This work is inspired by Lavanya Shukla's housing price prediction Kaggle kernel. Since my prefer visualization tools are plotly, cufflinks, and plotly express, I will convert all of her EDA plots into the plotly stack, and add a few of plots of my own.
I am also curious as how CatBoost can stack up to other algorithms, so I will add CatBoost into the models selection, as well as using sklearn's pipeline to streamline the preprocessing steps.
Lastly, I want to see how Kaz-Anova's StackNet can be applied to this problem and what the result will be, so I will rewrite the stacking part of the notebook.
This is a work in progress, so I will keep updating these notebooks for coming weeks.
Note: I may have used cufflinks subplot features, which, as of Jun 2019, has an incompatibility issue with plotly, and the fix had not been upload to PYPI yet. I change my own cufflinks source code following this commit.