This project investigates the application of advanced machine learning techniques to predict energy consumption patterns using a comprehensive dataset. By implementing a range of algorithms, including ensemble methods and neural networks, the project enhances the accuracy of power consumption forecasts through rigorous data preprocessing, model selection, and hyperparameter optimization.

A significant outcome of this project is the provision of actionable insights that inform energy management strategies, enabling urban planners and utility companies to optimize resource allocation and reduce operational costs. This work demonstrates the transformative potential of machine learning in addressing real-world challenges in energy consumption forecasting, ultimately contributing to more sustainable urban environments and efficient energy use.