Churn Prediction in Business Using Logistic Regression and Logit Boost

Customer retention is essential in any industry. However, in the business sector, retaining existing customers has become even more critical due to intensified market competition. To proactively determine the likelihood of customer churn and retain customers, companies need to analyze customer behavior and purchasing patterns. In this study, this paper used the publicly available Churn data dataset from Kaggle and employed two machine learning techniques, namely logistic regression and logit boost, to predict the likelihood of customer churn. The experiments were conducted using the scikit-learn library in a Python environment, and the results were presented using different evaluation metrics such as accuracy, recall, and F1 score. These techniques can help companies identify high value customers who are likely to churn and develop targeted marketing strategies to retain them.