Thilak Ram

Full-Stack Developer

Chennai Institute of Technologyfull_time, internship, freelance

Open to roles

C++PythonJavaHTML

Student Expense Tracker - Money AI

This lightweight Student Expense Tracker has been built entirely using Python's standard library to guarantee clean, zero-dependency execution across any environment. In strict adherence to the project guidelines, no external database or heavy web frameworks (like Django or Flask) are used; instead, all transactions are managed dynamically in-memory using standard lists and dictionaries. To allow for immediate testing and evaluation, the application comes preloaded with three sample student expenses (Canteen Lunch, Monthly Bus Pass, and Reference Books). Reviewers can execute main.py directly from the terminal to view a formatted ledger, review automatic budget warnings, analyze spending categories, and add new transactions. All calculations and budget health thresholds update in real-time. Using the AI Student Expense Tracker offers several key benefits tailored specifically to the financial realities of college life: First and foremost, it instills strong financial discipline by keeping your monthly budget limit front and center, preventing accidental overspending before the month ends. By dividing your expenses into clear categories like Academics, Food, Transit, and Entertainment, you instantly see where your pocket money is actually going, helping you cut down on unnecessary impulses. The built-in budget health indicator acts as a personal financial guardian, proactively sending you warning alerts the moment your spending crosses 80% of your threshold. Finally, having an organized, clutter-free transaction ledger means no more guessing games at the end of the semester; you have a completely transparent, easy-to-read record of your habits that helps you make stress-free, smarter money decisions every day. Thank You.

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QueueCure Pro

Patients in hospitals often spend significant time waiting without knowing their queue status or estimated consultation time. Existing queue systems lack real-time visibility and intelligent predictions, leading to frustration and operational inefficiencies. Queue Cure solves this problem through AI-powered waiting-time prediction and real-time queue tracking. Process ### Our Process Our process began by identifying a common challenge faced by hospitals and patients: long waiting times and a lack of visibility into queue status. We researched existing queue management practices and analyzed the pain points experienced by both patients and healthcare providers. Based on these findings, we designed Queue Cure as an AI-powered queue management solution that provides real-time queue tracking and waiting-time predictions. We then planned the system architecture, including the patient interface, hospital dashboard, backend services, and AI components. Using Google AI Studio and modern web technologies, we developed the prototype and integrated real-time communication to ensure live queue updates. The system was tested under different queue scenarios to valid Results Queue Cure successfully demonstrates the potential of combining artificial intelligence with real-time queue management to improve the healthcare experience. The developed prototype enables patients to view their queue status, receive estimated waiting times, and stay informed through live updates, reducing uncertainty during the waiting process. For healthcare providers, the system offers better visibility into patient flow and supports more efficient queue management. Through the integration of AI-based wait-time prediction and real-time synchronization, the project d Reflection If given more time and access to real-world healthcare environments, I would focus on improving the accuracy, scalability, and practical adoption of Queue Cure. I would train the AI model using actual hospital queue data to provide more precise waiting-time predictions and integrate appointment scheduling to help patients plan their visits more effectively. I would also develop a dedicated mobile application with multilingual support to improve accessibility for a wider range of users. Additionally, I would introduce features such as emergency patient

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