Finance and Risk Analytics Project: Predicting credit default risk using machine learning models (Logistic Regression, Random Forest) and assessing stock market risk through historical returns and volatility analysis to guide financial risk management and investment strategies.
Identifying the creditworthiness of loan applicant and the risk associated with them using exploratory data analysis.
Supervised Learning and Classification Models
Machine Learning-Based Credit Scoring System (MLCSS) is a machine learning algorithm designed to evaluate and score the creditworthiness of individuals.
Creditworthiness_Calculator is a simple Matlab program designed to evaluate the creditworthiness of individuals based on Fuzzy Logic principles.
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