Predicting Energy Consumption using Machine Learning

In this mini project, we aim to predict energy consumption using machine learning techniques. The project involves collecting historical energy usage data from various sources, such as smart meters, weather data, and demographic information. After preprocessing the data to handle missing values and normalize features, we will select suitable algorithms, such as linear regression, decision trees, or neural networks, to build predictive models. We will split the dataset into training and testing sets to evaluate model performance using metrics like mean absolute error (MAE) and root mean square error (RMSE). Visualizations will be created to illustrate trends and patterns in energy consumption, while feature importance analysis will help identify the most significant factors influencing usage. Ultimately, the goal is to develop a robust model that can assist households and businesses in optimizing their energy consumption, contributing to cost savings and sustainability efforts.