SWE
Creating a simple DIY Facial Recognition Attendance System using AI for efficient and hassle-free attendance tracking.
A machine learning project designed to detect and predict fraudulent transactions in a highly imbalanced dataset. The model uses techniques like SMOTE for class balancing and a Keras neural network to classify transactions as legitimate or fraudulent. Achieves an accuracy of 93%, with room for further optimization.
The Dream Catcher project utilizes computer vision tools like OpenCV and MediaPipe to track head movements during sleep. It employs NumPy for data manipulation and Pandas for organizing data into tables. Matplotlib is then used to visualize the analyzed results.
A Flutter application for booking medical appointments, featuring Firebase backend integration and a payment system.