H2O.ai > 实例探究 > H2O.ai empowers New South Wales Government To Deliver Exceptional Services for its Citizens with AI

H2O.ai empowers New South Wales Government To Deliver Exceptional Services for its Citizens with AI

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公司规模
Large Corporate
地区
  • Pacific
国家
  • Australia
产品
  • H2O Driverless AI
技术栈
  • Machine Learning
  • Data Science
实施规模
  • Enterprise-wide Deployment
影响指标
  • Customer Satisfaction
  • Productivity Improvements
技术
  • 分析与建模 - 机器学习
适用行业
  • 城市与自治市
适用功能
  • 商业运营
用例
  • 质量预测分析
  • 补货预测
服务
  • 数据科学服务
关于客户
New South Wales is a major global and Asia-Pacific cultural hub. Home to a diverse population of 7.7 million people, NSW is Australia’s oldest and largest state. It is also an economic powerhouse, with an economy larger than Hong Kong, Malaysia, and Singapore. The NSW Government is committed to promoting the use of data and information to improve its services and to benefit its citizens.
挑战
The New South Wales (NSW) Government wanted to build out its data practice and initiatives. They needed to enable its analysts to draw upon data science and automatic machine learning platforms to help find answers, pinpoint solutions and use data to create better services for all. The government was looking for a solution that could improve the accuracy of its predictive models and empower its team of data scientists to build models faster.
解决方案
The NSW Department of Customer Service used H2O.ai to improve the accuracy of its predictive models and empower its team of data scientists to build models faster. This allowed them to deliver more personalized and targeted programs to the citizens of NSW with improved services, policies and innovations. Programs such as the Active Kids program, which supports families by providing vouchers to put towards sport and active recreation costs each year, can be better supported with Driverless AI because it can now predict factors that cause families to miss out on these activities, target these factors and support increased participation.
运营影响
  • Accelerated the speed to build models
  • Achieved higher prediction accuracy
  • Enabled more personalized and targeted programs

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