Which algorithm to use to predict the duration of some task I have a data set of 36K processes each with 7 features. Each set represents a task and I know how long it took to complete each of the tasks. I want to build a model which will be able to predict the duration of the future tasks.

I've only learned about Decision Tree (DT) and tried to apply it to my problem. The resulting accuracy score is 0.03. I believe DT is not suitable because the time is continuous and DT is meant for categorization.

Which algorithm is suitable for duration prediction?

My environment: Python with sklearn, if that matters.
What others have said is accurate, that you need to build a regression model of some sort. Depending on the scale of the duration of your task, you will have to model it slightly differently. 

In fact, there's an entire class of models that try to predict duration. These are called survival models. Here's a Python library on survival analysis.

But these models are fairly academic. A typical hack is to use Gamma, Poison, or Log-Normal regression which works out nicely because these models predict non-negative values.