Machine Learning Based Telecom-Customer Churn Prediction

Abstract

In the highly competitive telecom sector, maintaining client loyalty is a critical obstacle to longterm profitability and expansion. This research uses the Random Forest and Logistic Regression algorithms to give a detailed investigation of customer attrition prediction specifically for the telecom industry. Building a strong predictive model to identify possible churners will enable telecom businesses to implement focused customer loyalty campaigns. Our methodology incorporates a wide range of telecom-specific characteristics, such as call trends, usage information, and customer support exchanges. By utilizing the Random Forest and Logistic Regression methods, we may increase the forecasting accuracy by exploring the complex patterns that indicate customer churn. Carefully considered feature engineering techniques are used to improve the model's capacity to capture subtleties specific to the telecom . Our approach is validated using a real-world telecom dataset that includes a range of customer categories. Performance metrics such as F1 score, recall, accuracy, and precision show how well our model forecasts customer attrition in the dynamic telecom market. Keywords:- Customer Churn, Machine Learning, Telecom Sector, Performance Metrics