The Smart Campus Energy project focuses on predicting energy consumption in a campus using machine learning techniques. It uses data such as temperature, humidity, time, and occupancy to estimate how much energy will be used. Two algorithms, Linear Regression and Random Forest Regressor, are applied to build the prediction model. Linear Regression helps understand the relationship between variables, while Random Forest improves accuracy by handling complex patterns. This project helps in efficient energy management, reducing wastage, and supporting sustainable campus operations.

Inspiration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Smart campus energy

Built With

• jupyter
• numpy
• panda
• python