# Data modelling challenge.

__author__ = 'Remus Knowles <remknowles@gmail.com>'

import pandas as pd

F_DATA = r'data challenge test.csv'
F_CORRELATIONS = r'correlations.csv'

def main():
	df = pd.read_csv(F_DATA)
	df.drop('id', axis=1, inplace=True)

	# Split off the data we want to put in to our model.
	df = df[:60]
	df_to_predict = df[60:]

	# Check for any obvious correlation in explanatory variables.
	print df.corr().to_csv(F_CORRELATIONS)

if __name__ == '__main__':
	main()
@@ -345,108 +345,203 @@
 %5D%0A%0A%09
-# Check for any obvious correlation in explanatory variables.%0A%09print df.corr().to_csv(F_CORRELATIONS
+x = df%5B'groups'%5D%0A%09y = df.drop('groups', axis=1)%0A%0A%09x_train = x%5B:50%5D%0A%09x_test = x%5B50:%5D%0A%09y_train = y%5B:50%5D%0A%09y_test = y%5B50:%5D%0A%0A%09print len(x_train), len(x_test)%0A%09print x_train.head()%0A%09print y_train.head(
 )%0A%0Ai

split data in to training and test sets