Designed and optimized Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) algorithms to improve the accuracy of sign language recognition.Utilized MediaPipe and OpenCV for real-time hand tracking and gesture recognition, enabling efficient and accurate sign language detection.Preprocessed and converted data formats, split datasets into training and testing sets, and employed Pandas for effective data manipulation and analysis.Evaluated models using metrics such as accuracy, precision, recall, and F1-score; analyzed confusion matrices to refine model performance.Created an intuitive and accessible GUI for interactive sign language learning, enhancing user engagement and ease of use.Conducted rigorous testing and debugging of machine learning models to ensure high performance and reliability of the sign language recognition tool.Documented project processes, outcomes, and future improvement strategies, ensuring clear communication and knowledge sharing.

Enhanced operational efficiency by designing and implementing secure, scalable healthcare management systems,improving hospital management operations.Architected an Admin module with extensive control over the system, managing doctors, patients, adding doctorspecializations, and monitoring session logs, ensuring robust administration.Collaborated with cross-functional teams to integrate patient, doctor, and admin functionalities, streamlining workflowsand improving system usability.