Welcome to the AI-Assistant-Satisfaction-Prediction-Engine! This application helps you understand user satisfaction with AI assistants. It uses machine learning to analyze user behavior and predicts how satisfied users will be.
- Machine Learning Pipeline: A complete process that takes raw data and predicts outcomes.
- SHAP Explainability: Understand why predictions are made, enhancing trust in AI.
- Evaluation Suite: Tools to review and assess prediction accuracy.
- Interactive Streamlit Dashboard: An easy-to-use web interface for visualizing analytics.
- Data Insights: Find out how different factors affect user satisfaction.
Before you start, make sure your system meets the following requirements:
- Operating System: Windows 10 or later, macOS, or a Linux distribution.
- RAM: At least 8 GB recommended.
- Storage: 500 MB of free disk space.
- Python: Version 3.7 or higher installed (if needed).
- Internet: Required for downloading and running the app.
To get started, visit the Releases page to download the application.
- Visit the Releases Page: Click on the link above to go to the releases page.
- Choose Your Version: Find the latest version of the software. Look for the file that matches your operating system:
- For Windows: Download the
.exefile. - For macOS: Download the
.dmgfile. - For Linux: Download the appropriate
.tar.gzfile.
- For Windows: Download the
- Download the File: Click on the download link for your chosen file. The download will start automatically.
- Install the Application:
- For Windows: Double-click the
.exefile and follow the installation prompts. - For macOS: Open the downloaded
.dmgfile and drag the application to your Applications folder. - For Linux: Extract the
.tar.gzfile and follow any provided instructions.
- For Windows: Double-click the
- Launch the Application: Once installed, you can find the application in your programs or applications list. Open it to begin.
- Open the Application: Launch the software from your device.
- Upload Data: Import your dataset containing user behaviors. The system will guide you through the steps.
- Select Features: Choose the factors you want to include in the satisfaction predictions.
- Run the Model: Click the ‘Run’ button to begin the prediction process.
- Analyze Results: Use the Streamlit dashboard to view your results and insights. You can explore different aspects of user satisfaction.
The application provides detailed results and visualizations of your data, including:
- User Satisfaction Predictions: Get clear forecasts based on user behavior.
- Feature Importance: See which factors impact satisfaction the most.
- Interactive Charts: Explore trends and patterns through visual data representations.
For additional help on using the application, refer to the user guide included in the installation. If you encounter any issues:
- GitHub Issues: Report any bugs or problems directly on the Issues page.
- Community Support: Join discussions with other users in the community forums for tips and shared experiences.
This project utilizes a range of technologies and libraries, including:
- Python for the main application logic.
- Pandas and NumPy for data handling and manipulation.
- Scikit-learn for building machine learning models.
- Streamlit for creating the interactive user interface.
- SHAP for model explainability.
Explore more about AI, machine learning, and user satisfaction through these topics:
- ai-analytics
- behavioral-analysis
- machine-learning
- human-ai-interaction
- data-science
Stay updated on new releases and features by following this project. Regular updates might bring new insights into user satisfaction prediction.
This application is built for educators, researchers, and businesses looking to leverage AI for better user experiences. We appreciate contributions and feedback from all users.
Visit the Releases page to download the latest version now.