Row 5760

Row ID: 5760 | Dataset Entry | Axioma AXP Content Repository

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Suppose I’m trying to predict churn based on previous purchases information. What I do today is come up with features like average spend, count of transactions and so on. I want to instead treat the problem as a sequence one, modeling the sequence of transactions using NN.

The problem is that some users have 5 purchases, while others 15. How to handle this input size change from user to user, and more importantly which architecture to use?

Thanks!!

FieldValue
text Suppose I’m trying to predict churn based on previous purchases information. What I do today is come up with features like average spend, count of transactions and so on. I want to instead treat the problem as a sequence one, modeling the sequence of transactions using NN. The problem is that some users have 5 purchases, while others 15. How to handle this input size change from user to user, and more importantly which architecture to use? Thanks!!
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-03
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url_encoded Z0FBQUFBQm5Lak9HSVRXblM1M0dSNHNoTER6aEQ4OENoR2Q1MnZLUWxZRmJKc1Z0YTdicW9sYVZtRjlzZllKZTE2Vmo5Vmh3blN6eW5mOTZCSFRmdGUxVlo4ODJ6UjgxdlVkNjJQekVpWGIxcG15T2ViQVVUeGo2N3pXTnowNFd2cmVpVXVYTDR4Z1JrN2dUZ3hWZnNFZV9raUlFM1hVclY2enhXZUpQY3hLbEl6SWxKZTRCSW5ZSjZIbzNfQTV3UFlRckkwT2R6a0FM

Raw Record

{
  "text": "Suppose I’m trying to predict churn based on previous purchases information. What I do today is come up with features like average spend, count of transactions and so on. I want to instead treat the problem as a sequence one, modeling the sequence of transactions using NN. \n\nThe problem is that some users have 5 purchases, while others 15. How to handle this input size change from user to user, and more importantly which architecture to use?\n\nThanks!!",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-03",
  "username_encoded": "Z0FBQUFBQm5LakwyblNoRkhzWUJaYWtMMnA2V2MxazY2SmxQNmk1VVpBdmpKNEpNbktOc1YzaXFXTXVXRDFkeXVzcWJJMDktVExHMmt0cXRKMnZNMzV2ci1KcklPblhOQS1GakZVdDdKZ2NxTFFwOWQ2a3p4dzA9",
  "url_encoded": "Z0FBQUFBQm5Lak9HSVRXblM1M0dSNHNoTER6aEQ4OENoR2Q1MnZLUWxZRmJKc1Z0YTdicW9sYVZtRjlzZllKZTE2Vmo5Vmh3blN6eW5mOTZCSFRmdGUxVlo4ODJ6UjgxdlVkNjJQekVpWGIxcG15T2ViQVVUeGo2N3pXTnowNFd2cmVpVXVYTDR4Z1JrN2dUZ3hWZnNFZV9raUlFM1hVclY2enhXZUpQY3hLbEl6SWxKZTRCSW5ZSjZIbzNfQTV3UFlRckkwT2R6a0FM"
}
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