This final project aims to develop regression models to evaluate the energy efficiency of residential buildings. Given eight features of a building, we hope to predict the corresponding efficiency that is characterized by two output variables, heat loading and cool loading. We also highlight the important features to provide potential guidelines for the design of energy-efficient residential buildings.

• python version-Python 3.6.5
• Machine learning models-scikit-learn==0.21.3
• Data analysis-pandas==0.25.0
• Linear algebra-numpy==1.16.1
• Visualizaiton-matplotlib==2.0.2
• Feature importance-shap==0.32.1

• The dataset is from UCI Machine Learning Repository
• Relevant Papers: A. Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567, 2012.