A brief description of each file and their associated Medium Posts below:
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The OLSplots.py module contains functions to construct the classic R diagnostic plots after conducting a linear
regression. The LinearRegression Jupyter noteboook contains examples of how to use the functions.
Companion Medium post:
https://medium.com/@jasonsadowski552/going-from-r-to-python-linear-regression-diagnostic-plots-144d1c4aa5a
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The StratifiedRandomSampling R script and Jupyter notebook simulate a dataset and then show how inferences change
with Stratified vs. Simple random sampling.
Companion Medium post:
https://medium.com/@jasonsadowski552/how-to-analyze-stratified-random-sampled-data
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The PartialDependencePlots.ipynb notebook uses sklearn's PDP functions and custom PDP functions to analyze how different features in a random forest model predict the Boston Housing Dataset.
Companion Medium post:
https://towardsdatascience.com/looking-beyond-feature-importance-37d2807aaaa7