The Four Musketeers

Project Info

Team Name

The Four Musketeers

Team Members

William , Bas and 3 other members with unpublished profiles.

Project Description

Unleash the power of machine learning to predict the causal factors of noncompliance.
The team delivered learning Algorithm model to predict the risk of noncompliance on case by case basis.

Data Story

Our team built a complete machine learning model that harness large volume of government datasets from various organisations including: Australian Taxation Office, Australian Bureau of Statistics and Australian Government Financial Security Authority-non-compliance in personal insolvency data.

Evidence of Work

Video

Homepage

Team DataSets

non-compliance-personal-insolvencies

Description of Use main datasets to compare with others datasets

Data Set

health-welfare-services/homelessness-services for GovHack 2018.

Description of Use dataset to integrate with other related datasets for visualisation and modelling purposes

Data Set

Challenge Entries

To bankruptcy or not to bankruptcy, keeping the process real.

Helping predict non-compliance in the personal insolvency system. How can Artificial Intelligence and Machine Learning assist us in the future?

Go to Challenge | 13 teams have entered this challenge.

The Friendly ATO

How can the ATO use artificial intelligence or machine learning to better understand and develop ways to engage with our clients?

Go to Challenge | 15 teams have entered this challenge.

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.

Bounty: Is seeing truely believing?

How can we tell a story with visualisations, that speaks the truest representation of our data?

Go to Challenge | 28 teams have entered this challenge.

SEED - Open Data with a Purpose

We are seeking to challenge the status quo. Moving from open data as a bi-product of government business, to active management of open data to better support reuse and innovation – hence open data with a purpose. To achieve this we want to trigger a conversation between developers and the data custodians.

Go to Challenge | 11 teams have entered this challenge.

Bounty: Integrating AIHW

How can we integrate AIHW and other data sources in interesting ways?

Go to Challenge | 28 teams have entered this challenge.

Data4Good

How can open data be used to make a social impact, contributing to the betterment of society? How can we improve prospects for children, and education, using open data? What sort of impact can be made on homelessness, mental health outcomes, or the environment, using open data?

Go to Challenge | 19 teams have entered this challenge.

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