title	emoji	colorFrom	colorTo	sdk	pinned	license	short_description
Home Credit Default Risk Prediction	🍃	indigo	purple	docker	true	mit	ML Classification models applied to Home Credit Risk dataset

🏦 Home Credit Default Risk Prediction

Table of Contents

1. Project Description
2. Methodology & Key Features
3. Technology Stack
4. Dataset

1. Project Description

This project focuses on building a machine learning pipeline to predict a client's ability to repay a loan. It is a binary classification task that uses a real-world financial dataset to identify clients who may face payment difficulties.

The project goes beyond a standard model by including a practical application that:

• Preprocesses and cleans the dataset for model training.
• Trains a machine learning model to predict loan repayment risk.
• Deploys an interactive predictor app using Marimo, hosted on Hugging Face Spaces.
• Allows users to make predictions by providing the top 10 most influential features.

This work showcases a complete end-to-end workflow, transforming raw data into a functional, user-friendly tool for risk assessment.

Important

• Check out the deployed app here: 👉️ Home Credit Default Risk Prediction App 👈️
• Check out the Jupyter Notebook for a detailed walkthrough of the project here: 👉️ Jupyter Notebook 👈️

2. Methodology & Key Features

• Model Selection: Four different models were trained and evaluated, with LightGBM selected as the final model due to its superior performance, achieving a ROC AUC score of 0.751 on the test set.
• Automated Preprocessing: The data preprocessing pipeline handles common tasks such as feature scaling and categorical encoding, ensuring the model receives clean and formatted data.
• Interactive Predictor: An application built with Marimo allows users to interact with the trained model directly. It uses the top 10 most important features—identified from the final LightGBM model—to generate real-time predictions.

3. Technology Stack

This project was built using the following technologies and libraries:

Dashboard & Hosting:

• Marimo: A Python library for building interactive dashboards.
• Hugging Face Spaces: Used for hosting and sharing the interactive dashboard.

Data Analysis & Visualization:

• Pandas: For data manipulation and analysis.
• Matplotlib: For creating static visualizations.
• Seaborn: For creating statistical graphics.

Modeling & Training:

• Scikit-Learn: For machine learning tasks such as preprocessing, feature engineering, and model training.
• LightGBM: It is a gradient boosting framework that uses tree based learning algorithms.

Development Tools:

• Ruff: A fast Python linter and code formatter.
• uv: A fast Python package installer and resolver.

4. Dataset

This project utilizes the Home Credit Default Risk from Kaggle, a public dataset containing details on over 246,000 of individuals who have made payments on their loans.

• Source: Kaggle Dataset