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Suggested way of data introduction, coupled with running capabilities.Data is inserted through a single file, either a py that contains a dictionary or a .json/csv. The data extraction happens on a different file and form there, the data are handled across all functions and classes. This file contains all of the requirements for the execution of the program (models to use, plots to have etc. and should also include any input parameters you want). As this file will, eventually, acquire huge dimensions, you should use more default values in your code to decrease clutter and increase readability. |
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Data insertion methodologyEach model file has been converted to a function with a series of inputs and was renamed as such: "_main.py". On the same folder, a different file was created, dubbed: "_execution.py". The execution file contains a dictionary that houses all of the primary inputs for the model (i.e. for the polynomial regression: degree of fit, for GBR: the depth of each tree etc.). Along with this primary dictionary, secondary inputs are also given (i.e. true/false statements to control save, plotting and other parameters like the execution device for the ANN etc.). All of the above are inserted into the function and used inside the main file. |
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Maybe insert data through a csv file? Or some other way?
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