musagithub1/credit_scoring_project
Machine learning project for predicting credit risk using Logistic Regression, Decision Tree, and Random Forest.
This project helps credit analysts and risk managers evaluate loan applications more accurately. It takes raw credit data, processes it, and then uses various machine learning models to predict whether an applicant is likely to default on a loan. The output includes detailed performance reports for each model, allowing you to choose the best predictor for your needs.
No commits in the last 6 months.
Use this if you need to automate and improve the accuracy of credit risk assessment in your financial institution by leveraging advanced analytics.
Not ideal if you require real-time, ultra-low latency scoring for high-frequency trading or need to comply with highly specialized, non-standard regulatory models without any customization.
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Language
Python
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Last pushed
Aug 21, 2025
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