We will explore basic regression algorithms through an analysis of an energy efficiency dataset (Tsanas and Xifara, 2012). We will investigate the heating and cooling load requirements of the buildings based on their construction characteristics, such as surface, wall, and roof area; height; glazing area; and compactness. The researchers have used a simulator to design 12 different house configurations, while varying 18 building characteristics. In total, 768 different buildings were simulated.
Our first goal is to systematically analyze the impact that each building characteristic has on the target variable, that is, the heating or cooling load. The second goal is to compare the performance of a classical linear regression model against other methods, such as SVM regression, random forests, and neural networks. For this task, we will use the Weka library.
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