PROJECTS

Embedded Systems

Developed an automated system to optimize water usage for gardens and lawns. By integrating soil moisture sensors with a microcontroller and controllable valves, the system intelligently monitors soil conditions and adjusts watering schedules accordingly. It features real-time weather data integration, allowing for efficient irrigation based on environmental factors, and can be remotely controlled via a smartphone app. This project not only reduces water waste but also ensures optimal plant health, showcasing the practical application of embedded systems in creating sustainable solutions for everyday challenges.

Machine Learning

Developed a groundbreaking machine learning model focused on predicting stroke risk in patients. This project leveraged advanced algorithms to analyze critical health indicators such as hypertension levels, blood sugar readings, body weight, and age. By processing and interpreting these key features, our model achieved remarkable accuracy in identifying individuals at high risk of stroke. The system's ability to synthesize multiple health metrics into actionable insights demonstrates the power of machine learning in preventive healthcare. This project not only highlights proficiency in data analysis and predictive modeling but also underscores the potential of AI to revolutionize medical diagnostics and improve patient outcomes.