I am working on my thesis but I am stuck on the last task to solve.
STEP 1) I have a dataset composed of three variables:
fidelity_card_ID: fidelity card associated to the purchases
shopping_date: day when the purchases were made
cluster: express the pattern of this shopping visit
Examples of clusters description are: shopping for clothes, shopping for housecleaning products, shopping for a meal, shopping for weekly grocery, etc.
STEP 2) Each
fidality_card_ID has a unique profile in terms of clusters composition.
For example, 100% of shopping visits made by
fidelity_card_ID == 1 are clustered as "shopping for clothes". On the other hand, there is
fidelity_card_ID == 2 which 99% of shopping visits were clustered as "shopping for housecleaning products" and there is 1% of shopping visits clustered as "shopping for a meal".
STEP 3) What is the correct approach to develop a model to classify/predict/detect for each fidelity_card_ID those shopping vists that do not belong to the recurrent pattern of that specific fidelity_card_ID?
For example, this model should "highlight" the 1% of shopping visits clustered as "shopping for meal" of
fidelity_card_ID == 2 and it should "not highlight" any shopping visit made by
fidelity_card_ID == 1 because they all belongs in its recurrent pattern.
One of the possible object is understand whether there are several different people sharing the same fidelity_card_ID.