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Saturday, October 26, 2019

Decision Trees for Shopping

Perhaps simplistic, but interesting.   Even if you have to ultimately complicate such a model, its useful to start with something this simple and see how well it can predict, then add more complexity later.  In the enterprise, we did lots of that.  It can also act as a benchmark for more complex models.  Decision trees are also nicely transparent.

Decision Trees for Online Shopping Analysis
Towards Data Science by Chathuranga Siriwardhana  

Nowadays there is a trend to use online shopping solutions like Amazon, eBay, AliExpress. These websites provide a platform for the sellers to sell their products to a large number of customers. Since many delivery services are connected with these online shopping platforms, customers from different countries buy products. Unlike the traditional shops, the ratings and the good-name is directly represented on the shopping platform for each seller. Therefore the sellers have let the customers return their bought items if they don’t like the product or there is any defect of the item. Some sellers refund the whole amount if the customers complain that the items are not delivered within the promised period. Some customers are misusing these facilities and fraud to the sellers. Therefore, the sellers on the online shopping platforms experience a huge loss of profits. Let’s discuss how we can spot these types of customers by developing a simple Machine Learning model; a Decision Tree.

Have a look at this medium post on Decision Trees if you are not familiar with them. For a quick recap, a decision tree is a model in machine learning which includes the conditions on which we are categorizing the data (for labelling problem). As an example, think about a simple situation where a man is happy is the weather is sunny or he is on vacation. This scenario is modelled below. Note that you can use weather and vacation status to predict the man’s happiness with this model.  ... "

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