Projects
Places
I developed a responsive traveling website using React, Express, Node, and MongoDB that allows users to upload, update, and delete photos, names, descriptions, and addresses of places they visit. The website integrates the Google Maps Geolocation API to convert manually written addresses by users into coordinates, and the JavaScript SDK API is used for client-side rendering on the map. For authentication, I implemented JWT tokens, and for image uploads, I used a file-picker for local storage, ensuring error handling is in place on both the server and client sides.
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Expenser
I developed a fully responsive Expense Tracker website utilizing modern technologies including React with Vite for the frontend, Tailwind CSS for styling, and Express and Node.js for the backend. MongoDB served as the database with a GraphQL API integrated using Apollo Client for efficient data fetching and management. The website offers extensive functionality for managing expenses, including options for uploading transactions, updating, deleting, and presenting data with clarity using Chart.js for visual representation. Authentication was implemented using Passport.js with sessions stored in MongoDB, ensuring secure user sessions and access control. Robust error handling was incorporated on both the server and client sides to enhance reliability and user experience across the platform.
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LifeLink
I developed a fully responsive Organ Donation Management System using HTML/CSS, JavaScript, PHP, MySQL, and Tailwind CSS. This system enables doctors and hospitals to register, update, and delete potential organ donors, as well as track the availability of organs. Authentication was implemented using PHP NodeMailer, which generates tokens sent via email to ensure enhanced security and verify user identities. The system's user-friendly interface and robust backend functionality facilitate efficient management of organ donation records and improve coordination between medical professionals and healthcare facilities.
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Machine Learning Mini Project
From January to May 2024 under the guidance of Prof. Pavan Chakraborty, I conducted a Comparative Analysis of Various Clusterings on SDSS Galaxy Images. The study involved implementing KMeans, Agglomerative, DBSCAN, and Gaussian Clustering algorithms. Each clustering method was evaluated using various metrics to assess their effectiveness in clustering SDSS galaxy images. The research aimed to identify the optimal clustering model based on the evaluation results, contributing to advancements in clustering techniques applicable to astronomical image analysis.
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