Welcome to our Credit Card Fraud Detection project! In today's digital world, safeguarding financial transactions from fraud is paramount. Leveraging advanced machine learning models like Logistic Regression, Random Forest, and Decision Tree, our project is dedicated to detecting and preventing fraudulent activities with precision.
Access the Kaggle dataset for Credit Card Transactions Fraud Detection.
This dataset contains simulated credit card transactions, including both legitimate and fraudulent transactions occurring from January 1st, 2019, to December 31st, 2020. It covers transactions involving credit cards from 1000 customers and interactions with 800 merchants. The dataset was generated using the Sparkov Data Generation tool created by Brandon Harris.
Ensure you have the following libraries installed:
Follow these steps to install the project:
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Clone the repository:
git clone https://github.com/kamlesh-IY9/Credit-Card-Transactions-Fraud-Detection-by-Afame-Technologies.git
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Navigate to the repository directory:
cd Credit-Card-Transactions-Fraud-Detection-by-Afame-Technologies
Usage:
- Open the notebook file named "Credit_Card_Transaction_Fraud_Detection.ipynb" in your Jupyter Notebook or Visual Studio Code.
- Follow the instructions within the notebook to execute the code cells and analyze the fraud detection process.
We welcome contributions from the community to enhance our fraud detection solution. Whether it's ideas for improvements, bug fixes, or new features, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License, allowing you to freely use, modify, and distribute the code, with no warranties or liabilities.
Got questions, suggestions, or interested in collaborating? Reach out to us at [email protected]. We're eager to connect with fellow enthusiasts and experts in the field of fraud detection!
Empowering financial security, one transaction at a time. Join us in the fight against fraud! 🛡️🔒💪