Customer Churn Prediction in Telecommunication, Medical Industry Using Machine Learning Classification Models

Predicting the number of customers leaving a company is important for companies operating in various sectors. Businesses can now employ predictive models to identify clients at risk of churning, thanks to the growing availability of data and developments in methodologies for machine learning. This abstract offers an overview of predicting customer churn and highlights the most important techniques and strategies used in this subject. The purpose of developing models for customer churn prediction is to properly estimate which customers are likely to end their connection with a company or stop utilising the goods or services offered by that firm. Businesses can execute targeted retention measures to reduce customer turnover and increase customer loyalty when customers likely to leave are identified in advance. Several different machine learning classification models, including Random Forest, Logistic Regression, K Nearest Neighbor (KNN), AdaBoost, Decision Tree, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), have been used in the process of predicting customer turnover. To forecast future churn, these models uncover relevant patterns from previous customer data, such as demographics, transaction history, customer interactions, and use trends. The unique features of the industry that are being taken into account should guide the selection of the most appropriate model. Variations in data availability, client behaviour, and other industry-specific issues may need individualised strategies for various businesses, such as those in the medical field, the banking industry, the online retail sector, and the telecommunications sector. When evaluating the efficacy of churn prediction models, standard performance evaluation criteria include accuracy, precision, recall, and F1-score. With these measures, companies can evaluate how successful their models are at accurately classifying customers as either churners or non-churners.