Customer Churn Prediction for Telecom Industry

Customer churn is a critical challenge in the telecom industry, as losing existing customers directly impacts company

revenue and market competitiveness. Retaining customers is significantly more cost-effective than acquiring new ones;

therefore, identifying customers who are likely to discontinue services has become an important task for telecom providers. This

project proposes a machine learning–based customer churn prediction system that analyses historical telecom customer data to

detect patterns associated with churn behaviour. The proposed framework utilizes customer attributes such as tenure, monthly

charges, service subscriptions, and customer support interactions to train predictive models. Data preprocessing and feature

engineering techniques are applied to improve model performance. Multiple machine learning algorithms, including Random

Forest, Support Vector Machine (SVM), and Logistic Regression, are implemented and evaluated to determine the most effective

approach for churn prediction. Experimental results demonstrate that the Random Forest model achieves superior prediction

accuracy compared to other algorithms, highlighting its ability to capture complex relationships within telecom customer data.

The proposed system enables telecom companies to proactively identify high-risk customers and implement targeted retention

strategies such as personalized offers and improved customer support. Overall, the integration of machine learning and

predictive analytics provides a data-driven solution for reducing customer churn and improving long-term customer retention in

the telecom industry.