Orchestrated development of an advanced models incorporating feature engineering techniques for solar energy forecasting.

Evaluated six deep learning models and achieved an individual accuracy score of 99.49% through rigorous model assessment and testing.

Implemented LSTM and Linear Regression models for feature extraction and utilized RMSProp optimizer to accurately predict number of future COVID-19 cases.

Evaluated models such as RNN, LSTM, and XGBoost algorithms, determining that RNN outperformed others with an RMSE of 0.029 in predicting energy consumption.