DS - Data Science with Python: Random Forest Classifiers

Author: Philip Wilkinson, First Year Representative, UCL Data Science Society (philip.wilkinson.19@ucl.ac.uk)
Proudly presented by the UCL Data Science Society

Software prerequisite

• 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

• Knowledge of Numpy, Pandas and Matplotlib (DS01, DS02, DS03) is good to have but not neccesary.
• Knowledge of Decision Tree Classifiers is good to have not not necessary

Structure

├── DS - Data Science with Python: Random Forest Classifiers
│   ├── Data
│   |   ├── NBA_tot.txt
│   |   ├── red_wine.csv
│   |   ├── white_wine.csv
│   ├── Problem.ipynb
│   ├── Solutions.ipynb
    └── workshop.ipynb