3. Predicting Property Prices with No Code Machine Learning

Today, we’re gonna build a model to predict the price for an upcoming property listing.

We’ll start by:

  1. Reviewing the dataset
  2. We’ll then build a Machine Learning model for predicting property prices.
  3. And instantly see how we can make predictions and share them with others on the team.

So let’s get started.

Number 1: Reviewing the dataset

In this dataset, every single row is a property that was listed for sale.

And every column tells us something about that property.

Like number of bedrooms, condition, year built, etc.

At the end, we have a column here called price.

This tells us the cost at which the property in that row was sold.

This is a historical dataset.

And we now wanna use it to predict how much a new property listing is likely to sell for.

Number 2: Building the machine learning model

To build a model, we start by uploading this CSV. 

We can upload spreadsheets,

Connect to apps & services like Salesforce and Dropbox

And even databases like MySQL and BigQuery.

Once uploaded, all you have to do is select your prediction column. 

In this case, it’s the Price column.

From here, Obviously AI will automatically build a machine learning model to make predictions, in less than 1 minute.

Number 3: Make predictions and share with your team

Once your model is built, it’s ready to be used right away. No deploying, no maintenance.

You can see it’s accuracy and performance details here. 

Seems like we’re good to go!

We start by heading to Personas to make a first prediction.

Let’s say we have a new property.

It has 2 bedrooms, 2 bathrooms, 2 rooms with a view, say it was built in 1899 and got renovated in 2001. 

As I change these details, I can see the prediction for this new property come up right here.

Predicted selling price is roughly $203,000 dollars, give or take $6,400 dollars.

Now, let’s head to export predictions. We can upload a list of new customers and get a prediction on each one. An API can also be used to automate these predictions and embed them in your own app or website.

You will also have the option to get a shareable link to send to anyone on your team. It will enable them to use the model you built to start making their own predictions.

So, what would you like to predict today?

Complete and Continue