Energy forecasting represents a critical application of machine learning in sustainability and cost optimization. In this detailed walkthrough, I'll demonstrate building a complete ML pipeline for predicting building energy consumption based on features like square footage, occupancy patterns, weather conditions, and historical usage data. We'll start with exploratory data analysis using pandas and matplotlib, implement comprehensive data preprocessing including feature scaling, encoding categorical variables, and handling missing values. The tutorial covers model selection comparing linear regression, random forests, and neural networks, followed by rigorous evaluation using cross-validation and multiple regression metrics. We'll build a Streamlit web interface for real-time predictions and deploy the entire system for production use. I'll share insights from achieving high accuracy across diverse building types and the business impact of accurate energy forecasting on operational costs.