Enhancing Telecom Customer Churn Prediction Using Machine Learning and LSTM for Time-Series Analysis

The telecom industry faces customer attrition as an essential problem because keeping present customers proves more economical than finding new clients. Churn prediction methods built with traditional models depend mainly on fixed customer properties together with service history data while neglecting active modifications in user conduct. The study evaluates machine learning algorithm prediction of customer churn by implementing K-Nearest Neighbors, Support Vector Classifier, Random Forest, Logistic Regression, Decision Trees, AdaBoost, Gradient Boosting, a Voting Classifier, and a Long Short-Term Memory (LSTM) network. The LSTM model surpasses standard prediction methods by processing time-based patterns which includes customer data activity combined with service pattern usage and payment records. Experimental data shows the LSTM model surpasses standard classifiers by producing testing accuracy results of 89% and precision results of 0.86 together with recall results of 0.85.