predicting unimpaired flows with machine learning methods
two Rmarkdown files record this study. 1) "data preperation" in the intermediary processing folder records the process of gathering the input variables and formatting them into the rfdf dataframe 2) "machine learning" in the main folder records the visualizations of the data in rfdf, the builidng of the machine learning model, predicting to test set, calculating measures of fit and benchmarking the study.
the input data was to large to hot on Github, but can be downloaded by folowing the instruction in the data_prep.rmd file. All sources of data are available to the public.
the output visualizations were included in the presentation and poster.
contact the author at: elawhite@ucdavis.edu