Update machine-learning-sample-prediction-of-number-of-bike-rentals.md

'hour' was missing in: Features D capture demand for bikes at particular hour and particular day of the week.
MicrosoftDocs · Jul 15, 2014 · 542ae6b · 542ae6b
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diff --git a/articles/machine-learning-sample-prediction-of-number-of-bike-rentals.md b/articles/machine-learning-sample-prediction-of-number-of-bike-rentals.md
@@ -17,7 +17,7 @@ We used 2011 data as a training set and 2012 data as a test set. We compared 4 s
 1. number of bikes that were rented in each of the previous 12 days at the same hour
 1. number of bikes that were rented in each of the previous 12 weeks at the same hour and the same day
 
-Features B capture very recent demand for the bikes. Features C capture demand for bikes at particular hour. Features D capture demand for bikes at particular and particular day of the week.
+Features B capture very recent demand for the bikes. Features C capture demand for bikes at particular hour. Features D capture demand for bikes at particular hour and particular day of the week.
 
 Since the label (number of rentals) is real-valued we have regression setting. Also, since the number of features is relatively small (less than 100) and they are not sparse, the decision boundary is probably nonlinear. Based on this, we decided to use boosted decision tree regression algorithm.