Addressable markets for AI-centric applications are projected to increase significantly from 2019 to 2024. Companies are building more AI driven business applications by infusing AI into the workflows or by using AI to provide additional values via add-on use cases. This new paradigm of building AI based applications brings in more challenges in terms of explaining the ML outcomes from the business context. Hence, explaining the results of the algorithms with local and global explanations that can be leveraged across the business applications (for eg: transaction systems) is becoming increasingly important.
How to run code:
- Run ipynb files separately to generate datasets and respective models and shaply values
- cd streamlit
- streamlit run main.py
Dependencies:
- sklearn
- pandas, numpy, matplotlib, seaborn
- shap
- pickle
- streamlit