Predicting Intent to do X with a confidence score or intent percentage score? I have a data set like:

did_purchase  action_1_30d action_2_20d action_2_10d ....
   False            10          20            100
   True            ....etc


Where did_purchase shows whether the customer purchased or not, and the columns indicate the volume of actions taken before the purchase (or non-purchase) event. 

So, for the first row the customer did 10 of action_1 within 30 days of the purchase event, but didn't purchase in the end.

I have been using sklearn's LogisticRegression to predict the did_purchase false/true, and can get about 89% accuracy, which is nice. 

However, I'd like a percentage intent score instead. So it could say user-321 has a 46% chance of purchasing in the next 10 days.

What would be a good algo/approach for this?
You could use the probabilities output by LogisticRegressions predict_proba method.

Almost all classifiers give you a probability in sklearn. One exception is the support vector classifier which will give you a points distance to the decision hyperplane, which can be interpreted as a confidence (you can get probabilities for the support vector classifier, but it is through a computationally costly cross validation process).