Merge pull request #369 from alonnir/patch-1

typo

docs/Quickstart.md

@@ -26,7 +26,7 @@ For all models, the following nomenclature is used:
 - `recency` represents the age of the customer when they made their most recent purchases. This is equal to the duration between a customer's first purchase and their latest purchase. (Thus if they have made only 1 purchase, the recency is 0.)
 - `monetary_value` represents the average value of a given customer's purchases. This is equal to the sum of all a customer's purchases divided by the total number of purchases. Note that the denominator here is different than the `frequency` described above.
 
-If your data is not in the format (very common), there are [utility functions](#example-using-transactional-datasets) in lifetimes to transform your data to look like this
+If your data is not in the format (very common), there are [utility functions](#example-using-transactional-datasets) in lifetimes to transform your data to look like this.
 
 #### Basic Frequency/Recency analysis using the BG/NBD model
 
@@ -59,7 +59,7 @@ For small samples sizes, the parameters can get implausibly large, so by adding
 
 ##### Visualizing our Frequency/Recency Matrix
 
-Consider: a customer bought from you every day for three weeks straight, and we haven't heard from them in months. What are the chances they are still "alive"? Pretty small. On the other hand, a customer who historically buys from you once a quarter, and bought last quarter, is likely still alive. We can visualize this relationship using the **Frequency/Recency matrix**, which computes the expected number of transactions a artificial customer is to make in the next time period, given his or her recency (age at last purchase) and frequency (the number of repeat transactions he or she has made).
+Consider: a customer bought from you every day for three weeks straight, and we haven't heard from them in months. What are the chances they are still "alive"? Pretty small. On the other hand, a customer who historically buys from you once a quarter, and bought last quarter, is likely still alive. We can visualize this relationship using the **Frequency/Recency matrix**, which computes the expected number of transactions an artificial customer is to make in the next time period, given his or her recency (age at last purchase) and frequency (the number of repeat transactions he or she has made).
 
 ```
 from lifetimes.plotting import plot_frequency_recency_matrix