Credit-Risk-Analysis-Machine-Learning-Model

Credit Default Prediction using Responsible Machine Learning: A data analytics and machine learning project focused on predicting credit default risk while prioritizing fairness, interpretability, and ethical AI practices.

Credit Default Prediction – Responsible AI Approach

Project Overview

This project explores credit default prediction using machine learning, with a strong emphasis on fairness, interpretability, and responsible AI. Rather than optimizing accuracy alone, the goal was to build a model that makes reliable predictions without disadvantaging demographic groups.

Problem Statement

Financial institutions need accurate credit risk models, but poorly designed models can unintentionally introduce bias. This project evaluates how demographic features affect model performance and demonstrates how fairness-aware design improves trust without sacrificing accuracy.

Dataset

The dataset contains customer financial behavior and demographic attributes, including: Credit payment history Billing and repayment behavior Demographic variables (gender, education, marital status)

Methodology

Data cleaning and preprocessing Exploratory Data Analysis (EDA) Feature engineering Logistic Regression modeling Bias analysis across demographic groups Feature re-evaluation and removal of biased attributes Model re-training and performance comparison

Key Results

Accuracy: 0.705 ROC-AUC: 0.75 Removing demographic variables preserved performance while improving: Fairness Interpretability Compliance with ethical credit modeling principles

Tools & Technologies

Python Pandas, NumPy Scikit-learn Matplotlib & Seaborn Jupyter Notebook

Key Takeaway

Responsible AI is not about limiting performance — it’s about building models that are fair, transparent, and trustworthy.