Machine learning algorithms for regression view (Python)

Description

This code shows several machine learning algorithms in-depth for a random regression dataset. I made this because I wanted to see each algorithms sensitivity against over/underfitting. The main library that I'm using is SK-learn which is for python. Take also into account that I built this in collab from Google. The structures is as follows:

1. libraries installation, imports, etc.
2. Definition of a random dataset
3. Drive mounting
4. Random dataset generation
5. Linear regression algorithm
6. KNN
7. SVR
8. SVR - kernel sensitivity
9. Decision tree model
10. Decision tree model - Sample split sensitivity
11. Decision tree model - Leaf's sensitivity
12. Random forest model
13. Gradient boosting

How to install this

1. Download the code
2. Open it in google colab. I didn't try with Jupyter but I guess that should be more or less the same. As far as I had just checked, this guy made it: https://www.youtube.com/watch?v=L80dn0PiN_U&ab_channel=TechTips

How to tweak this code

There are several point to tweek. You can see at the beggining of each section how it is made.

Found a bug?

Please let us know by writing it down and your thoughts about what caused that bug. Try to explain as much as you can. It will be very appreciate it

Work in progress

I'm thinking about adding ensembled learning techniques actually.