Tanya Garg

Full-Stack Developer

S.D. COLLEGE OF ENGINEERING AND TECHNOLOGYfull_time, internship, freelance

Open to roles

Problem SolvingCommunicationGITProject Management

Queue Cure – Real-Time Clinic Queue Management System

Many neighborhood clinics still manage patient queues using paper tokens and verbal announcements. Patients often wait for hours without knowing when their turn will arrive, while receptionists spend valuable time answering repeated questions and manually tracking queue progress. This process creates confusion, increases the risk of errors, and reduces operational efficiency. Queue Cure solves this problem through a real-time digital queue system that lets receptionists manage tokens instantly and allows patients to view queue status, tokens ahead, and accurate wait-time estimates live today. Process I started by analyzing how small clinics manage patient flow and identifying the main pain points faced by receptionists and patients. The core requirement was enabling instant queue visibility without page refreshes while keeping the workflow reliable. I designed two interfaces: a receptionist dashboard for adding patients, calling the next token, and setting consultation time, and a patient view for tracking queue status. React was used for the frontend, while Node.js, Express.js, and MongoDB handled backend operations and persistence. Socket.IO was integrated to synchronize updates across screens. Wait times were calculated dynamically using queue position and consultation duration. I also tested edge cases such as empty queues, duplicate tokens, refresh recovery, and concurrent actions Results Reduced patient uncertainty by enabling real-time queue visibility and live wait-time estimation. Receptionists experienced faster token handling and reduced repetitive inquiries. System improved operational efficiency by minimizing manual tracking errors. Socket.IO ensured instant sync across devices, improving responsiveness. Testing confirmed stable performance under concurrent updates and refresh scenarios. Overall, the system improved transparency, reduced perceived wait time, and streamlined clinic workflow significantly. Reflection Next time, I would focus more on user research with real clinic staff earlier in the process to validate assumptions before building. I would also introduce predictive wait-time models based on historical consultation data instead of fixed averages. Improving UI accessibility for elderly patients would be a priority. Additionally, I would add offline fallback support for network failures and build analytics for clinics to track peak hours and optimize staffing.

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IPL Match Analytics — What Actually Wins IPL Games?

IPL discussions are often based on assumptions like “winning the toss guarantees victory” or “powerplay overs decide matches.” This project aimed to test those assumptions using real IPL ball-by-ball data from 5 seasons. The goal was to identify which factors truly impact match outcomes by analyzing toss results, phase-wise performances, and player statistics through data visualization and exploratory analysis. Process I collected IPL datasets in CSV format and analyzed them using Python and Pandas. The first step involved cleaning missing values, organizing match records, and dividing innings into Powerplay, Middle Overs, and Death Overs. I then performed exploratory data analysis to compare winning and losing teams based on runs, wickets, and toss outcomes. Multiple visualizations were created using Matplotlib to identify patterns and trends. Different chart layouts and comparison methods were tested before selecting the most readable and insight-driven visuals. The final report summarized findings with charts, tables, and concise explanations. Results The project analyzed over 240,000 ball-by-ball IPL records and converted raw data into meaningful insights. The analysis showed that toss advantage had a smaller impact on match results than commonly believed, while strong death-over performances were closely linked to victories. Top-performing batters and bowlers across seasons were identified using statistical analysis. The project improved my skills in data cleaning, visualization, exploratory analysis, and storytelling with data. Reflection If I continue this project, I would build an interactive dashboard using Power BI or Tableau to make the analysis more dynamic. I would also include venue-based performance analysis, player pressure metrics, and predictive models to forecast match outcomes. Integrating live IPL APIs and advanced machine learning techniques could further improve the depth and real-time capabilities of the project.

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