Main program is called

Metodos.py

The GUI is divided into two parts.

• The first consists of a compilation of the graphs most used in exploratory analysis. In two of them, you must select a main variable.
• The second part is about calculations and some data anaysis. You can find the data distribution by two ways and using sweetviz you can see a dashboard in html format with all the summary about the dataframe. This package is widely used in data analysis.

In this case, three methods of machine learning are used:

• Random Forest -----> For more info about this method check Random Forest
• Ridge y Lasso -----> For more info about this method check *Ridge
• kNN -----> For more info about this method check *kNN

In * you can find, in addition to the explanation of the method, a Python code as an example.

Finally, there is a group box called options where you can define the percentage of data to train in the model as well as define the name of the excel or txt according to the chosen output.

If you choose a machine learning model, the output will be an excel file with three sheets and an extra one if you want it (Descriptive statistics). With linear regression a txt file is displayed because statsmodel is used.

The GUI is in Spanish and some codes have comments for your better understanding.

If you choose more than one action, the application will probably freeze for a few seconds until all operations are finished. This is normal because after a large number of actions the QT application tends to freeze and needs to be optimized.