Data Analytics Computing: Decision Trees With Python

In this video, I will cover the basics of computing for Decision Trees for Regression and Classification in Python Dataset used for this video: https://www.kaggle.com/datasets/thedevastator/know-your-worth-tech-salaries-in-2016 A comprehensive coverage of Decision Trees can be found in An Introduction to Statistical Learning, Chapter 5: https://www.statlearning.com/ Fundamentals of Decision Trees terminology and theory can be found in this article: https://www.kdnuggets.com/2020/01/decision-tree-algorithm-explained.html Basics of reading data into Python: https://www.w3schools.com/python/pandas/pandas_csv.asp How to install Anaconda and Spyder on your computer: https://docs.anaconda.com/anaconda/install/index.html How to plot the decision boundary of a Decision Tree Classifier: https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html Code used in this video: https://github.com/Aurelius2500/Decision-Trees/blob/main/Decision%20Trees%20Computing.py Chapters: 0:00 Introduction And Data 6:35 Removing outliers and EDA 11:30 Decision Tree Regressor 17:20 The basics of Decision Trees 30:50 Expanding X from one predictor variable to multiple 41:35 Feature Importance for Decision Tree Regressor 45:50 Out-of-range data predictions 55:05 Decision Tree Classifier 59:45 Feature Importance for Decision Tree Classifier 1:00:50 Additional Classification Remarks 1:09:00 Comparing Model Complexity