Where do I start with the Quaartz AI platform?
I go to the Quaartz application website and register for the free trial. Choose your login, select your password. And you’re in.
No need to have data to start using Quaartz!
Select one of our use cases, and start with the associated preloaded dataset!
Click on the ‘use case’ button.
Click on a use case, for instance, ‘Estimating your house listing price.’
Then create a project from this use case.
Rows and columns?
As you can see, there are several rows and columns in the spreadsheet provided. Each row represents a house sold. Each column contains the values corresponding to one variable. For instance, there is one column for the age of the house. There is a column that represents the latitude of its location. There is one column with the price of the house sold.
There is possibly a connection between the surface of a house and its selling price. There is also perhaps a connection between the location of the house and its selling price. Our team has gathered in this spreadsheet these informations (surface, location, price) for several houses already sold.
The goal is to anticipate the selling price of a new house for sale on the market. A place that is not yet part of our spreadsheet because it has not been sold.
A goal?
Quaartz can make guesses on the future price of a house that was not sold yet using its sophisticated AI engine. The only thing you have to do is tell Quaartz that it is indeed the selling price of the house that you want it to guess. You do this by selecting the house selling price as a ‘goal.’
Just click on the item you want Quaartz to predict for you, such as the house selling price.
A prediction?
Click on ‘create variable set.’ And next on ‘Generate model.’
You will see a screen showing that Quaartz has finished its work.
That’s it, you’ve run Quaartz once. If you add new information about a new house to the spreadsheet, Quaartz will automatically estimate its price.
But what if you want to use your data? No worries, you can read our article about “How to prepare your data.”
