synthpop : Bespoke Creation o…A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities (2024)A Survey on the Use of Synthe…Credit card churn forecasting by logistic regression and decision tree (2011)A comparison of machine learning techniques for customer churn prediction (2015)A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees (2018)A Comparison of Machine Learning Algorithms for Customer Churn Prediction (2023)Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner (2023)AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector (2025)The Antecedents and Consequences of Customer Satisfaction for Firms (1993)Determinants of Long-Term Orientation in Buyer-Seller Relationships (1994)The Behavioral Consequences of Service Quality (1996)An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes (2023)A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry (2025)Customer Churn Prediction: A Systematic Review of Recent Advances, Trends, and Challenges in Machine Learning and Deep Learning (2025)1,500 scientists lift the lid on reproducibility (2016)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)A Survey on Bias and Fairness in Machine Learning (2021)Using 26,000 diary entries to show ovulatory changes in sexual desire and behavior. (2018)The Role of Personality in COVID-19-Related Perceptions, Evaluations, and Behaviors: Findings Across Five Samples, Nine Traits, and 17 Criteria (2021)REFORMS: Consensus-based Recommendations for Machine-learning-based Science (2024)The Principles of Data-Centric AI (DCAI) (2022)From Concept to Implementation: The Data-Centric Development Process for AI in Industry (2023)
this work references cited by second hop

References

  • 2016synthpop : Bespoke Creation of Synthetic Data in R408 cited
  • 2022A survey on machine learning methods for churn prediction76 cited
  • 2021What can Data-Centric AI Learn from Data and ML Engineering?35 cited
  • 2020Churn Prediction using Ensemble Learning30 cited

Cited by

  • 2024A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities12 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work