chore: Redact README file

.gitattributes

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 *tfevents* filter=lfs diff=lfs merge=lfs -text
 home_credit_dataset.csv filter=lfs diff=lfs merge=lfs -text
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+*.png filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 AlexRTC
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -9,8 +9,40 @@ license: mit
 short_description: ML Classification models applied to Home Credit Risk dataset
 ---
 
-Check out marimo at <https://github.com/marimo-team/marimo>
-Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>
+# 🏦 Home Credit Default Risk Prediction
+
+## Table of Contents
+
+1. [Project Description](#1-project-description)
+2. [Methodology & Key Features](#2-methodology--key-features)
+3. [Technology Stack](#3-technology-stack)
+4. [Dataset](#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](https://huggingface.co/spaces/iBrokeTheCode/Home_Credit_Default_Risk_Prediction) 👈️
+> - Check out the Jupyter Notebook for a detailed walkthrough of the project here: 👉️ [Jupyter Notebook](https://huggingface.co/spaces/iBrokeTheCode/Home_Credit_Default_Risk_Prediction/blob/main/tutorial_app.ipynb) 👈️
+
+![App](./public/app-demo.png)
+
+## 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
 
@@ -36,3 +68,9 @@ This project was built using the following technologies and libraries:
 
 - [Ruff](https://github.com/charliermarsh/ruff): A fast Python linter and code formatter.
 - [uv](https://github.com/astral-sh/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](https://www.kaggle.com/competitions/home-credit-default-risk/overview)