0
$\begingroup$

I am working on a time series classification problem to identify what items customers would buy in their next order (customers orders different products every week).

Let's say we have a customer who orders every week but different items. Overall, this customer ordered 1k items in the past, and my task would be to identify approximately 80-120 unique items (number of items customer order each week was between 80-120) from these 1k items.

Right now, I am using the xgboost classifier, and my precision is close to 70%, and recall is 65%. I understand xgboost cannot handle seasonality by default. So I created additional features like a week of the month, week of the year, season (Spring/summer/winter/fall), etc.

I wanted to know if anyone in this community worked on a similar kind of problem. I have seen lot of peoples posting same kind of problem but not sure whether they ended up being helped/ moving it to production.

$\endgroup$
0
$\begingroup$

Try association rules, they will help you in finding the right set of rules like what is chance of customer buying X if he purchased A, B, C in the past.

Also from my personal experience, these kinds of tasks are more business need driven. Like sales capture from the item. So even if your model is 60% accurate but captures 90% sales, you should be good to go. Try to give it a thought in this way.

$\endgroup$
  • $\begingroup$ Thanks, @shivam shah I already have association apriori algorithm integrated to my ML model and coupling items customers bought together more times. 60% of accuracy might be good to go in most of the cases like in the recommendation system, but I am trying to automate the whole ordering system with better precision. $\endgroup$ – BANDI HEMANTH Oct 4 at 2:59

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.