Online Payment Fraud Detection is a project designed to identify and mitigate fraudulent transactions in real-time. Utilizing machine learning algorithms, this project analyzes transaction data to distinguish between genuine and potentially fraudulent activities, offering valuable insights to improve security in online payment systems. This solution is especially relevant for financial institutions and e-commerce platforms looking to safeguard customer transactions.
Data Preprocessing: Cleans and preprocesses transaction data for accurate analysis.
Feature Engineering: Extracts key features from transaction data to enhance model accuracy.
Fraud Detection Models: Implements various machine learning models (e.g., Logistic Regression, Random Forest, and XGBoost) for classifying transactions.
Real-Time Detection: Simulates real-time fraud detection for immediate response to suspicious activities.
Performance Evaluation: Analyzes model performance with metrics such as accuracy, precision, recall, and F1-score.
Programming Language: Python
Libraries: Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, Seaborn
Optional Integrations: Flask or FastAPI for API deployment
Online-Payment-Fraud-Detection/
│ ├── data/ # Folder for storing raw and cleaned data files
├── src/ # Folder containing source code
│ ├── data_preprocessing.py # Code for cleaning and preprocessing transaction data
│ ├── feature_engineering.py # Code for feature extraction and transformation
│ ├── model_training.py # Scripts to train and evaluate machine learning models
│ ├── real_time_detection.py # Simulates real-time fraud detection
├── notebooks/ # Jupyter Notebooks for exploratory analysis and model experimentation
├── README.md # Project overview and usage instructions
└── requirements.txt # List of required Python libraries
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Clone this repository:
git clone https://github.com/hhdjwdabsxsx/Online-Payment-Fraud-Detection.git
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Navigate to the project directory:
cd Online-Payment-Fraud-Detection
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Install the required libraries:
pip install -r requirements.txt
Data Preprocessing: Run data_preprocessing.py to clean and prepare transaction data.
Feature Engineering: Use feature_engineering.py to extract and transform relevant features.
Model Training: Execute model_training.py to train fraud detection models and evaluate their performance.
Real-Time Detection Simulation: Run real_time_detection.py to simulate real-time fraud detection and test the model on new transactions.
Fraudulent transaction identification with high accuracy
Model performance metrics: precision, recall, and F1-score
Insights into features contributing to fraud detection
Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request.
Contact For questions or further information, please reach out at [yashrajsinha02@gmail.com].
Secure your transactions! 🚀