🛡️ Fraud-Detection-SQL-Unsupervised - Make Sense of Financial Transactions

🚀 Getting Started

Welcome to the Fraud-Detection-SQL-Unsupervised project. This software helps you identify suspicious financial transactions easily. We use SQL and Python to analyze data and find unusual patterns, all without requiring advanced technical knowledge.

📥 Download & Install

To download the application, please visit our releases page below:

Follow these steps to get started:

1. Click the download link above.
2. Look for the latest release.
3. Download the appropriate file for your operating system.
4. Locate the downloaded file and double-click to run it.

🔧 System Requirements

Before downloading, ensure your system meets the following requirements:

• Operating System: Windows 10 or later, macOS 10.12 or later, or a Linux distribution.
• Memory: At least 4 GB of RAM.
• Storage: A minimum of 1 GB of free disk space.
• Connectivity: Internet connection for data analysis features.

🖥️ Features

This application includes:

• Detection of suspicious transactions in banking data.
• User-level behavioral features built using SQLite.
• Application of Isolation Forest for finding anomalies.
• Simple interface for easy data visualization of high-risk patterns.

📊 How It Works

The software utilizes SQL to manage and analyze financial data. Here’s a brief overview of its process:

1. Data Input: Load your financial transaction data in a supported format.
2. Processing: The software processes the data to create user behavior models.
3. Anomaly Detection: It applies the Isolation Forest algorithm to identify potential fraud.
4. Visualization: View the results in an easy-to-understand format.

📝 Usage Instructions

To use the software effectively, follow these instructions:

1. Open the application after installation.
2. Import your dataset by navigating to the "Import" menu.
3. Select the file you wish to analyze.
4. Enter any necessary parameters for detection. For example, specify the date range.
5. Click the "Analyze" button to start the process.
6. Wait for the analysis to complete. Review the visual results presented.

📈 Data Analysis Examples

You can use the software for various types of data analysis, such as:

• Monthly transaction reviews.
• Identifying unusual spending patterns.
• Assessing user behavior trends over time.

These examples help ensure you're using the application to its full potential and gaining meaningful insights from your data.

🛡️ Support & Resources

If you run into questions or need assistance, consider the following resources:

• Documentation: Comprehensive guides on how to navigate the software can be found on our GitHub Wiki.
• Frequently Asked Questions (FAQ): Check the FAQ section for common issues and solutions.
• Community Support: Join discussions and ask questions in our community forums linked on the repository page.

📤 Contribution Guidelines

If you wish to contribute to the project, you can do so by:

1. Forking the repository.
2. Making your changes.
3. Submitting a pull request.

We welcome suggestions and improvements that enhance the project.

🆕 Future Updates

Stay tuned for updates, including:

• Enhanced data processing speed.
• New features for advanced visualization.
• Support for additional data formats.

🔗 Additional Resources

For further reading on fraud detection, consider these topics:

• Anomaly Detection: Learn more about how this method identifies outliers.
• Data Analysis: Explore techniques to analyze financial data effectively.
• Machine Learning: Gain insight into how ML can enhance fraud detection.

📬 Get in Touch

For more information or inquiries, feel free to contact the project maintainers via the GitHub discussion page or direct messages.

Remember, you can find the software to download here: