Bounty: Tax Help Centers
Looking at how the ATO could use artificial intelligence or machine learning to locate the best locations for Tax Help Centers
Go to Challenge | 21 teams have entered this challenge.
A Taxing Problem
Project Info
Team Name
Nostradata
Team Members
Daniel and 6 other members with unpublished profiles.
Project Description
Our focus was the challenge of identifying where the ATO should locate their Tax Help Centers. This project also addressed the challenge of combining data sets and using open data to help governments answer questions.
Data Story
We set out to demonstrate in this challenge how Machine Learning can be used to help government agencies make decisions, in this case where to put Tax Help Centers to best serve the community.
We utilised this opportunity to merge data from a variety of sources, namely:
1. ATO individual tax return data from 2014 - 2015 & 2015 - 2016
2. ABS demographic summary data from 2015 & 2016
3. Tax Help Center locations
4. ABS Geography publications
Given that the current distribution of Tax Help Centers is not necessarily optimal, the merged data was used to calculate an adjusted score for each postcode's requirement for tax assistance based upon the ATO eligibility criteria.
The adjusted scores were used to train a deep neural network to predict the required number of Tax Help Centers in a postcode, which after using cross-folds validation to verify the accuracy of the model, achieved a score of 96%
Team DataSets
Taxation Statistics 2014-15
ABS Geography Publications
ATO GovHack 2018
ATO GovHack 2017
Taxation Statistics 2015-16
Challenge Entries
More than apps and maps: help government decide with data
How can we combine data to help government make their big and small decisions? Government makes decisions every day—with long term consequences such as the location of a school, or on a small scale such as the rostering of helpdesk staff.
Go to Challenge | 58 teams have entered this challenge.
Bounty: Mix and Mashup
How can we combine the uncombinable?
Go to Challenge | 61 teams have entered this challenge.