I have data of bank branches and amount of revenue they have generated in a month. The data looks like this:

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I am tasked to find the expected revenue for the branch for the next month using machine learning. Initially I was planning to use LSTM networks for such analysis, but I doubt its possible with such small amount of data.

I personally think machine learning is an overkill for such task. What would be the most appropriate way to predict the revenue for next month? I thought about increasing the amount of data by treating every branch as equal and using the row corresponding to each branch as separate instance for training (but I doubt that is a correct approach).

Any advice would be appreciated


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