README.txt

Shanmukha Bodapati - ssb180006 
Raghav Sriram - rxs180123
CS 4372.001

The code can be run on either Jupyter notebook or uploaded to Google Colab and run.
We used Colab to run it and we ensured that it works on Jupyter as well
as well assuming you have all the dependencies installed (pandas, numpy, matplotlib, seaborn, sklearn, scipy, graphviz, etc.).
We also included commands to pip install xgboost and pip install scikit-plot

The dataset is in a public repository (GitHub). We included the link on the notebook but it's here as well:
"https://github.com/SBodapati11/Tree-Classification/blob/main/Bank%20Customer%20Churn%20Prediction.csv?raw=true".

You can simply open the code in Google Colab or Jupyter notebook and run all cells.

***Please note the values might be slightly different after running on your PC. 
However, the differences are extremely small and do not alter our conclusions and analysis.
The differences are because the training and testing splits are randomized.***

Another thing is the trees may be difficult to zoom in and view on the report. Please 
use the ipynb if you need more clarification.