In my 4th semester of BCA, I undertook a project centered around an Online Doctor Appointment System. This comprehensive system, built with HTML, CSS, JavaScript, and PHP, integrates seamlessly with Twilio API for appointment reminders and leverages Google Maps API for convenient location tracking. The utilization of SQL ensures secure data management, offering patients a user-friendly interface to schedule appointments while enhancing doctor-patient interactions. This project not only honed my technical skills but also deepened my understanding of API integration and database management.

In my recent freelancing project, I created an e-commerce website for Divine Mines and Gems. I developed a user-friendly platform showcasing a variety of gems and mines. The site features secure transactions, personalized user dashboards, and efficient search capabilities. Through API integration and database management, the website ensures real-time updates and secure data handling. This project enriched my web development skills and provided valuable experience in e-commerce solutions, API integration, and database management.

In my latest project, achsConfession, I built a dynamic platform with Next.js, MongoDB, and various APIs. It enables users to share anonymous confessions for a unique and engaging experience. Leveraging Next.js for fast server-side rendering and MongoDB for seamless data storage, the project boasts strong API functionality for posting and retrieving confessions. The user interface is intuitive with real-time updates and a responsive layout. I deployed this Next.js project using Vercel, enhancing accessibility and performance. Achs Confession honed my skills in Next.js development, MongoDB management, API integration, and efficient deployment in Vercel.

In my latest project, Real-Time Face Recognition Attendance System, I developed an innovative attendance tracking solution using OpenCV, sklearn, and various Python libraries. This system enables seamless face recognition and attendance marking for students, either through live camera feed or uploaded images, ensuring flexibility and ease of use. Leveraging OpenCV for efficient image processing and face detection, and sklearn's KNeighborsClassifier for accurate face recognition, the project is both robust and reliable. The system includes features like time-based attendance validation and CSV-based record management. By deploying this project, I honed my skills in computer vision, machine learning, real-time data processing, and efficient Python scripting.