Customer Churn Prediction Using Machine Learning Algorithms

This study explores the application of machine learning algorithms for predicting customer churn within the telecommunications sector, using a dataset containing key customer attributes like service usage indicators, and account details. Customer churn prediction seeks to determine the likelihood of customers discontinuing service, a crucial insight for retention strategies. Multiple predictive models were developed and compared, including logistic regression, decision trees, random forests, and gradient boosting classifiers, to assess their individual effectiveness and performance. In this analysis, extensive feature engineering was employed to enhance data representation, followed by targeted feature selection to refine model precision and reduce overfitting. Each model was evaluated using essential metrics: accuracy, precision, recall (sensitivity), and the area under the ROC curve, with comparative analysis highlighting the relative strengths of each approach. Results indicate that ensemble methods such as gradient boosting and random forests perform best in capturing complex churn patterns.