Comparative Analysis of Machine Learning Models for Telecom Customer Churn Prediction

View Full Paper

Ask AI

Bookmark

Authors

Discussion

This analysis evaluates customer churn prediction across machine learning models, highlighting accuracy and interpretability.

Machine learning models achieved 81.1% accuracy in predicting customer churn, showing strong performance.
Logistic Regression offered the best interpretability and precision, aiding in customer retention strategies.
Predictive modelling techniques were evaluated using the Telco Customer Churn dataset from Kaggle, enhancing empirical insights.
Results suggest that telecom operators can effectively leverage data-driven methods to minimize churn and improve profitability.

Shixuan Wei (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

This analysis evaluates customer churn prediction across machine learning models, highlighting accuracy and interpretability.

Machine learning models achieved 81.1% accuracy in predicting customer churn, showing strong performance.
Logistic Regression offered the best interpretability and precision, aiding in customer retention strategies.
Predictive modelling techniques were evaluated using the Telco Customer Churn dataset from Kaggle, enhancing empirical insights.
Results suggest that telecom operators can effectively leverage data-driven methods to minimize churn and improve profitability.

Shixuan Wei (2025) studied this question.

View Full Paper

Ask AI

Bookmark