Predicting Home Rental Prices with MindsDB

Introduction

In this tutorial, we’ll create and train a machine learning model, or as we call it, an AI Table or a predictor. By querying the model, we’ll predict the rental prices of the properties based on their attributes, such as the number of rooms, area, or neighborhood.

Make sure you have access to a working MindsDB installation, either locally or at MindsDB Cloud.

If you want to learn how to set up your account at MindsDB Cloud, follow this guide. Another way is to set up MindsDB locally using Docker or Python.

Let’s get started.

Data Setup

Connecting the Data

There are a couple of ways you can get the data to follow through with this tutorial.

Connecting as a database

You can connect to a demo database that we’ve prepared for you. It contains the data used throughout this tutorial (the example_db.demo_data.home_rentals table).

CREATE DATABASE example_db
WITH ENGINE = "postgres",
PARAMETERS = {
    "user": "demo_user",
    "password": "demo_password",
    "host": "3.220.66.106",
    "port": "5432",
    "database": "demo"
};

Now you can run queries directly on the demo database. Let’s preview the data that we’ll use to train our predictor.

SELECT *
FROM example_db.demo_data.home_rentals
LIMIT 10;

Pay Attention to the Queries From now on, we’ll use the example_db.demo_data.home_rentals table. Make sure you replace it with files.home_rentals if you connect the data as a file.

Understanding the Data

We use the home rentals dataset, where each row is one property, to predict the rental_price column value for all the newly added properties.

Below is the sample data stored in the example_db.demo_data.home_rentals table.

+-----------------+---------------------+------+----------+----------------+----------------+--------------+
| number_of_rooms | number_of_bathrooms | sqft | location | days_on_market | neighborhood   | rental_price |
+-----------------+---------------------+------+----------+----------------+----------------+--------------+
|               2 |                   1 |  917 | great    |             13 | berkeley_hills |         3901 |
|               0 |                   1 |  194 | great    |             10 | berkeley_hills |         2042 |
|               1 |                   1 |  543 | poor     |             18 | westbrae       |         1871 |
|               2 |                   1 |  503 | good     |             10 | downtown       |         3026 |
|               3 |                   2 | 1066 | good     |             13 | thowsand_oaks  |         4774 |
+-----------------+---------------------+------+----------+----------------+----------------+--------------+

Where:

Column	Description	Data Type	Usage
number_of_rooms	Number of rooms in a property [0,1,2,3].	integer	Feature
number_of_bathrooms	Number of bathrooms in a property [1,2].	integer	Feature
sqft	Area of a property in square feet.	integer	Feature
location	Rating of the location of a property [poor, great, good].	character varying	Feature
days_on_market	Number of days a property has been on the market.	integer	Feature
neighborhood	Neighborhood [alcatraz_ave, westbrae, ..., south_side, thowsand_oaks].	character varying	Feature
rental_price	Rental price of a property in USD.	integer	Label

Labels and Features

A label is a column whose values will be predicted (the y variable in simple linear regression).
A feature is a column used to train the model (the x variable in simple linear regression).

Training a Predictor

Let’s create and train the machine learning model. For that, we use the CREATE MODEL statement and specify the input columns used to train FROM (features) and what we want to PREDICT (labels).

CREATE MODEL mindsdb.home_rentals_model
FROM example_db
  (SELECT * FROM demo_data.home_rentals)
PREDICT rental_price;

We use all of the columns as features, except for the rental_price column, whose values will be predicted.

Status of a Predictor

A predictor may take a couple of minutes for the training to complete. You can monitor the status of the predictor by using this SQL command:

DESCRIBE home_rentals_model;

If we run it right after creating a predictor, we get this output:

+------------+
| status     |
+------------+
| generating |
+------------+

A bit later, this is the output:

+----------+
| status   |
+----------+
| training |
+----------+

And at last, this should be the output:

+----------+
| status   |
+----------+
| complete |
+----------+

Now, if the status of our predictor says complete, we can start making predictions!

Making Predictions

Making a Single Prediction

You can make predictions by querying the predictor as if it were a table. The SELECT statement lets you make predictions for the label based on the chosen features.

SELECT rental_price, rental_price_explain
FROM mindsdb.home_rentals_model
WHERE sqft = 823
AND location='good'
AND neighborhood='downtown'
AND days_on_market=10;

On execution, we get:

+--------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price | rental_price_explain                                                                                                                          |
+--------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 4394         | {"predicted_value": 4394, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 4313, "confidence_upper_bound": 4475} |
+--------------+-----------------------------------------------------------------------------------------------------------------------------------------------+

Making Batch Predictions

Also, you can make bulk predictions by joining a data table with your predictor using JOIN.

SELECT t.rental_price AS real_price,
       m.rental_price AS predicted_price,
       t.number_of_rooms,  t.number_of_bathrooms, t.sqft, t.location, t.days_on_market
FROM example_db.demo_data.home_rentals AS t
JOIN mindsdb.home_rentals_model AS m
LIMIT 100;

On execution, we get:

+------------+-----------------+-----------------+---------------------+------+----------+----------------+
| real_price | predicted_price | number_of_rooms | number_of_bathrooms | sqft | location | days_on_market |
+------------+-----------------+-----------------+---------------------+------+----------+----------------+
| 3901       | 3886            | 2               | 1                   | 917  | great    | 13             |
| 2042       | 2007            | 0               | 1                   | 194  | great    | 10             |
| 1871       | 1865            | 1               | 1                   | 543  | poor     | 18             |
| 3026       | 3020            | 2               | 1                   | 503  | good     | 10             |
| 4774       | 4748            | 3               | 2                   | 1066 | good     | 13             |
+------------+-----------------+-----------------+---------------------+------+----------+----------------+

What’s Next?

Have fun while trying it out yourself!

• Bookmark MindsDB repository on GitHub.
• Sign up for a free MindsDB account.
• Engage with the MindsDB community on Slack or GitHub to ask questions and share your ideas and thoughts.

If this tutorial was helpful, please give us a GitHub star here.