Credit Card Fraud Detection using Machine Learning

📌 Project Overview

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. The dataset used for training and testing was obtained from Kaggle: Credit Card Fraud Detection Dataset 2023. The dataset contains 30 numerical features, suspected to be transformed using Principal Component Analysis (PCA), but no feature descriptions are available.

🎯 Project Goal

• Build an effective fraud detection model.
• Handle class imbalance in the dataset.
• Train, evaluate, and fine-tune various machine learning models.
• Assess the feasibility of deploying the model in real-world scenarios.

📊 Dataset Information

• Source: Kaggle
• Features: 30 numerical columns (V1, V2, ..., V30)
• Target Variable: Binary (0 = Legitimate, 1 = Fraudulent)
• Challenge: Lack of feature descriptions, making real-world deployment difficult

🔧 Technologies Used

• Programming Language: Python
• Libraries: Sklearn, NumPy, Pandas, Matplotlib, Seaborn, PyTorch
• ML Models Tested: Logistic Regression, Decision Trees, Random Forest (Best Model)

🏆 Best Model - Random Forest Classifier

After testing multiple models, Random Forest Classifier was found to be the most effective:

• Training Accuracy: 99.98%
• Testing Accuracy: 99.94%
• Training Loss: 0.49
• Testing Loss: 1.04
• Cross-validation Accuracy: 99.93%
• Feature Importance: The most important features were V17, V16, V2, V21, and V9.

🚧 Limitations & Deployment Challenge

While the model performs exceptionally well on the dataset, it cannot be deployed in real-world conditions due to:

• The dataset lacking proper feature descriptions.
• PCA-transformed features making it unclear what real-world input values would be.
• The need for actual banking transaction features for real deployment.

✅ How to Use

1. Clone the repository:

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

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Jupyter Notebook (Fraud-Detection.ipynb) to see the full training process.

📝 Future Improvements

• Use a dataset with clearly defined transaction features.
• Experiment with deep learning techniques like autoencoders or anomaly detection.
• Implement real-time fraud detection using streaming data.

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📌 Note: This project is for learning purposes only and is not intended for real-world financial fraud detection.

📢 Contributions & Feedback: Feel free to contribute or suggest improvements! 🚀