# Credit Risk Analysis

> XGBoost predictive model for financial risk classification

URL: https://www.matthewswong.com/id/projects/credit-risk-analysis/

A comprehensive credit risk classification project built during a virtual internship with id/x partners x Rakamin Academy. Used XGBoost and advanced feature engineering to predict credit default risk, with full exploratory data analysis and stakeholder-ready visualizations.

## How it works

- Exploratory data analysis (EDA) to identify patterns and outliers
- Feature engineering and missing value treatment
- Baseline model comparison across multiple algorithms
- XGBoost model tuning with cross-validation
- SHAP-based feature importance analysis
- Presentation deck with data storytelling for stakeholders

## Features

- Full EDA with statistical analysis
- XGBoost with hyperparameter tuning
- SHAP feature importance visualization
- Stakeholder presentation with insights

## Impact

Achieved high classification accuracy for credit default prediction. The project demonstrated production-ready data science skills and earned completion recognition from id/x partners.

- Category: Mobile & Data
- Stack: Python, XGBoost, Pandas, Matplotlib, SHAP, Scikit-learn

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- HTML version: https://www.matthewswong.com/id/projects/credit-risk-analysis/
- English: https://www.matthewswong.com/en/projects/credit-risk-analysis/
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- API description (OpenAPI): https://www.matthewswong.com/openapi.json