📊 Aim: Predict the likelihood of borrower defaulting on a loan
📚 Dataset: Borrowers' information (credit score, income, loan amount, employment history, etc.)
🎯 Task: Binary classification (classify as "will default" or "will not default")
⚖️ Evaluation metrics: Accuracy, precision, recall, F1-score
📈 Models: Logistic Model, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Decision Tree, K-Nearest Neighbor
💰 Excel sheet: Calculates bank profits using the predictive models
🎯 Goal: Develop accurate and reliable loan default prediction model
💼 Benefits: Informed loan approval decisions, effective risk management