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title: Credit Worthiness Risk Classification
emoji: "📈"
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: "6.14.0"
python_version: "3.10"
app_file: app.py
pinned: false
---
# 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](https://www.kaggle.com/datasets/mpwolke/cusersmarildownloadsgermancsv)
## Run Locally
```bash
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
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