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October 12, 2025International Scientific Journal of Engineering and Management

Customer Churn Predicted Using Gradient Boosted Decision Tree

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Authors

STSayali TanpurePPPooja Patil

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Overview

Analysis shows gradient boosted decision trees improve prediction accuracy for customer churn, suggesting targeted retention strategies.

Key Points

  • Gradient boosted decision trees efficiently predict customer churn, improving company retention efforts.
  • The model demonstrated high accuracy and effectively handled missing information, crucial for reliable predictions.
  • Metrics like precision, recall, and AUC-ROC confirmed the model's capability to identify at-risk customers.
  • Machine learning techniques applied to historical customer data can significantly enhance revenue stability for businesses.

Cite This Study

Tanpure et al. (2025) studied this question.

synapsesocial.com/papers/68ec1be02b8fa9b2b78ad332https://doi.org/10.55041/isjem05088
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comparing Traditional Machine Learning and Advanced Gradient Boosting Techniques in Customer Churn Prediction: A Telecom Industry Case Study2025
  2. 2Data-Driven Decision-Making: Accurate Customer Churn Prediction with Cat-Boost2024 · 7 citations
  3. 3Customer Churn Prediction Using Machine Learning Techniques2026
  4. 4Enhancing Customer Analytics: A Comprehensive Framework for Effective Churn Prediction2024 · 1 citations
  5. 5A Novel Methodology for Customer Attrition Prediction2024