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Not normally distributed data
I'm trying to find a model to make a prediction for the execution time of a process step. I've data from over 200 different recurring process steps from the past 2 years (160.000 rows in excel sheet). When I plot the execution-time data per event, the data is not normally distributed but more like a Poisson distribution. Just loading the data in Rapidminer Studio and applying the models do not return a good fit. What can i do? (for data pre-processing in Python or R I would need a step-by-step guide because I'm pretty new in all of this)
Some help would really be appreciated!