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ENB2012_data.csv
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README.md

Data Science Dojo
Copyright (c) 2016 - 2019


Level Intermediate
Recommended Use: Regression/Classification Models
Domain: Energy

Energy Efficiency Data Set

Assess heating and cooling load requirements of building


This intermediate level data set has 768 rows and 10 columns. This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters Energy analysis using 12 different building shapes simulated in Ecotect has been performed. The buildings differ with respect to the glazing area, the glazing area distribution, and the orientation, amongst other parameters. It can also be used as a multi-class classification problem if the response is rounded to the nearest integer. This data set is recommended for learning and practicing your skills in exploratory data analysis, data visualization, regression and classification modelling techniques. Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following data dictionary gives more details on this data set:


Data Dictionary

Column Position Atrribute Name Definition Data Type Example % Null Ratios
1 X1 Relative Compactness Quantitative 0.86, 0.71, 0.79 0
2 X2 Surface Area Quantitative 588.0, 689.0, 710.5 0
3 X3 Wall Area Quantitative 318.5, 416.5, 294.0 0
4 X4 Roof Area Quantitative 110.25, 147.00, 122.50 0
5 X5 Overall Height Quantitative 7.0, 3.5 0
6 X6 Orientation Quantitative 2, 3, 5 0
7 X7 Glazing Area Quantitative 0.1, 0.25, 0.4 0
8 X8 Glazing Area Distribution Quantitative 0, 2, 4 0
9 Y1 Heating Load Quantitative 10.43, 11.69, 11.09 0
10 Y2 Cooling Load Quantitative 13.71, 14.45, 19.34 0

Acknowledgement

This data set has been sourced from the Machine Learning Repository of University of California, Irvine Energy Efficiency Data Set (UC Irvine). The UCI page mentions the following publication as the original source of the data set: 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 (the paper can be accessed from.