""" PROBLEM STATEMENT:
How should we design buildings (with respect to heating load) in the future?
Additional questions:
1. Is there a relationship between building features and heating load?
2. How strong is that relationship?
3. Which are the strongest contributing features to heating load?
4. Can heating load be predicted given a particular building design?
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SOURCE:
http://archive.ics.uci.edu/ml/datasets/Energy+efficiency
The dataset was created by Angeliki Xifara (angxifara '@' gmail.com, Civil/Structural Engineer) and was processed by Athanasios Tsanas (tsanasthanasis '@' gmail.com, Oxford Centre for Industrial and Applied Mathematics, University of Oxford, UK).
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FEATURES INFORMATION:
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 each of the two responses.
Specifically:
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
NOTE:
Feature names were changed to facilitate human readability.
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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
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CITATIONS:
1. 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.
For further details on the data analysis methodology:
2. Tsanas, 'Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear
speech signal processing and statistical machine learning', D.Phil. thesis, University of Oxford, 2012.
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ADDITIONAL SOURCES:
1. Based upon this tutorial by Kevin Markham: https://github.com/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb
"""