Predicting Customer Churn Rate Using Machine Learning

Customer churn prediction is essential for businesses aiming to retain their customer base in competitive markets. This study leverages machine learning to predict customer churn, employing models including Random Forest, Support Vector Classifier (SVC), Logistic Regression, Decision Tree, Gradient Boosting, and Long Short-Term Memory (LSTM) networks. Using a real-world dataset, we analyze customer behavior features and apply these models to classify churn outcomes. Model performance is evaluated exclusively through confusion matrix metrics, which provide insights into true positives, false positives, true negatives, and false negatives, thus enabling a direct assessment of each model's classification accuracy and misclassification tendencies. Results indicate that ensemble methods, especially Random Forest and Gradient Boosting, demonstrate strong performance in distinguishing churned from retained customers. The LSTM model, which considers sequential data, adds valuable temporal insight. This paper highlights the potential of various machine learning models for churn prediction, discussing model-specific advantages and limitations based on confusion matrix outcomes. Our findings underscore the role of machine learning in churn management and suggest potential areas for enhancing prediction accuracy through refined feature selection and deep learning approaches.