Abstract
Energy consumption prediction plays a critical role in optimizing resource allocation, reducing costs, and promoting sustainability in various industries. The goal of this study is to predict energy consumption using three machine learning models: Linear Regression, LightGBM, and a Neural Network. After conducting comprehensive exploratory data analysis (EDA) to understand the dataset's structure and key features, the models were built and evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² score. The results demonstrate that LightGBM outperforms the other models, achieving the highest prediction accuracy. These findings emphasize the importance of advanced machine learning methods in energy forecasting for improving predictive performance and decision- making.