Developed a comprehensive fitness tracker application, assisting 500+ users in monitoring their workout routines, dietary intake, and health progress. Generated interactive dashboards and reports, leading to a 25% increase in user engagement and promoting better health and fitness decision-making.

Implemented a GAN (Generative Adversarial Network) in Python for collective activity recognition, streamlining the identification process by 30% compared to conventional models.

Created a sentiment analysis tool using Python and machine learning algorithms to analyze social media posts for a brand, helping improve their online reputation management strategies.

Implemented key features such as product listings, shopping cart functionality, and user authentication. Leveraged React JS and Firebase to implement vital e-commerce features, propelling user engagement by 25% with enhanced shopping cart functionality and user authentication.

Led the design and implementation of a Hospital Billing Management System using C++, Python, and web technologies, streamlining billing for a local hospital and reducing bill generation time by 50%. The system, integrated with PostgreSQL, automated charge calculations, enhanced data security, and improved billing efficiency by 30%, positively impacting both staff satisfaction and patient experience.

Developed a versatile Python application for dual-format encryption, capable of securely encrypting both images and text. Achieved efficient encryption with a user-friendly interface, ensuring high security and ease of use. Rigorously tested across 1,000+ instances, demonstrating reliability and adaptability for various security needs.

Developed a RESTful API using Spring Boot and MySQL, implementing transaction management and dependency injection to support seamless CRUD operations, scaling effectively with increasing employee data. Implemented Spring Security for authentication and authorization, securing sensitive employee data by restricting access to specific endpoints based on user roles.

Built a responsive full-stack web application using React, Express, and MongoDB, implementing real-time CRUD functionality with efficient state management, enabling users to interact seamlessly across devices. Designed a dynamic user interface using React MUI, introducing a light/dark mode toggle feature to enhance user customization and improve accessibility for diverse user preferences.

Created an e-commerce platform using React and Redux for efficient state management, integrating features like a dynamic shopping cart and product filtering, resulting in a 30% increase in user engagement. Utilized Node.js and Express for backend services, implementing efficient API calls to handle a high volume of concurrent user requests while maintaining low server response times.

Built a comprehensive API using Node.js and Express, integrating the Google Books API to allow users to search for books by ISBN, author, or title, significantly enhancing the app's functionality and usability. Implemented secure authentication using JWT to ensure that only authenticated users can access certain features, maintaining a high level of security throughout the application.

This project is a RESTful API built with FastAPI and MongoDB to manage a product inventory. It allows users to create new products or update existing ones by name, retrieve individual products by their unique ID, and list all available products. Utilizing asynchronous operations with the Motor driver ensures efficient database interactions, while Pydantic models handle data validation and serialization. The API is secured with CORS middleware, permitting requests from https://localhost:3000, making it ideal for integration with frontend applications. Comprehensive error handling ensures robust performance, making this API a reliable backbone for inventory management in applications like e-commerce platforms.

Developed an AI-powered skincare product recommendation platform using React for the frontend and Flask for the backend, seamlessly integrating machine learning algorithms to analyze facial features and deliver highly accurate personalized skincare product recommendations. Implemented a robust Flask API backend capable of processing large datasets from user-uploaded images and generating real-time recommendations, significantly enhancing user engagement by providing tailored results that cater to individual skincare needs.

Developed a reliability-aware caching system utilizing Redis for storing API data, Express as the middleware, and React for the frontend interface. The primary objective of the project was to enhance system reliability and reduce the number of API calls by caching responses in Redis, thereby minimizing direct interactions with the API. Implemented a Time-To-Live (TTL) cache invalidation policy to ensure data freshness and maintain optimal cache performance. This architecture not only improves application performance by decreasing latency and reducing the load on API servers but also ensures a more dependable and efficient user experience through effective caching strategies. By leveraging Redis for caching, Express for seamless middleware integration, and React for a responsive frontend, the system achieves a robust balance between reliability and performance.