Credit Risk Modeling using Machine Learning

📌 Overview

This project builds an end-to-end machine learning pipeline to predict credit risk (loan default) using historical customer data.

The objective is to help financial institutions identify high-risk applicants and reduce loan default rates.

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🧠 Machine Learning Pipeline

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Training (Logistic Regression, Random Forest, XGBoost)
• Model Evaluation (ROC-AUC, Precision, Recall, Confusion Matrix)

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📊 Model Evaluation

• ROC-AUC Score
• Precision-Recall Trade-off
• Confusion Matrix visualization
• Class imbalance handled using SMOTE / class weighting

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🛠 Tech Stack

• Python
• NumPy, Pandas
• Scikit-learn
• XGBoost
• Matplotlib, Seaborn