Big Data Analytics for Credit Risk Assessment

This project focuses on utilizing big data analytics in the banking sector to aid in decision-making processes, particularly in assessing the creditworthiness of loan applicants. By employing machine learning algorithms such as Decision Trees and Random Forests, the project aims to develop predictive models to identify trustworthy customers, thereby minimizing financial risks for banks. Python, PySpark, and Jupyter Notebook are utilized for data preprocessing, analysis, and model development. The project workflow involves data cleaning, exploration analysis, model building, and evaluation. Banks can make data-driven decisions to optimize their loan approval processes through this approach.