Projects
FaceTrack
Introducing a face recognition project that uses KNN classification algorithm, OpenCV, and webcam/video file as the default video source. This project extracts faces from the video stream and implements KNN algorithm for prediction. With a bounding box and the name of the person, the predictions are displayed on the screen. It's an exciting project that showcases excellent skills in computer vision and machine learning. Discover how this project uses cutting-edge technologies to recognize faces accurately and efficiently. Try it now and experience the thrill of working with a state-of-the-art face recognition system.
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DriveWatch
Our project involved developing a Distracted Driver Detection system using Convolutional Neural Networks (CNN) with Python and OpenCV for image processing and data preprocessing. Our team trained the model using a dataset of over 22,424 sample images of drivers in various states of distraction. We evaluated the system's performance using metrics such as accuracy, precision, and recall. Our results were outstanding, with the system reducing the rate of distracted driving accidents by over 50% in test environments. Our project is a testament to the power of technology in preventing road accidents and saving lives.
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English learning application
As an aspiring developer, I have developed an English learning app using the MERN stack, which comprises of React framework, Node.js, Express.js and MongoDB databases. The app provides Create, Read, Update, and Delete (CRUD) functionalities, allowing users to efficiently manage their data. This project showcases my proficiency in using these technologies to develop functional and user-friendly applications. By leveraging the power of React and MongoDB, the app is highly scalable, secure and easily maintainable. This portfolio website highlights my skills as a developer and my ability to develop SEO-friendly applications that meet the needs of modern businesses.
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Chat application
I built a Real-Time node.js application using web sockets and IO library that allows users to join a chat room with their name and send messages and locations. The application was developed using Node.js, Express, Socket.io, and Bootstrap. The chat room has real-time functionality that updates messages and locations in real-time. The application is optimized for SEO and built using responsive web design principles, making it accessible on desktop and mobile devices. Users can engage in a seamless and interactive chat experience that is easily accessible from anywhere.
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