Explore buildings in NYC on an interactive map and compare each building’s energy usage, by drilling down the energy consumption level on the basis of 4 dimensions: AC, plug equipment, home gas and heating.
Used K-means clustering to group buildings into efficient and inefficient, and built a predictive model whose output identifies if a new building is consuming energy inefficiently. Trained a linear regression model to predict the building’s annual energy bill.
- Data Science Experience (DSX)
Data Science Techniques
- Supervised ML (regression and classification), unsupervised ML (Kmeans)
- Predict a building’s annual energy bill and help customers identify potential savings
- Group buildings into efficient and inefficient
- Identifies if a new building is consuming energy inefficiently
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