marcilioduarte/Credit-Worthiness-Risk-Classification

Credit Worthiness Risk Classification

End-to-end classification case study for credit risk prediction, including preprocessing, model selection, and app delivery.

Objective

Build and evaluate models to classify credit applicants by risk profile in a reproducible workflow.

What Is Included

• —data preparation and feature engineering steps;
• —supervised models: Logistic Regression, Decision Tree, Random Forest;
• —hyperparameter tuning with GridSearchCV;
• —model evaluation pipeline;
• —lightweight Gradio app for interactive inference (app.py).

Dataset

• —Local path: data/raw/german_credit.csv
• —Source: Kaggle - German Credit Dataset

Run Locally

pip install -r requirements.txt
python app.py

Project Metadata

• —sdk: gradio
• —sdk_version: 6.14.0
• —app_file: app.py
• —license: Apache-2.0