The objective of this project is to gain understanding on how Decision Trees work, the math behind it and what are its use cases.
We will also look at the advantages and disadvantages of using them.
The project has been structured as using pyisa.
Following are the components:
- data - Central repository for all data
- models - ML models used
- preprocess - Feature engineering scripts
- results - Results of all the experiments
- visualize - Visualization scripts and images
A jupyter notebook explaining the core concepts and code interpretation is included in the root folder.
First and the fore most important thing to do is ensure all the required packages listed in the requirements.txt are installed. From the command line run:
pip install -r requirements.txt
While the project is uploaded on github and the repo can be cloned locally, the best way to learn would be to follow the instructions verbatim as provided in the notebook.