A Novel Sustainable Regression Modeling to Recognize the Likelihood of Customer Churn Management Forecasting

Customer churn refers to a situation where customers end their association with a business, which poses a considerable challenge for various industries including telecommunication and e-commerce. Predictive modeling techniques offer a proactive approach to address this issue by distinguishing customers who are at a risk of churn, enabling businesses to deploy focused approaches to sustain the customers at the risk of churn. We have undertaken a comparable analysis of some machine learning techniques to predict the customer churn in this research paper. The study commences with an in-depth examination of several machine learning algorithms, such as decision trees, support vector machines, random forest, and logistic regression. Each algorithm’s suitability for customer churn prediction is evaluated based on its ability to handle imbalanced datasets, interpretability, computational efficiency, and predictive performance. Once the machine learning model is made (using the training data), we need an unseen dataset known as the testing data, to assess the model’s performance. The evaluation will be done by using a logistic regression model, which falls under supervised machine learning technique. Supervised technique uses a set of independent variables to predict the categorical dependent variable. Logistic regression model is adept at predicting categorical dependent variables using a set of independent variables. It offers simplicity and interpretability while effectively capturing the correlation between input features and the likelihood of the churn. Through comprehensive experimentation and evaluation on real-world datasets, we demonstrate the efficacy of the proposed approach in accurately predicting customer churn.