Prasath Anissh S

Data Analyst

Sastra Deemed Universityfull_timeOpen to roles

4 projects2 challenge entries1 reviewed

PythonMachine LearningData Analysis

Challenge entry · MemoryVerse AI '26 by Wooble

AI-digital-identify-system

This project is an AI-powered Digital Identity Management System developed using React, TypeScript, Node.js, Express.js, and the Google Gemini API. Setup Steps: 1. Clone the repository. 2. Run npm install 3. Create a .env file and add your Gemini API key: GEMINI_API_KEY=YOUR_GEMINI_API_KEY 4. Run npm run dev Test Credentials: No login credentials are required. Design Decisions: - Built with a responsive and user-friendly interface. - AI responses are powered by Google Gemini. - Clean component-based architecture for easy maintenance and scalability. - Optimized for modern web browsers.

Reviewed

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Challenge entry · Student Expense Tracker by Retrod Travel Tech

Student Expense Tracker

This project is built using React, Vite, and TypeScript. No login or test credentials are required. Simply open the application and start adding, editing, or deleting expenses. The project focuses on a clean, responsive UI and simple expense management. Future improvements include monthly budget tracking, expense analytics, and cloud data storage.

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Project

AI-digital-identify-system

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Project

Real-Time Clinic Intelligence | Live Queue Management

Many clinics still rely on paper token systems and manual queue management. Patients often wait for long periods without knowing their position in the queue or estimated waiting time. Receptionists must manually manage patient flow, leading to delays and errors. The goal of this project was to build a real-time clinic queue management system that provides visibility to patients and simplifies queue handling for clinic staff. Process I started by identifying the core requirements: patient registration, token generation, live queue tracking, and wait-time estimation. I designed two separate views: a receptionist dashboard for managing the queue and a patient view for tracking queue status. For real-time updates, I evaluated different approaches and implemented Server-Sent Events (SSE) to keep all connected clients synchronized without requiring manual page refreshes. The backend was built using Express.js, while the frontend was developed using React and TypeScript. I implemented queue state management, token assignment, consultation time settings, and automatic wait-time calculations. After testing the application locally and in deployment, I refined the update flow to ensure patients instantly receive queue changes Results The final solution provides real-time queue visibility for both patients and receptionists. Queue updates are reflected instantly across connected screens without requiring page refreshes. Patients can view the current token, the number of people ahead, and their estimated waiting time. The system successfully demonstrates a scalable approach to replacing manual token systems with a digital workflow. The project was deployed successfully and includes live synchronization, queue management, and dynamic wait-time calculation. Reflection If given more time, I would add user authentication, appointment scheduling, SMS or WhatsApp notifications, and support for multiple doctors or clinic branches. I would also replace file-based storage with a production database such as MongoDB or PostgreSQL and implement advanced analytics for patient wait times and clinic performance. I would further improve reliability by adding role-based access control and enhanced error handling for larger-scale deployments.

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