Customer Churn Prediction Analysis for Retention Using Ensemble Learning

In the 21st century, there is a handful of information on the Internet. It is easy for customers to switch to a competing company in any sector. It is important for a company to retain its existing customers rather than secure new ones. So, customer retention becomes an essential part of an organisation’s business. Customer churn prediction is a type of data analysis based on the historical data of any organization. It helps to predict whether the customers are going to leave the organisation or not in advance. Also, it helps the organisation predict what customers are expecting from them and where they are lacking. There are a wide range of algorithms used for churn prediction. However, there is no agreement on choosing the best one. Therefore, this project presents customer churn prediction using the ensemble method in the telecommunications sector. The ensemble approach amalgamates predictions from base classifiers such as decision trees, random forests, gradient-boosting machines, and support vector machines. By studying the strengths of these models, ensemble techniques like bagging, boosting, and stacking mitigate individual model biases and variance, leading to improved predictive performance. We are using the XGBoost and Random Forest methods to predict the churn. It is a type of ensemble method. The ensemble method is used to increase accuracy by combining multiple models rather than using a single model. From the result, we have observed that the proposed model, XGBoost and Random Forest, attains 99% accuracy. Overall, the project adds significant value by enabling proactive churn management, customer-centric strategies, and sustainable business growth.