Challenge background
The existing Tax infrastructures all across the globe are managed by the central government who collect Taxes and use them for purposes whose priorities are decided often by government officials and in board rooms of private companies. This project identifies the possibility of providing a new soft power to the public taxpayers to allow them to direct their own Tax Money into solving their own problems.
Project timeline
- 1
Week 1
SkillSet 1 (Tax Expert): Develop the similarities and differences in the individual tax amount collection among countries of interest SkillSet 2 (Web Application Expert): Create a basic Web Application which would allow users to submit their Personal Details with their Tax Amount (similar to existing portals as a testing front) SkillSet 3 (DS/AI/ML Expert): Develop a classification model for understanding the basic data based on existing features in the Tax Portals
- 2
Week 2
Transition between Weeks 1 and 3 (we would hold meetings and complete all tasks of Week 1 so that we are ready to take the main final challenge)
- 3
Week 3
SkillSet 1 (Tax Expert): Understand what features would help in governance and ponder on the Tax Direction and it’s benefits SkillSet 2 (Web Application Expert): Upgrade the basic Web Application which would allow users to submit theirTax Amount alongwith the option of exercising the optional feature of Tax Direction SkillSet 3 (DS/AI/ML Expert): Develop a classification model for understanding and visualising this new featured data allowing better governance
- 4
Week 4
Week 3 tasks continue
- 5
Week 5
Week 5 - 7: Approach governments with the basic model for development of such a system on an experimental basis. Simultaneously focus on publication of our work in appropriate OpenAccess-SCI journals with open-sourced code and freely available results
What you'll learn
- Understanding Tax structures and data 2) Data Tagging (Amount tagged with Direction) 3) Data Classification (Clustering similar Tax Directions with their Tax Amounts) 4) Web Application development (for testing) 5) Use AI/ML/NLP applications and cluster data 6) Data Visualization (after the clustering has been done) 7) Publication with collaborative efforts of the results obtained from synthetic data 8) Collaboration with Governments for implementation and upgradation of this idea