The study is focused on using machine learning to assess the cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.
The dataset was obtained from the UCI Machine learning repository https://archive.ics.uci.edu/ml/datasets/energy+efficiency
The dataset contains eight attributes (or features, denoted by X1...X8) and two responses (or outcomes, denoted by Y1 and Y2). The aim is to use the eight features to predict the response, Cooling Load(Y2).
X1 - Relative Compactness
X2 - Surface Area
X3 - Wall Area
X4 - Roof Area
X5 - Overall Height
X6 - Orientation
X7 - Glazing Area
X8 - Glazing Area Distribution
y1 - Heating Load
y2 - Cooling Load
The GradientBoostingRegressor was selected as the best model for predicting the cooling load of buildings based on the given parameters of this dataset. This model was selected because it gave the highest r2_score of 0.99.