In this example, we use our sample PostgreSQL database. You can connect to it like this:
CREATE DATABASE example_db
WITH ENGINE = "postgres",
PARAMETERS = {
"user": "demo_user",
"password": "demo_password",
"host": "3.220.66.106",
"port": "5432",
"database": "demo"
};
First, we create and train the model using a subset of the home_rentals data, considering properties that have been on the market less than 10 days.
CREATE MODEL mindsdb.adjust_home_rentals_model
FROM example_db
(SELECT *
FROM demo_data.home_rentals
WHERE days_on_market < 10)
PREDICT rental_price;
On execution, we get:
Query successfully completed
We can check its status using this command:
SELECT *
FROM mindsdb.models
WHERE name = 'adjust_home_rentals_model';
Once the status is complete, we can query for predictions.
SELECT rental_price, rental_price_explain
FROM mindsdb.adjust_home_rentals_model
WHERE sqft = 1000
AND location = 'great'
AND neighborhood = 'berkeley_hills'
AND number_of_rooms = 2
AND number_of_bathrooms = 1
AND days_on_market = 40;
On execution, we get:
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price | rental_price_explain |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 2621 | {"predicted_value": 2621, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 2523, "confidence_upper_bound": 2719} |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
Let’s adjust this model with more training data. Now we consider properties that have been on the market for 10 or more days.
FINETUNE mindsdb.adjust_home_rentals_model
FROM example_db
(SELECT *
FROM demo_data.home_rentals
WHERE days_on_market >= 10);
On execution, we get:
Query successfully completed
To check the status and versions of the model, run this command:
SELECT name, engine, project, active, version, status
FROM mindsdb.models_versions
WHERE name = 'adjust_home_rentals_model';
On execution, we get:
+---------------------------+-----------+---------+--------+---------+----------+
| name | engine | project | active | version | status |
+---------------------------+-----------+---------+--------+---------+----------+
| adjust_home_rentals_model | lightwood | mindsdb | false | 1 | complete |
| adjust_home_rentals_model | lightwood | mindsdb | true | 2 | complete |
+---------------------------+-----------+---------+--------+---------+----------+
Please note that the longer the property is on the market, the lower its rental price. Hence, we can expect the rental_price prediction to be lower.
SELECT rental_price, rental_price_explain
FROM mindsdb.adjust_home_rentals_model
WHERE sqft = 1000
AND location = 'great'
AND neighborhood = 'berkeley_hills'
AND number_of_rooms = 2
AND number_of_bathrooms = 1
AND days_on_market = 40;
On execution, we get:
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| rental_price | rental_price_explain |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 2055 | {"predicted_value": 2055, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 1957, "confidence_upper_bound": 2153} |
+---------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
