Home Rental Prediction#

Run on Google Colab View source on GitHub Download notebook


In this tutorial, we present how to use Prediction AI Engines in EvaDB to predict home rental prices. EvaDB makes it easy to do predictions using its built-in AutoML engines with your existing databases.


To follow along, you will need to set up a local instance of EvaDB via pip.

Connect to EvaDB#

After installing EvaDB, use the following Python code to establish a connection and obtain a cursor for running EvaQL queries.

import evadb
cursor = evadb.connect().cursor()

Connect EvaDB to PostgreSQL Database Server#

We will assume that you have a PostgreSQL database server running locally that contains the data needed for analysis. Follow these instructions to install PostgreSQL.

EvaDB lets you connect to your favorite databases, data warehouses, data lakes, etc., via the CREATE DATABASE statement. In this query, we connect EvaDB to an existing PostgreSQL server:

CREATE DATABASE postgres_data
WITH ENGINE = 'postgres',
    "user": "eva",
    "password": "password",
    "host": "localhost",
    "port": "5432",
    "database": "evadb"

We will assume that the input data is loaded into a PostgreSQL database. To load the home rental data into your database, see the complete home rental prediction notebook on Colab.

Preview the Home Sales Data#

We use the home rental data in this usecase. The data contains eight columns: number_of_rooms, number_of_bathrooms, sqft, location, days_on_market, initial_price, neighborhood, and rental_price.

SELECT * FROM postgres_data.home_rentals LIMIT 3;

This query previews the data in the home_rentals table:

| home_rentals.number_of_rooms | home_rentals.number_of_bathrooms | home_rentals.sqft | home_rentals.location | home_rentals.days_on_market | home_rentals.initial_price | home_rentals.neighborhood | home_rentals.rental_price |
|                            1 |                                1 |               674 |                  good |                           1 |                       2167 |                  downtown |                      2167 |
|                            1 |                                1 |               554 |                  poor |                          19 |                       1883 |                  westbrae |                      1883 |
|                            0 |                                1 |               529 |                 great |                           3 |                       2431 |                south_side |                      2431 |

Train a Home Rental Prediction Model#

Let’s next train a prediction model from the home_rental table using EvaDB’s CREATE FUNCTION query. We will use the built-in Ludwig engine for this task.

( SELECT * FROM postgres_data.home_rental )
TYPE Ludwig
PREDICT 'rental_price'

In the above query, we use all the columns (except rental_price) from home_rental table to predict the rental_price column. We set the training time out to be 3600 seconds.


Go over Model Training with Ludwig page on exploring all configurable paramters for the model training frameworks.

| Function PredictHouseRent successfully added |

Predict the Home Rental Price using the Trained Model#

Next we use the trained PredictHouseRent to predict the home rental price.

SELECT PredictHouseRent(*) FROM postgres_data.home_rentals LIMIT 3;

We use * to simply pass all columns into the PredictHouseRent function.

| predicthouserent.rental_price_predictions |
|                               2087.763672 |
|                               1793.570190 |
|                               2346.319824 |

We have the option to utilize a LATERAL JOIN to compare the actual rental prices in the home_rentals dataset with the predicted rental prices generated by the trained model, PredictHouseRent.

SELECT rental_price, predicted_rental_price
FROM postgres_data.home_rentals
JOIN LATERAL PredictHouseRent(*) AS Predicted(predicted_rental_price)

Here is the query’s output:

| home_rentals.rental_price | Predicted.predicted_rental_price |
|                      2167 |                      2087.763672 |
|                      1883 |                      1793.570190 |
|                      2431 |                      2346.319824 |
+------------------ --------+----------------------------------+

What’s Next?#

👋 EvaDB’s vision is to bring AI inside your database system and make it easy to build fast AI-powered apps. If you liked this tutorial and are excited about our vision, show some ❤️ by: