Predict Home Rental Prices with MindsDB
In this tutorial, we’ll use a regression model to predict home rental prices.
This tutorial uses the Lightwood integration that requires the mindsdb/mindsdb:lightwood
Docker image. Learn more here.
Connect a data source
We will start by connecting a demo database to MindsDB using the CREATE DATABASE
statement.
Let’s preview the data that will be used to train the model.
Deploy and train an ML model
Let’s create and train a machine learning model. For that we are going to use the CREATE MODEL
statement, where we specify what query to train FROM
and what we want to PREDICT
.
It may take a couple of minutes for the training to complete. You can monitor the status of your model as below.
Make predictions
Once the model’s status is complete, you can make predictions by querying the model.
You can also make batch predictions by joining the data table with the model.
Automate continuous improvement of the model
Now, we can take this even further. MindsDB includes powerful automation features called Jobs which allow us to automate queries in MindsDB. This is very handy for production AI/ML systems which all require automation logic to help them to work.
We use the CREATE JOB
statement to create a Job.
Now, let’s use a Job to retrain the model every two days, just like we might in production. You can retrain the model to improve predictions every time when either new data or new MindsDB version is available. And, if you want to retrain your model considering only new data, then go for finetuning it.
This job will execute every 2 days only if there is new data available in the home_rentals
table. Learn more about the LAST
keyword here.
And there you have it! You created an end-to-end automated production ML system in a few short minutes.
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