Trying a simple ML experiment to check if the performance improves using Intel powered XGBoost

1. Reads input csv file containing a text and its label
2. Uses XGBoost to implement Single Label Text Classification Model
3. Preprocessing steps are eliminated in this test to focus on the crux of the comparison

Steps to run the Intel extended XGBoost:

1. Start running aikit_demo.ipynb notebook
2. It sets up aikit-modin library and the Intel extension for XGBoost
3. As the last step, it runs the aikit_xgboost_incidents notebook that actually contains the XGBoost model
4. Make sure the incident_classification_postprocess.csv is present in the same path as the aikit_demo notebook

Step to compare that against the normal XGBoost:

5. Open another instance (connecting to a different runtime session that does not have the aikit installed)
6. Clone aikit_xgboost_incident.ipynb and the incident_classification_postprocess.csv files to it
7. Run the notebook
8. Note the time elapsed in "model.fit" step in both cases and compare results!