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I have extracted data from 4 satellites and ground data (for the past 10 years) of rainfall in excel files separately . Now i need to compare each satellite data with the ground data and find the difference and similarity between them. For example: if in satellite data the column "dog" has 100 values then i need to compare that column to the "dog" column in ground data using RNN. I need to compare each satellite data to the ground data one by one and find the similarity percentage and the difference percentage. If you guyz point me to some helpful resources or help me to get started, it will be much appreciated. I am kinda new to machine learning and this is for my masters research. Thank you.

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    $\begingroup$ Why would you use machine learning and even more so RNN for such a task? Is there something else you would like to do too, with your model? Comparing of two arrays can be done with functions or even excel tools, when you choose the metric you would like to base the similarity on. If you just need to compare column tables, machine learning is a huge overkill $\endgroup$ – Nikos Jul 17 at 7:30
  • $\begingroup$ Even I insist to try hands on some similarity metrics rather than jumping to ML algorithms. $\endgroup$ – Shubham Panchal Jul 17 at 14:21
  • $\begingroup$ I want to use Machine learning bcz i have huge data sets and i need machine learning model to train by this data then i will be using data from 8 satellites and ground data for asia and predict floods and rainfall for different regions. We have done this work in Matlab but this time we want to use Machine learning (paper published ). My part was to extract the data from gound and satellite (which i have done and saved it in excel file) and then run it through RNN. I just need a little guidance we have around 3 months to do it. $\endgroup$ – Arsalan khan Jul 17 at 16:30

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