You are facing a regression problem: your aim is to predict the value of a continuous variable given the value of a set of input variables (these input variables can be of any type: numbers, categories, etc.) A decision tree is usually applied to classification problems, in which you are aiming at predicting a discrete value.
There are several regression methods in sklearn that you could use. The simplest ones, and maybe the ones you should start from, are linear models: http://scikit-learn.org/stable/modules/linear_model.html.
Notice that you may need to transform your input data. For instance, if one of the features is categorical, you may need to transform it into a set of binary variables. Other processes, like data normalisation, may also be advisable in order to get better results.
Edit: as stated in the comments, Decision Trees can also be applied to regression problems. However, and in my own experience, the output curve that you obtain by means of this algorithm usually has a step-wise shape that may affect the final bias (see, for instance the example in the scikit-learn docs.). I would suggest not to constraint yourself and try different types of algorithms.