Welcome to our firth workshop in the Data Science with Python Series! In this workshop we will introduce you to advanced regression techniques, building on the linear regression workshop. This will cover five potential regression models you could use: Lasso Regression, Ridge Regression, Elastic Net Regression, Decision Tree Regression and Random Forest Regression.
Author: Philip Wilkinson, Head of Science (2021/22), UCL Data Science Society (philip.wilkinson.19@ucl.ac.uk) Proudly presented by the UCL Data Science Society
- Install Anaconda
For Windows use: https://repo.anaconda.com/archive/Anaconda3-2020.07-Windows-x86_64.exe
For MacOS use: https://repo.anaconda.com/archive/Anaconda3-2020.07-MacOSX-x86_64.pkg
- Run this code in a code cell in the Jupyter Notebook to intall Matplotlib library:
!pip install scikit-learn
├── DS05 - Data Science with Python: Advanced Regression
│ ├── data
│ | ├── ENB2012_data.csv
│ ├── problem.ipynb
│ ├── solution.ipynb
└── workshop.ipynb