A newer version of the Gradio SDK is available:
6.5.1
title: Credit Worthiness Risk Classification
emoji: 🏆
colorFrom: blue
colorTo: pink
sdk: gradio
sdk_version: 3.27.0
app_file: app.py
pinned: false
license: apache-2.0
This is a case study about creditworthiness classification where I did the whole process of building a creditworthiness classification model and app, from the data cleansing part until the deployment of the model in an application simulated for the banks managers. To achieve this goal, I analyzed and prepared the dataset for machine learning models. The applied models are: Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, which are available in Python's sklearn library. To optimize the workflow and model results, I applied a personalized pipeline for model application and GridSearchCV for parameter optimization. The app development was made using gradio app.
The data is uploaded in path: german_credit_risk/data/raw, but it was first obtained from Kaggle and can be obtained HERE.
I used a previous work from Pennsylvania State University as a reference in many parts of the code, you can find it HERE. Also, as this is a case study, the code steps are commented in english.
It is worth mentioning that the feature selection process was carefully performed according to the regulations of the Central Bank of Brazil, as I'm Brazilian.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference