Machine Learning

Project Info

Team Members


5 members with unpublished profiles.

Project Description


Hi, we are the pengunz and our project encompasses three Govhack challenges relating to the implementation of machine learning algorithms into data-sets to better achieve results.

Machine learning is the use of a variety of algorithms to conduct in-depth analysis of data-sets to fit specific requests. Machine learning is effective in finding patterns in data to better understand the cause of data outcomes.

To demonstrate a practical application for this we come to our first issued challenge: Helping to predict non-compliance in the personal insolvency system.


Data Story


We collected data from the ATO and the AFSA and used Microsoft Azure to develop methods of utilising machine learning algorithms to better use and organise the data.


Video

Team DataSets

Bankruptcy Data from the bank

Description of Use This is a data set for our AI

Data Set

ATO GovHack Data

Description of Use Which we will put into our AI

Data Set

Challenges

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.

Most creative use of WA data award

This challenge aims to champion the creative use of Western Australian data.

Go to Challenge | 5 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.

Most innovative award

This an open challenge to recognise the use of data in an innovative way that was unexpected and challenges the common use of data

Go to Challenge | 8 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.