🚀 Tarass

Intelligent Appointment System for Absher Users

Our project introduces an intelligent service that can be integrated with the Absher platform to enhance user experience—especially for senior citizens—by improving appointments and reducing service-center congestion.

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✨ Features

• 👥 Crowd Analytics: Analyze real-time and historical congestion levels.
• 📈 Congestion Forecasting: Predict crowd levels using ML models.
• 🗓️ Smart Appointment Recommendation: Suggest the best appointment time for each user.
• ⏰ No-Show Prediction & Early Alerts: Estimate the likelihood of no-shows and notify users.

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🛠️ Technologies & Tools

Tool / Language	Icon
Python	🐍
C#	💻
SQL / SQLite	🗄️
ASP.NET	🌐
Expo (React Native)	📱
Figma	🎨
Google Colab	☁️

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📂 Project Structure

• ml_models/ : Machine learning models (forecasting & prediction)
• ml_api/ : API to serve the ML models
• frontend/ : mobile interface for users
• Backend/ : Database and Integrartion

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⚡ Getting Started

Each component can run independently. Check the respective README in each folder for setup instructions.

• ML Models: Open notebooks in Google Colab or Jupyter Notebook
• API: Run python app.py (check port)
• Frontend: Open index.html in browser or run Expo for mobile app