To Design and Implement Application for Bank Customer Churning Rate Prediction and Analysis using Machine Learning Algorithm

The banking industry faces a constant challenge in retaining customers and mitigating customer churn. To address this issue, this study presents the design and implementation of an innovative application aimed at predicting and analyzing bank customer churning rates. Leveraging state-of-the-art machine learning algorithms, this application offers a comprehensive solution for identifying potential churners and devising effective retention strategies. The first phase of the project involves data collection and preprocessing, where historical customer data, including transaction history, demographics, and behavior patterns, is gathered and cleaned for analysis. Subsequently, various machine learning algorithms, including but not limited to logistic regression, random forests, and gradient boosting, are employed to build predictive models. These models are trained on the historical data and are fine-tuned to achieve optimal predictive accuracy.