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So let's say I create a logistic model to predict who will open a loan based on a based email list that includes who opened and who didn't that's 90% accurate. The model says age, income, bank engagement are three key variables that decide who opens a loan. Is there a way to apply this model to a different email list with the same variables to predict who will open a loan? Or what percentage of the people will open the loan? Or will I just need to analyze the data on the list myself to determine this.

Sorry, probably a dumb question, but it's one thing I've struggled to figure out on my data scientist journey.

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Yes, you can predict who will open a loan with this model. You have to keep several things in mind. The model expects the new data to be from the same distribution as the training data, meaning the variables have the same distribution and targets as well.

You can not train on one set where 3% opened a loan and then predict on a set where you expect that 90% open a loan. This data is then too different for the model to accurately predict on.

Remember that your model only has 90% accuracy, so it is not guaranteed that a specific costumer will open a loan when the model predicts so.

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