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| title: FraudSenseXAI | |
| emoji: 🏃 | |
| colorFrom: yellow | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 4.13.0 | |
| app_file: app.py | |
| pinned: false | |
| <html> | |
| <head> | |
| <title>FraudSenseXAI</title> | |
| <style> | |
| body { | |
| font-family: 'Arial', sans-serif; | |
| margin: 0; | |
| padding: 0 0 100px; /* Adjust bottom padding */ | |
| background-color: #f4f4f4; | |
| color: #333; | |
| line-height: 1.6; | |
| } | |
| .container { | |
| width: 80%; | |
| margin: auto; | |
| overflow: hidden; | |
| padding-bottom: 120px; /* Additional padding to push content up */ | |
| } | |
| h1, h2 { | |
| color: #0056b3; | |
| } | |
| h1 { | |
| font-size: 2.5em; | |
| margin-bottom: 10px; | |
| } | |
| h2 { | |
| font-size: 1.8em; | |
| margin-top: 30px; | |
| } | |
| p { | |
| font-size: 1.1em; | |
| } | |
| ul { | |
| list-style-type: none; | |
| padding: 0; | |
| } | |
| ul li { | |
| background: #e9ecef; | |
| padding: 10px; | |
| margin-bottom: 10px; | |
| border-radius: 5px; | |
| } | |
| ul li strong { | |
| color: #007bff; | |
| } | |
| footer { | |
| background-color: #333; | |
| color: #fff; | |
| text-align: center; | |
| padding: 10px; | |
| position: fixed; | |
| left: 0; | |
| bottom: 0; | |
| width: 100%; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <h1>FraudSenseXAI</h1> | |
| <h2>Overview</h2> | |
| <p><strong>FraudSenseXAI</strong> is an innovative Machine Learning (ML) and Explainable Artificial Intelligence (XAI) application, developed as a part of an MSc final project by Othniel Obasi. This application is dedicated to detecting and analyzing fraudulent activities, with a strong emphasis on the interpretability and transparency of its AI models.</p> | |
| <h2>Key Features</h2> | |
| <ul> | |
| <li><strong>Robust Fraud Detection:</strong> Utilizes advanced ML techniques to identify fraudulent transactions accurately.</li> | |
| <li><strong>Explainable AI Elements:</strong> Employs XAI approaches to provide clear insights into the decision-making processes of the AI.</li> | |
| <li><strong>Interactive Web Interface:</strong> Features a user-friendly web application for easy access and interpretation of results.</li> | |
| <li><strong>Dynamic Visualizations:</strong> Integrates Plotly for interactive and insightful data visualizations.</li> | |
| <li><strong>Applicability Across Sectors:</strong> Suitable for use in finance, e-commerce, digital banking, and other sectors.</li> | |
| </ul> | |
| <h2>About the Author</h2> | |
| <p>This project is an MSc Dissertation on the XAI Application of Fraud Detection, authored by Othniel Obasi. It represents a significant contribution to the field of AI, offering practical solutions and valuable insights for the detection of fraudulent activities using AI.</p> | |
| </div> | |
| <footer> | |
| <p>FraudSenseXAI © 2024</p> | |
| </footer> | |
| </body> | |
| </html> | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |