Fixed: Another type in xgboost_customer_churn

Author: djarpin

introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn.ipynb

@@ -502,7 +502,7 @@
     "\n",
     "An important point here is that because of the `np.round()` function above we are using a simple threshold (or cutoff) of 0.5.  Our predictions from `xgboost` come out as continuous values between 0 and 1 and we force them into the binary classes that we began with.  However, because a customer that churns is expected to cost the company more than proactively trying to retain a customer who we think might churn, we should consider adjusting this cutoff.  That will almost certainly increase the number of false positives, but it can also be expected to increase the number of true positives and reduce the number of false negatives.\n",
     "\n",
-    "To get a rought intuition here, let's look at the continuous values of our predictions."
+    "To get a rough intuition here, let's look at the continuous values of our predictions."
    ]
   },
   {