Body: <p>I have a customer purchasing dataset and the data set is from a retailer having an online store and offline stores. So, customers have two options in their shopping channel, online or offline. In an online shopping, there is a shipping fee however if a basket size is larger than $50 there is no shipping fee. </p>
  
  <p>I found pieces of evidence that customers are trying to add some of items to make their basket size larger than $50 when their baskets are near and a little bit below the $50, because their shipping fee can be waived by doing that.</p>
  
  <ul>
  <li>In this situation, I am trying to identify and characterize items that were purchased only because of the shipping threshold by using a machine learning algorithm. </li>
  </ul>
  
  <p>If there is no shipping threshold, $50, the customers would not purchase the items, but they purchased some items to make their basket size larger than $50. I have not observed those kinds of items (added items because of the shipping threshold). </p>
  
  <ul>
  <li>Is there any machine learning algorithm that I can identify those kinds of items? </li>
  </ul>
  
  <p>I think I need to use some of unsupervised machine learning algorithm.</p>
  
  <p>Another challenging part is that each customer has different characteristics so I probably need to consider it as well. How can I detect those kinds of items??</p>
  
Tags: <machine-learning><unsupervised-learning><pattern-recognition>
Title: Detect observations under certain conditions
UserDisplayName: John legend2
ThreadText: # Detect observations under certain conditions
  
  (Asked by: username_0 on 2018-02-21)
  
  I have a customer purchasing dataset and the data set is from a retailer having an online store and offline stores. So, customers have two options in their shopping channel, online or offline. In an online shopping, there is a shipping fee however if a basket size is larger than $50 there is no shipping fee.
  
  I found pieces of evidence that customers are trying to add some of items to make their basket size larger than $50 when their baskets are near and a little bit below the $50, because their shipping fee can be waived by doing that.
  
  * In this situation, I am trying to identify and characterize items that were purchased only because of the shipping threshold by using a machine learning algorithm.
  
  If there is no shipping threshold, $50, the customers would not purchase the items, but they purchased some items to make their basket size larger than $50. I have not observed those kinds of items (added items because of the shipping threshold).
  
  * Is there any machine learning algorithm that I can identify those kinds of items?
  
  I think I need to use some of unsupervised machine learning algorithm.
  
  Another challenging part is that each customer has different characteristics so I probably need to consider it as well. How can I detect those kinds of items??
  
  **Tags:** machine-learning, unsupervised-learning, pattern-recognition
  
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  ## 0 answers
  
  