A Two-Stage Ensemble Approach for Analysis of Optimizing Customer Churn with Lime Interpretability

Siddhant Ashwani · Aayushi Jeeban Tripathy · K Dinesh Kumar

11 citations0/0 files storedIEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Businesses must outbid suppliers to win over new clients in a very competitive industry. Since client retention directly affects a company’s revenue, it is a prominent topic for research. Businesses can prevent client attrition by proactively addressing it when it occurs. A two stage predictive ensemble method is proposed with Gaussian Naive Bayes, Perceptron, Gradient Boosting Classifier, Decision Tree, K-Nearest Neighbors, Logistic Regression, and XGBoost used in the first stage model classification, and then models like Adaboost, Bagging Classifier, Stacking, and Voting are used in the second stage to increase forecast accuracy for the top four models chosen in the first stage. The LIME Explainer algorithm also analyzes data instance adjustments and their effect on predictions, improving interpretability for “black box” models. This two-step approach addresses issues in crucial decision-making, by optimizing the final model’s predictive power and interpretability.

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2014A data-driven approach to predict the success of bank telemarketing 2023Analyzing Employee Attrition Using Explainable AI for Strategic HR Decision-Making 2023Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction 2023Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis 2023Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art 2022Predicting customers churning in banking industry: A machine learning approach 2022Research on telecom customer churn prediction based on ensemble learning 2023Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis

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