Machine Learning, What are the common techniques for feature engineering and presenting the model?
Feature engineering is more of art than technique. That might require domain knowledge or you could try adding, subtracting, dividing and multiplying different columns to make features out of it and check if it adds value to the model. If you are using Linear Regression then the adjusted R-squared value must increase or in the Tree models, you can see the feature importance, etc.
I am having a ML language identification project (Python) that requires a multi-class classification model with high dimension feature input. 
Currently, all I can do to improve accuracy is through trail-and-error. Mindlessly combining available feature extraction algorithms and available ML models and see if I get lucky. 
I am asking if there is a commonly accepted workflow that find a ML solution systematically. 
This thought might be naive, but I am thinking if I can somehow visualize those high dimension data and the decision boundaries of my model. Hopefully this visualization can help me to do some tuning. In MATLAB, after training, I can choose any two features among all features and MATLAB will give a decision boundary accordingly. Can I do this in Python? 
Also, I am looking for some types of graphs that I can use in the presentation to introduce my model and features. What are the most common graphs used in the field? 
Thank you