Decision trees are a very popular machine learning model. The beauty of it comes from its easy-to-understand visualization and fast deployment into production.
Check out this tutorial for a 3 step procedure for visualizing a decision tree in Python.
This is a tutorial of using the seaborn library in Python for Exploratory Data Analysis (EDA).
In this guide, you’ll discover (with examples):
– How to use the seaborn Python package to produce useful and beautiful visualizations, including histograms, bar plots, scatter plots, boxplots, and heatmaps.
– How to explore univariate, multivariate numerical and categorical variables with different plots.
– How to discover the relationships among multiple variables.
– Lots more.