Built a machine learning system to predict loan approval using applicant demographics, credit history, and financial data. Performed EDA, feature engineering, categorical encoding, and feature scaling. Trained and compared five models: Logistic Regression, Decision Tree, Random Forest, KNN, and Gradient Boosting, evaluated via accuracy, precision, recall, F1-score, and confusion matrices. Integrated Bayesian inference with a prior-probability knowledge base, benchmarked against ML models. Included distribution plots, correlation heatmaps, and F1-score threshold optimization.
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Built a machine learning system to predict loan approval using applicant demographics, credit history, and financial data. Performed EDA, feature engineering...
