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.