Paper Title
Helios- Solar Energy Predictor

The utilization of solar energy has gained significant prominence as a sustainable and renewable power source in recent years. One of the critical factors affecting the efficiency of solar cells is the temperature difference between their upper and lower surfaces. This project presents a machine learning-based approach to predict the amount of energy produced by a solar cell by analyzing the temperature differential data. By employing various regression algorithms and a comprehensive dataset, the project successfully creates an accurate predictive model, offering potential benefits for optimizing solar panel installations and enhancing renewable energy resource management. In conclusion, this project showcases the potential of machine learning techniques in enhancing the efficiency and sustainability of solar energy systems by accurately forecasting energy production based on temperature differentials. The model's ability to predict energy output can contribute to better resource management and informed decision-making in the renewable energy sector.