Data Analytics Computing: Random Forests For Regression and Classification With Python

In this video, I will cover the basics of computing for Random Forests, a powerful ensemble algorithm for Regression and Classification in Python Dataset used for this video: https://www.kaggle.com/datasets/thedevastator/fatal-traffic-accidents-in-arizona-2012-2016?select=accident.csv A comprehensive coverage of Decision Trees and Random Forests can be found in An Introduction to Statistical Learning, Chapter 5: https://www.statlearning.com/ A more comprehensive on the theory behind Random Forests can be found in this article: https://williamkoehrsen.medium.com/random-forest-simple-explanation-377895a60d2d 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 Link to the script used in the video: https://github.com/Aurelius2500/Random-Forest Chapters: 0:00 Introduction And Data 10:50 Dummy Variables and finishing data 16:50 Random Forest Classifier 21:55 The Train Test Split 30:16 Feature Importance for Random Forest Classifier 37:11 Multimodal/Multiclass Classification 41:25 Why More Complex Models are not necessarily always better 45:30 Random Forest Regressor 50:43 Feature Importance for Random Forest Regressor 52:30: Additional Remarks