# Designing a training set for regression on probabiltiy values given time , categorical and continous features

Assume we have following variables out of which "Probability of sale " needs to be predicted , and this is to be done for a portable business vendor whose location changes with time :

Business street address|weather description | Day of Year | Day of Week | Hour of Day | Probability of sale

The above plot shows the average sales count along the time axis.

So how to design the training set for forecasting the sale probability in scikit learn such that it outputs a continous proabability range and thus help in the forecast or probability of sale prediction?

Convert each feature into boolean variables. For example $x_{Main}$ = 1 if street is Main and 0 otherwise and construct a variable like that for every street. The do the same for other variables. If you have a huge number of these new booleans you could eliminate the ones that have for example only a single 1 or only a few ones, or the ones that are all zeros etc...