Updated: XGBoost direct marketing notebook markdown at the end

Author: djarpin

introduction_to_applying_machine_learning/xgboost_direct_marketing/xgboost_direct_marketing_sagemaker.ipynb

@@ -570,7 +570,7 @@
     "\n",
     "## Extensions\n",
     "\n",
-    "This example analyzed a relatively small dataset, but utilized Amazon SageMaker features such as distributed, managed training and highly available, autoscaling model hosting, which could easily be applied to much larger problems.  Please check out the other Amazon SageMaker direct marketing notebook for a more detailed walkthrough of improvements that could be made to the model (in particular tuning the model for better accuracy) and discussion of gradient boosting versus similar algorithms."
+    "This example analyzed a relatively small dataset, but utilized Amazon SageMaker features such as distributed, managed training and real-time model hosting, which could easily be applied to much larger problems.  In order to improve predictive accuracy further, we could explore techniques like hyperparameter tuning, as well as spend more time engineering features by hand.  In a real-worl scenario we may also look for additional datasets to include which contain customer information not available in our initial dataset."
    ]
   },
   {