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

enter image description here

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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  • $\begingroup$ I have also encountered similiar scenario but in a different domain, have you soloved the problem finally with the small amount of data? How did you solve it? Was it necessary to utilize machine learning model? Thank you. $\endgroup$
    – Anaconda
    Apr 20, 2021 at 2:48
  • $\begingroup$ @Anaconda unfortunately with data this little only linear regression was possible, which ultimately is not the model that can successfully predict the next outcome $\endgroup$
    – Ach113
    Apr 20, 2021 at 5:33

1 Answer 1

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You might find the link helpful.

https://towardsdatascience.com/how-to-model-time-series-data-with-linear-regression-cd94d1d901c0

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