"This video is about running a python script from UiPath and passing back and forth values, from the UiPath workflow to the python script, and back.
The python script is a simple linear regression model that is trained on the Boston housing dataset from 1970 and is basically predicting a house price based on a set of 13 input features, like the ""per capita crime rate by town"", or the ""average number of rooms per dwelling"" or the ""pupil-teacher ratio by town"". The 13 arguments are passed from the UiPath workflow to the python script, and the python script returns to the UiPath workflow the predicted house price.
00:01 Topic and summary of the video
00:39 UiPath Hacks intro theme
01:03 Using python with pyinstaller. Making an exe file that does not need python installed on the Virtual Machine that runs the process.
02:03 Using the python activities in UiPath - prerequisites
02:28 Example of using python for a linear regression model on the Boston housing dataset, predicting a house price based on a set of 13 input features, and returning the house price to UiPath
07:07 Wrap-up and conclusion
The Boston house price dataset can be found below:
https://www.kaggle.com/vikrishnan/bos..."
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