Clean up documentation

docs/user-guide/feature-retrieval.md

@@ -55,7 +55,7 @@ Historical feature retrieval can be done through either the Feast SDK or directl
 features = self._features + [self._target]
 
 # Retrieve training dataset from Feas. The "entity_df" is a dataframe that contains
-# timestamps and entity keys. In this case it is a dataframe with two columns.
+# timestamps and entity keys. In this case, it is a dataframe with two columns.
 # One timestamp column, and one customer id column
 dataset = client.get_batch_features(
     feature_refs=features,
@@ -71,7 +71,7 @@ In the above example, Feast does a point in time correct query from a single fea
 
 This is called a point in time correct join.
 
-Feast allows users to retrieve features from any feature sets and join them to gether in a single response dataset. The only requirement is that the user provide the correct entities in order to look up the features.
+Feast allows users to retrieve features from any feature sets and join them together in a single response dataset. The only requirement is that the user provides the correct entities in order to look up the features.
 
 ###  **Point-in-time-correct Join** 
 
@@ -81,14 +81,15 @@ Below is another example of how a point-in-time-correct join works. We have two
 
 ![Input 2: Driver DataFrame](https://lh3.googleusercontent.com/LRtCOzmcfhLWzpyndbRKZSVPanLLzfULoHx2YxY6N3i1gQd2Eh6MS1igahOe8ydA7zQulIFJEaQ0IXFXOsdkKRobOC6ThSOnT4hACbCl1jeM4O2JDVC_kvw8lwTCezVUD3d6ZUYj31Q)
 
-Typically the `input 1` DataFrame would be provided by the user, and the `input 2` DataFrame would already be ingested into Feast. In order to join these two, the user would call Feast as follows:
+Typically the `input 1` DataFrame would be provided by the user, and the `input 2` DataFrame would already be ingested into Feast. To join these two, the user would call Feast as follows:
 
 ```python
 # Feature references
 features = [
  'conv_rate',
  'acc_rate',
  'avg_daily_trips',
+ 'trip_completed'
  ]