BY USING DATA ANALYSIS AND MACHINE LEARNING TECHNIQUES FOR BANK TURNOVER PREDICTION - Meliboev Azizjon (University Research Base, 2024)
Bank turnover prediction entails utilizing data analysis and machine learning methods to anticipate the probability of customers leaving or churning from a bank. This predictive modeling approach relies on historical customer data, including various attributes like transaction history, account details, demographics, and patterns of customer behavior. By employing advanced analytical techniques and machine learning algorithms, the objective is to forecast and address customer churn by identifying significant contributors to turnover. In this study, we conduct data preprocessing, feature engineering, and exploratory analysis to glean insights from the dataset. Furthermore, we employ machine learning algorithms such as Random Forests, to develop the predictive model. These models are then validated and refined using methods like hyperparameter tuning, along with evaluation metrics such as accuracy of 86%.