Solving Insolvencies

Project Info

Team Members


4 members with unpublished profiles.

Project Description


We want to help Australian to be in a better financial situation. Our goal is to avoid another GFC or any other financial crisis. This is the reason why we wanted to work on the challenge related to insolvencies.

Our approach for this hackathon was to integrate large volume of data across multiple governmental agencies and analyse the main contributing factors impacting insolvencies in Australia.

https://public.tableau.com/profile/william6478#!/vizhome/GOVHACK/Main?publish=yes


Data Story


What we did:
- We integrated more than 20 different datasets from different governmental agencies
- We built a dataset with 200 different variables
- We used a combination of Machine Learning and Advanced Analytics to derive additional information
- We focused on interpreting our models outputs in order to derive meaningful insights. We are against black-box solution
- We built an interactive dashboard to explore all these informations

Our solution helps to:
- Explore different datasets we integrated
- Compare different SA3 areas on the different accessible variables
- Understand the factors that are impacting insolvencies ratio


Evidence of Work

Video

Homepage

Team DataSets

regional personal insolvency statistics

Data Set

CENSUS 2016 - LFSP Labour Force Status

Data Set

CENSUS 2016 - Highest Education Achieved (HEAP)

Data Set

CENSUS 2016 - Total family income in weekly amount

Data Set

CENSUS 2016 - Family composition

Data Set

CENSUS 2016 - Count of All Children

Data Set

CENSUS 2016 - Hours worked in ranges

Data Set

Mode of travel to work

Data Set

Disability (ASSNP)

Data Set

CENSUS 2016 - Year of arrival in Australia

Data Set

CENSUS 2016 - Language spoken at home

Data Set

CENSUS 2016 - Proficiency in spoken English (ENGP)

Data Set

CENSUS 2016 - Industry of employment

Data Set

CENSUS 2016 - Occupation

Data Set

CENSUS 2016 - Sex

Data Set

Social Marital Status

Data Set

CENSUS 2016 - Age

Data Set

Department of Social Services FOI Disclosure Log

Data Set

Australian Institute of Health and Welfare FOI Disclosure Log

Data Set

Specialist attendances and Medicare benefits expenditure on specialist attendances, per person, by Statistical Area Level 3 (SA3)

Data Set

Life Expectancy and Potentially Avoidable Deaths

Data Set

Census of Population and Housing: Estimating homelessness

Data Set

GovHackATO

Data Set

Higher Education Attrition Rates

Data Set

NSW childhood immunisation coverage data by SA3

Data Set

MBS Mental Health Data by SA3

Data Set

Job Seekers Receiving Newstart Allowance and Youth Allowance (Other) By Statistical Area Level 3

Data Set

B40 Non-School Qualification: Level of Education by Age by Sex

Data Set

B17 Total Personal Income (weekly) by Age by Sex

Data Set

DSS Young Carers Demographics March 2018

Data Set

Census 2016 Datapack

Data Set

Non Compliance Personal Insolvencies

Data Set

Debtors Regional Statistics

Data Set

DSS Payment Demographic Data

Data Set

Challenges

Bounty: Unmasking the State / Territory employment data.

How can we unmask the hidden data behind the labour market in our states and territories?

Go to Challenge | 8 teams have entered this challenge.

What do you want from government data challenge?

How should NSW government best provide data to the developer community? Show how our data can be made more usable for developers. What quality or format or standardisation issues does government need to fix or to consider? What developer community needs does the government need to support better?

Go to Challenge | 13 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: 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.

Bounty: Integrating AIHW

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

Go to Challenge | 28 teams have entered this challenge.

Bounty: Mix and Mashup

How can we combine the uncombinable?

Go to Challenge | 61 teams have entered this challenge.

Show Us The Numbers

How can we use open finance data to turn numbers into stories?

Go to Challenge | 13 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.

Spatial data challenge

How can spatial data be leveraged to provide the best community outcome? How can this mapping data be used to deliver value to the people of NSW?

Go to Challenge | 14 teams have entered this challenge.