Credit card fraud is a troubling issue, affecting millions globally. With the prowess of machine learning, we can forge effective solutions. This blog post will guide you through setting up a project aimed at detecting credit card fraud utilizing advanced sampling techniques and various machine learning models.

Understanding the Challenge

Credit card fraud detection comes with its hurdles, particularly with temporal drift and a significant class imbalance. In essence, this means that fraudulent transactions are much rarer than legitimate ones, making it challenging for models to recognize fraud effectively. Our project tackles these issues head-on using Adaptive Synthetic Sampling Approach (ADASYN) and Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset.

Getting Started: Installation

Follow these simple instructions to set up your fraud detection project:

• Clone the project repository:

$ git clone https://github.com/yazanobeidi/fraud-detection.git
cd fraud-detection

• Install the dependencies using a virtual environment:

$ virtualenv env
source env/bin/activate
pip install -r requirements.txt

Usage: Running the Project

Now that your environment is set up, here’s how to use the project:

1. Read the Paper (PDF): credit_card_fraud_detection_yazan_obeidi.pdf
2. Run the Jupyter Notebook:
1. Unzip the dataset:

$ unzip datacreditcardfraud.zip

2. Generate a balanced dataset using ADASYN resampling (this may take several minutes):

$ python adasyn.py

3. Launch the Jupyter Notebook:

$ jupyter notebook

How it Works: The Analogy Behind Machine Learning Models

Imagine trying to recognize different species of birds. If the majority of birds you see are pigeons (representing legitimate transactions), it becomes hard to spot a rare parrot (representing fraudulent transactions). The challenge is to train our eyes (the machine learning model) to recognize both pigeons and parrots despite the imbalance.

In our project, we have a blend of different models: Random Forest, Support Vector Machine, and Multi-Layer Perceptron. Each of these models acts like a unique birdwatcher, employing different techniques and perspectives for spotting our rare parrot. Some birdwatchers may be better at spotting parrots in certain environments, illustrating how the optimal sampling method depends on the data and the model.

Troubleshooting

If you encounter any issues while setting up or running the project, consider the following troubleshooting tips:

• Ensure all dependencies are correctly installed by reviewing the requirements.txt file.
• If the Jupyter Notebook fails to launch, confirm that Jupyter is installed in your virtual environment.
• For any dataset issues, check that the dataset has been unzipped correctly and is in the expected directory.
• If you experience slow performance during data resampling, ensure that your system has adequate resources (CPU and RAM).

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

In this project, we delved into the complexities of credit card fraud detection using machine learning. By employing sophisticated techniques such as ADASYN and SMOTE, coupled with various machine learning models, we enhance our ability to combat fraud effectively. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.