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I am working on a project 'Rossmann Sales prediction', in which I have to forecast the sales of Rossmann Stores. So it is a supervised ML problem. I applied random forest. But then in interviews question arises like - 1) why you did not apply Linear Regression? 2) Why you directly applied random forest? 3) Why did you not choose boosting technique?

So how to defend this kind of question? Which algorithm works well and what reason to choose that algorithm?

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There is no definite answer to this question. Usually all algorithms are tried and the best performing algo is selected.

But to answer your question, it depends on the type of data you are working with and it's size. Below is a flowchart that guides you on what algo to choose for your dataset. (Keep in mind that the flowchart is to be taken with a pinch of salt).

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It is always good to check all available options before saying that this particular algo works the best for my dataset.

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