Welcome to General Assembly DC's 31st Part Time Data Science Competition. Tonight, you'll be trying to hack away at Shuttle Radiator data trying to classify potential anomalies in the radiator position onboard Space Shuttles.
### NUMBER OF EXAMPLES
	training set     43500
	test set         14500

### NUMBER OF ATTRIBUTES
	9

The shuttle dataset contains 9 attributes all of which are numerical.
The first one being time.  The last column is the class which has been 
coded as follows :
        1       Rad Flow
        2       Fpv Close
        3       Fpv Open
        4       High
        5       Bypass
        6       Bpv Close
        7       Bpv Open
        
Approximately 80% of the data belongs to class 1. Therefore the default accuracy is about 80%. The aim here is to obtain an accuracy of 
99 - 99.9%.   Our evaluation metric is accuracy for this competition.
Acknowledgements
We thank NASA and UCI for providing this dataset.