CODEWITHSUNDEEP
algorithmmachine-learningregressioncluster-analysisdata-mining
Keithx
Keithx

Reputation: 3158

Clustering+Regression-the right approach or not?

I have a task of prognosing the quickness of selling goods (for example, in one category). E.g, the client inputs the price that he wants his item to be sold and the algorithm should displays that it will be sold with the inputed price for n days. And it should have 3 intervals of quick, medium and long sell. Like in the picture:enter image description here

The question: how exactly should I prepare the algorithm?

My suggestion: use clustering technics for understanding this three price ranges and then solving regression task for each cluster for predicting the number of days. Is it a right concept to do?

Upvotes: 0

Views: 126

Answers (2)

etov
etov

Reputation: 3032

There are two questions here, and I think the answer to each lies in a different domain:

  1. Given an input price, predict how long will it take to sell the item. This is a well defined prediction problem, and can be tackled using ML algorithms. e.g. use your entire dataset to train and test a regression model for prediction.
  2. Translate the prediction into a class: quick-, medium- or slow-sell. This problem is product oriented - there doesn't seem to be any concrete data allowing you to train a classifier on this translation; and I agree with @anony-mousse that using unsupervised learning might not yield easy-to-use results.

You can either consult your users or a product manager on reasonable thresholds to use (there might be considerations here like the type of item, season etc.), or try getting some additional data in order to train a supervised classifier.

E.g. you could ask your users, post-sell, if they think the sell was quick, medium or slow. Then you'll have some data to use for thresholding or for classification.

Upvotes: 1

Has QUIT--Anony-Mousse
Has QUIT--Anony-Mousse

Reputation: 77474

I suggest you simply define thesholds of 10 days and 31 days. Keep it simple.

Because these are the values the users will want to understand. If you use clustering, you may end up with 0.31415 days or similar nonintuitive values that you cannot explain to the user anyway.

Upvotes: 0

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