Customer Churn Prediction in the Telecom Industry Using Machine Learning

Abstract

Customer churn means customers leaving a company or stopping the use of its services. This project focuses on identifying such customers at an early stage so that businesses can take necessary actions. It is very useful in industries like telecom, banking, and online platforms where customer retention is important. In this project, machine-learning techniques are used to analyse customer data such as usage details, billing information, and customer behaviour. First, the data is cleaned by handling missing values and converting it into a proper format; then, important features that influence customer decisions are selected. After preprocessing, the dataset is divided into training and testing data, and machine-learning algorithms such as Logistic Regression and Decision Tree are applied to build the prediction model. The model is trained using past customer data and learns patterns from it; once training is complete, it predicts whether a customer is likely to leave or continue using the service. The performance of the model is evaluated using accuracy and other metrics. This project helps companies understand customer behaviour and identify the main reasons for churn, so that businesses can take preventive steps such as providing better services, offering discounts, or improving customer support. The system was validated through ten functional and validation test cases that all passed. Overall, this system helps increase customer satisfaction, reduce customer loss, and improve business profit.