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+Data Science Dojo <br/>
+Copyright (c) 2016 - 2019
+
+---
+
+**Level:** Intermediate <br/>
+**Recommended Use:** Regression/Classification Models<br/>
+**Domain:** Energy<br/> 
+
+## Energy Efficiency Data Set 
+
+### Assess heating and cooling load requirements of building 
+
+
+---
+![](OFASNQ0.jpg)
+---
+
+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)](https://archive.ics.uci.edu/ml/datasets/Energy+efficiency).<br/> 
+The UCI page mentions the following publication as the original source of the data set:<br/> 
+[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](http://people.maths.ox.ac.uk/tsanas/Preprints/ENB2012.pdf).