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?
