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I have a data set with transactions details from different business (roughly 1 thousand business entities). Each row is a transaction. The structure of the dataset is as follows:

client_id Sex Age transaction_ammount business_entity
123 M 88 4829 storeA
123 M 88 1049 storeB
255 F 25 1122 storeH

My goal is to cluster the clients depending on their consuming habits, age and sex.

I am having a hard time on deciding on the best features to feed this dataset into a clustering algorithm (probably K-means as a starter).

Some of the things I am planning to do are:

  • One hot encoding on: sex
  • Make each store be a column and each row value be the amount of transactions a certain user did pay to that store (for example, if user1 made two transactions to storeB, there will be a 2 in the user1 row on the storeB column).

One of the main things I am struggling with right now is how to sum the transactions data per user. I would need to run an aggregated operation on them, but don't know which one would be better. Some of the ones I have in my mind:

  • Average transaction amount per user
  • Min transaction amount per user
  • Max transaction amount per user
  • The above 3 but for each store per user (which would mean that, if I have 1000 stores, I would have to add 3000 thousand columns). This makes sense since each store have a wide range of product prices and running an operation among all the transactions of a user will be misleading.

What feature engineering technique would you recommend me? Is there any additional data wrangling I should do?

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1 Answer 1

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Depending on your processing limitations I'd be tempted to do the following, for each client a single row consisting of:

  • Age
  • Sex (one hot encoded)
  • Per store:
    • Purchase Count
    • Total Purchase Value
    • Average Purchase Value
    • Min Purchase Value
    • Max Purchase Value

Additionally if you have the transaction dates I'd try to include things like:

  • Days since first purchase
  • Avg. days between purchases

Hopefully that helps you a bit, good luck!

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