Using Machine Learning to Find Paradise

These eight groups of characteristics surfaced that make a residential area attractive: Education and Career, Family, Culture, Nature, Safety and Infrastructure, Cost of Living, Restaurants and Shopping, and Health. SAS Visual Data Mining and Machine Learning and SAS Visual Analytics were used for the analysis and to prepare visual representations of the data.

So now you know where the objectively 😉 best place in the world is, according to our analytical assessment. But maybe you don’t place the same value on career opportunities, family friendliness, hours of sunshine, income or cultural offerings.

That’s why we created the Paradise Configurator. It allows anyone to easily and quickly determine where their own personal paradise is located by weighting the characteristics according to personal preferences or custom search criteria.

The interesting thing about Paradise Found? Normally, we concentrate on our customers’ questions when we begin an analytical journey. This time, we came up with the assignment ourselves – find the best place in the world.

Machine Learning is not a Black Art

We were trying to demonstrate that machine learning is not a black art, but also doesn’t happen by simply waving a magic wand to start up the self-learning machine. What it actually involves is a bunch of algorithms that learn from data instead of using a model assumption. And it’s only effective when visualisation, data management and analytics work together seamlessly.

MachineLearning is not a black art, but also doesn’t happen by simply waving a magic wand. We found paradise in West Perth, find yours by using the Paradise Configurator. #ParadiseFound Click To Tweet
Is SAS entering the tourism industry or becoming a B2C company? Not at all. All we were trying to do is show what big data analytics and machine learning can do using an example that would be meaningful to as many people as possible.

Related: The Road to AI is Paved With Potential 

Our mission remains working together with you to find solutions for your unique business challenges – whether that involves finding the best place or best customer, uncovering potential fraudulent financial transactions, or identifying opportunities to optimise production processes.

Because practically any company can benefit from big data analytics and machine learning, regardless of the industry. And if you should happen to be sitting in the “best place in the world” right now, I’m glad to pass along contact information for my Australian colleagues.

This article has been republished with permission from SAS.