This repository contains an end-to-end data analytics project for credit risk analysis, focusing on loan default patterns and data quality assessment. It utilizes Python, SQL, machine learning, Power BI, and Excel to transform raw credit data into validated insights.
From the README
Credit Risk & Data Quality Analytics Platform Overview An end-to-end data analytics project focused on credit risk analysis, loan default patterns, and data quality assessment. The project transforms raw credit data into validated insights using Python, SQL, Machine Learning, Power BI, and Excel. Objectives • Analyze credit risk and loan default patterns • Clean and validate credit data • Perform SQL-based risk analysis • Build machine learning models for default prediction • Develop interactive Power BI dashboards • Implement a data quality and controls framework • Create a reproducible analytics workflow Tools & Technologies • Python — Pandas, NumPy, Matplotlib, Seaborn • SQL —...
Skills
- Jupyter Notebook
