A comprehensive project utilizing data science and machine learning to detect credit card fraud.
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This project aims to utilize data science and machine learning techniques to identify instances of credit card fraud. By examining various features and patterns within the dataset, the goal is to accurately distinguish fraudulent transactions from genuine ones.

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

To get a local copy up and running, follow these simple steps.

This is an example of how to list things you need to use the software and how to install them.

• Python 3.6+
• Jupyter Notebook
• etc.

1. Clone the repo

   git clone https://github.com/ThecoderPinar/Credit-Card-Fraud-Detection-Project.git

2. Install required packages

   pip install -r requirements.txt
   

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples, and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.

Your Name - piinartp@gmail.com

Project Link: https://github.com/ThecoderPinar/Credit-Card-Fraud-Detection-Project