What question did this study set out to answer?

This research aims to predict customer churn using various machine learning techniques to improve customer retention.

May 4, 2026Open Access

Customer Churn Prediction Using Machine Learning Techniques

Puntos clave

This research aims to predict customer churn using various machine learning techniques to improve customer retention.
Applied machine learning techniques such as Logistic Regression, Decision Tree, Random Forest, and XGBoost.
Used publicly available datasets for model training and evaluation.
Compared performance of ensemble-based methods against traditional algorithms.
Ensemble methods like Random Forest and XGBoost outperformed traditional algorithms.
Achieved higher accuracy and better recall rates compared to Logistic Regression and Decision Tree.

Resumen

Customer churn is one of the major challenges faced by organizations, especially in competitive industries such as telecommunications, banking, and e-commerce. Predicting customer churn helps companies take proactive steps to retain valuable customers. This research focuses on predicting customer churn using machine learning techniques including Logistic Regression, Decision Tree, Random Forest, and XGBoost. The model is trained and evaluated using publicly available datasets. Experimental results show that ensemble-based approaches like Random Forest and XGBoost outperform traditional algorithms, achieving higher accuracy and better recall rates.

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Cite This Study

Verma et al. (Thu,) studied this question.

synapsesocial.com/papers/69f837423ed186a73998168b https://doi.org/https://doi.org/10.5281/zenodo.19978213

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