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We have data consist of previous transaction history consisting of Date,Order-id, Product-id, Product name, ordered or not. We need to predict a specific product probability for all the customers that will buy or not

  1. What model will be apt for this scenario?
  2. What features we need to consider?

I am a beginner and having difficulty in coming up with a model. Any input is highly appreciated.

Thank You

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2 Answers 2

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There don't seem to be many variables involved. Since you're asking for the probability of customers buying a product, You could use a simple logistic regression model. It's used for classification using the sigmoid curve. It will give you a cutoff probability, above which you can consider customer would buy a product.

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When you are providing only one option (will it sell or not). This is assuming there are no consumer choices that may not be a correct assumption. You're model is more closely related to a coin flip. You have one choice (you have no choice).

If the question is closer to, "Given two or more products what is the likelihood a consumer would purchase A over B (over C (and on)).

When you ask this you are looking at receipts, historical (assuming you have enough to go on) and you are also building some simple A/B testing against your mystery product.

What model will be apt for this scenario?

Your potential models for A/B testing could be: The frequentist approach or Bayesian statistics approach.

What features we need to consider?

Historical data. Consumer Options. A/B testing (a hypothesis).

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  • $\begingroup$ Hi @corpus-callosum, main focus is suppose I am having n unique user-id with some different transactions for the product named as apple,orange,mango next time while purchasing what will be the probability for these three products to buy, based on the historical data of purchase. For this how i can proceed with machine learning further $\endgroup$
    – User
    Jul 7 at 9:25

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