Credit Card Fraud Detection System

This project is focused on building a machine learning model to detect fraudulent credit card transactions. The model analyzes transactional patterns and flags suspicious activities to help reduce financial fraud.

🔍 Overview

Credit card fraud is a significant issue in the financial sector, causing billions in losses annually. This project uses machine learning to classify transactions as either legitimate or fraudulent based on historical data.

📊 Dataset

• The dataset used in this project is from Kaggle: Credit Card Fraud Detection.
• It contains transactions made by European cardholders in September 2013.
• Total Records: 284,807 transactions
• Fraudulent Transactions: 492
• Features: 30 (anonymized due to confidentiality — PCA transformed)
• Label: Class → 0 (Non-Fraud), 1 (Fraud)

🧠 Features

• Data preprocessing & handling class imbalance
• Feature scaling and transformation
• Model training using:
  • Logistic Regression
  • Random Forest
  • Decision Tree
  • XGBoost (optional)
• Evaluation metrics:
  • Accuracy
  • Precision, Recall, F1-Score
  • Confusion Matrix
  • ROC-AUC Curve

🛠️ Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn
• Matplotlib, Seaborn
• Git & GitHub
• Git LFS (for handling large datasets)

🚀 How to Run

1. Clone the repository

   git clone https://github.com/ankitsingh32/Credit-Card-Fraud-Detection.git
   cd Credit-Card-Fraud-Detection

2. Install dependencies It’s recommended to use a virtual environment:

   pip install -r requirements.txt

3. Run the script

   python "Credit Card Fraud Detection model 1.py"
   

📁 Project Structure

Credit-Card-Fraud-Detection/
│
├── creditcard.csv                  # Dataset (Git LFS tracked)
├── Credit Card Fraud Detection model 1.py
├── Credit Card Fraud Detection model 2.py
├── .gitattributes                  # Git LFS configuration
├── README.md
└── requirements.txt                # Python dependencies#

📌 Notes

Dataset is large (>100MB). Make sure Git LFS is installed. Class imbalance is handled using under-sampling or SMOTE (Synthetic Minority Over-sampling Technique).

📄 License

This project is open-source and available under the MIT License.

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