QRC-NET: Quantum Reservoir Computing for Credit Risk Modeling

A Python-based project combining Quantum Reservoir Computing (QRC) and Machine Learning to analyze and predict credit risks using the German Credit dataset.

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Table of Contents

1. Introduction
2. Features
3. Dataset
4. Installation
5. Usage
6. Project Structure
7. Quantum Computing in QRC
8. Results
9. Contributing
10. License

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Introduction

This project implements a hybrid approach using Quantum Reservoir Computing to enhance feature extraction for credit risk modeling. The primary goal is to achieve better recall in identifying high-risk credit cases.

Why QRC?

• Exploits quantum dynamics for efficient feature mapping.
• Enhances prediction capabilities for imbalanced datasets.

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Features

• Quantum Circuit Construction: Generates a customizable quantum reservoir.
• Credit Risk Prediction: Uses Random Forest for classification.
• Dataset Preprocessing: Handles categorical and numerical features seamlessly.
• Performance Metrics: Focuses on recall to identify risky credit cases.

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Dataset

The project uses the German Credit dataset:

• Source: UCI Machine Learning Repository.
• Description: 24 attributes (categorical and numerical) and a target column (Risk).
• File: Available in the dataset directory as GermanCredit.csv.

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Installation

Follow these steps to set up the project:

1. Clone the Repository

git clone https://github.com/your-username/QRC-NET.git
cd QRC-NET


python -m venv venv
source venv/bin/activate       # On macOS/Linux
venv\Scripts\activate          # On Windows


pip install -r requirements.txt

python main.py