https://github.com/mdparwez/onlinepaymentfraud

online payment fraud using machine learning
https://github.com/mdparwez/onlinepaymentfraud

machine-learning-algorithms python streamlit

Last synced: about 2 months ago
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online payment fraud using machine learning

• Host: GitHub
• URL: https://github.com/mdparwez/onlinepaymentfraud
• Owner: MdParwez
• License: mit
• Created: 2024-05-10T06:45:12.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-03T04:00:14.000Z (about 2 years ago)
• Last Synced: 2025-04-09T20:55:31.401Z (about 1 year ago)
• Topics: machine-learning-algorithms, python, streamlit
• Language: Jupyter Notebook
• Homepage: https://tejasjd08-final-online-payment-fraud-detection.streamlit.app/
• Size: 2.34 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ONLINE-PAYMENT-FRAUD-DETECTION  (Data Science Project)

### Overview:

This project focuses on detecting online payment fraud using various machine learning algorithms. The objective is to identify fraudulent transactions to enhance the security of online payment systems.

### Machine Learning Algorithms Used

- **Support Vector Machine (SVM)**: Employed to classify transactions as fraudulent or non-fraudulent based on feature analysis.

- **K-Nearest Neighbors (KNN)**: Used for classification by finding the majority class among the k-nearest transactions.

- **Random Forest**: Utilized for its ensemble learning method, leveraging multiple decision trees to improve prediction accuracy.

- **Logistic Regression**: Applied for binary classification to estimate the probability of a transaction being fraudulent.

### Project Workflow

1. **Data Collection and Preprocessing**:

   - Gathered a dataset containing transaction details.

   - Cleaned and preprocessed the data to handle missing values and categorical features.

   - Performed feature scaling to standardize the dataset.

2. **Exploratory Data Analysis (EDA)**:

   - Conducted EDA to understand the distribution of data.

   - Visualized patterns and correlations between different features.

   - Identified key indicators of fraudulent transactions.

3. **Model Training and Evaluation**:

   - Split the dataset into training and testing sets.

   - Trained each machine learning algorithm on the training data.

   - Evaluated the models using metrics such as accuracy, precision, recall, and F1-score.

   - Compared the performance of different algorithms to select the best model.

4. **Deployment**:

   - Integrated the best-performing model into a Streamlit application.

   - Created an interactive user interface for real-time fraud detection.

   - Deployed the application to provide a user-friendly platform for detecting fraudulent transactions.

### Streamlit Application

The final model was deployed using Streamlit, a powerful and easy-to-use framework for building data applications. The Streamlit app allows users to:

- Upload transaction data for analysis.

- View predictions on whether a transaction is fraudulent or not.

- Access visualizations and insights derived from the data.

### Conclusion

This project demonstrates the effectiveness of machine learning algorithms in detecting online payment fraud. By leveraging multiple algorithms and deploying the solution on a Streamlit app, it provides a robust and user-friendly tool for enhancing the security of online payment systems.

### Future Work

- **Improving Model Accuracy**: Explore additional machine learning algorithms and techniques to further improve prediction accuracy.

- **Real-time Detection**: Implement real-time data processing and fraud detection capabilities.

- **Scalability**: Enhance the application to handle larger datasets and more complex transaction patterns.