Project Credit Risk Analysis 📊

Welcome to the Project Credit Risk Analysis repository! This project focuses on analyzing the German Credit Data from 1994. The goal is to assess credit risk using various classification techniques. You can find the latest releases of the project here.

Table of Contents

1. Introduction
2. Dataset
3. Methodology
4. Key Metrics
5. Installation
6. Usage
7. Contributing
8. License
9. Contact

Introduction

Credit risk assessment is crucial for financial institutions. It helps in determining the likelihood of a borrower defaulting on a loan. This project utilizes a dataset from 1994 that contains information about various applicants. By applying classification algorithms, we can predict the creditworthiness of individuals.

Dataset

The dataset used in this project consists of 1,000 samples, each containing 20 features. The features include:

• Age
• Gender
• Credit amount
• Duration of credit
• Employment status
• Previous credit history

This data allows us to build a model that can predict whether an applicant is a good or bad credit risk.

Methodology

We employ several classification algorithms to analyze the data, including:

• Decision Trees
• Random Forests
• Logistic Regression
• Support Vector Machines (SVM)

Each algorithm is evaluated based on accuracy, precision, recall, and the area under the ROC curve (AUC). We also use confusion matrices to visualize the performance of our models.

Key Metrics

• Accuracy: Measures the proportion of true results among the total number of cases examined.
• Precision: Indicates the number of true positives divided by the sum of true positives and false positives.
• Recall: Reflects the number of true positives divided by the sum of true positives and false negatives.
• AUC: Represents the degree of separability achieved by the model.

These metrics help us understand how well our models perform in predicting credit risk.

Installation

To get started with this project, clone the repository and install the required libraries.

git clone https://github.com/OfficialYapper/Project-Credit-Risk-Analysis.git
cd Project-Credit-Risk-Analysis
pip install -r requirements.txt

Make sure you have Python 3.x installed on your machine.

Usage

After installation, you can run the analysis script. The script will generate results based on the dataset provided.

python credit_risk_analysis.py

You can find the output files in the results directory. For the latest updates and downloads, visit the Releases section.

Contributing

We welcome contributions to improve this project. If you want to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions or suggestions, feel free to reach out:

• Author: Your Name
• Email: your.email@example.com
• GitHub: OfficialYapper

Thank you for your interest in the Project Credit Risk Analysis! For more details, visit the Releases section to download and execute the files.

Happy coding!