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Author: Jared Weed

projects/creating_customer_segments/customer_segments.ipynb

@@ -667,7 +667,7 @@
    },
    "source": [
     "### Question 10\n",
-    "Companies often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether that change affects its customers positively or negatively. The wholesale distributor wants to consider changing its delivery service from 5 days a week to 3 days a week, but will only do so if it affects their customers positively. *How would you use the customer segments you found above to perform an A/B Test for this change?*  \n",
+    "Companies often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether that change affects its customers positively or negatively. The wholesale distributor wants to consider changing its delivery service from 5 days a week to 3 days a week, but will only do so if it affects their customers positively. *How would you use the customer segments you found above to perform an A/B test for this change?*  \n",
     "**Hint:** Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?"
    ]
   },
@@ -683,7 +683,7 @@
    "metadata": {},
    "source": [
     "### Question 11\n",
-    "Additional structure is derived from originally unlabelled data when using clustering techniques. Since each customer has a segment it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and has made estimates for each customer's annual spending of the six product categories. Knowing these estimates, the wholesale distributor wants to classify each new customer to one of the customer segments to determine the most appropriate delivery service.  \n",
+    "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a segment it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and has made estimates for each customer's annual spending of the six product categories. Knowing these estimates, the wholesale distributor wants to classify each new customer to one of the customer segments to determine the most appropriate delivery service.  \n",
     "*Describe a supervised learning strategy you could use to make classification predictions for the ten new customers.*  \n",
     "**Hint:** What other input feature could the supervised learner use besides the six product features to help make a prediction?"
    ]