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I have been given a task with a real transaction dataset. The task is to predict something using either logistic regression or simple binary classification.

The columns are as follow:

  • Transaction ID
  • Quantity purchased
  • Product name
  • Coupon code
  • Transaction Date
  • City (where transaction was made)
  • Delivery fee (if any)
  • Total amount spent

I am having a rough time figuring out what to predict using regression or classification given only these columns.

i.e: Given a full row how much is the total spent... etc

In other words I need help deciding what will be the label of my dataset and what would be the reason behind choosing that label.

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  • $\begingroup$ Do you want to know what is the output variable in the dataset? $\endgroup$
    – hanugm
    Nov 30 '21 at 22:53
  • $\begingroup$ @hanugm Essentially I am looking to predict 1 column from the others so what would be the best label given this dataset as it seems you can't really predict any of them as its only transaction with no mention of customer data. $\endgroup$ Nov 30 '21 at 22:56
  • $\begingroup$ Oh, you want to decide the classifying label without data. $\endgroup$
    – hanugm
    Nov 30 '21 at 22:57
  • $\begingroup$ @hanugm I have the data but the columns seem very unrelated and the task I was given says I need to predict one of the columns given the others which looks very unlikely to me. $\endgroup$ Nov 30 '21 at 23:00

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