Baseline
Steps to run the code:
1. Add the path to the dataset in cell 2, lines 1, 2, 3
2. Run published_baseline.ipynb cell by cell
3. An output file 'results_of_active_learning.txt' will get created once the notebook has finished running
4. Pass this file as input to score.py to generate the Area under the Learning Curve metric
Evaluation metric = Area under the Learning Curve
Area under the learning curve for random sampling = 0.4741 (Baseline from milestone 2)
Area under the learning curve for active learning = 0.4889
The final graph containing the learning curves of both random sampling and active learning method is 'published_baseline_plot.png'