Loan Default Prediction & Time Series Forecasting

This comprehensive course, Loan Default Prediction & Time Series Forecasting, is designed for professionals and learners eager to master predictive modeling within the banking and finance domain. By combining machine learning techniques with real-world financial data, you’ll develop practical skills to forecast and prevent loan defaults—a crucial aspect of risk management for any financial institution.

The course covers two main areas. First, you’ll delve into Loan Default Prediction, learning to apply machine learning models like XGBoost and Artificial Neural Networks (ANN) to identify high-risk borrowers. We’ll take you through each step, from understanding the unique banking dataset to training and tuning predictive models. By mastering these techniques, you’ll gain insights into critical financial factors and learn to pinpoint borrowers more likely to default.

The second part addresses Time Series Forecasting for loan defaults, where you’ll use the Prophet model to predict future trends. This is invaluable for financial planning, allowing institutions to proactively manage risk based on anticipated default rates.

Our course includes hands-on experience in building a Data Architecture Model using tools like Apache NiFi and MySQL to simulate a real-world banking environment. From data extraction and transformation to loading into data warehouses, you’ll acquire the end-to-end skills needed for managing and analyzing large datasets in finance.

This course is ideal for data scientists, financial analysts, data engineers, and anyone interested in financial data modeling. Join us to gain a competitive edge in predictive analytics and drive impactful insights within the banking sector!