solar power predicting

Developed a machine learning solution to predict solar power generation using environmental and weather-related data. The project involved data cleaning and preprocessing, exploratory data analysis (EDA), and data visualization to identify patterns and relationships between weather conditions and solar energy production.

Key features include analyzing relevant variables, training and evaluating machine learning models, comparing model performance, and assessing prediction accuracy using Python, Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.

The project demonstrates the application of data science and predictive modeling techniques to renewable energy analysis, supporting better understanding of solar power generation patterns.