Follow the steps below to start making data forecasts with MindsDB using standard SQL.
Check out our Getting Started guide to set up and work with MindsDB using your own data and models.

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1. Create a MindsDB Cloud Account or Install MindsDB Locally

Currently, we’re working on enhancing the MindsDB Cloud experience. Therefore, we encourage you to install MindsDB locally, as it provides you with the latest updates to our codebase.
Create your free MindsDB Cloud account to start practicing right away using the MindsDB Cloud Editor. If you prefer a local MindsDB installation, follow the Deployment guides of MindsDB documentation. You can install MindsDB using Docker or follow the standard installation using pip.

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2. Connect to MindsDB from a SQL Client

You can use the MindsDB Editor or open your preferred SQL client, such as DBeaver or MySQL CLI, and connect to MindsDB. Learn more here.

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3. Connect a Database Using CREATE DATABASE

We have a sample database that you can use right away. To connect a database to your MindsDB Cloud account, use the CREATE DATABASE statement, as below. 
CREATE DATABASE example_data
WITH ENGINE = "postgres",
PARAMETERS = {
  "user": "demo_user",
  "password": "demo_password",
  "host": "3.220.66.106",
  "port": "5432",
  "database": "demo"
};
On execution, we get: 
Query OK, 0 rows affected (3.22 sec)

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4. Preview the Available Data Using SELECT

You can now preview the available data with a standard SELECT statement. 
SELECT *
FROM example_data.demo_data.home_rentals
LIMIT 10;
On execution, we get: 
+-----------------+---------------------+------+----------+----------------+---------------+--------------+--------------+
| number_of_rooms | number_of_bathrooms | sqft | location | days_on_market | initial_price | neighborhood | rental_price |
+-----------------+---------------------+------+----------+----------------+---------------+--------------+--------------+
| 0.0             | 1.0                 | 484  | great    | 10             | 2271          | south_side   | 2271         |
| 1.0             | 1.0                 | 674  | good     | 1              | 2167          | downtown     | 2167         |
| 1.0             | 1.0                 | 554  | poor     | 19             | 1883          | westbrae     | 1883         |
| 0.0             | 1.0                 | 529  | great    | 3              | 2431          | south_side   | 2431         |
| 3.0             | 2.0                 | 1219 | great    | 3              | 5510          | south_side   | 5510         |
| 1.0             | 1.0                 | 398  | great    | 11             | 2272          | south_side   | 2272         |
| 3.0             | 2.0                 | 1190 | poor     | 58             | 4463          | westbrae     | 4124         |
| 1.0             | 1.0                 | 730  | good     | 0              | 2224          | downtown     | 2224         |
| 0.0             | 1.0                 | 298  | great    | 9              | 2104          | south_side   | 2104         |
| 2.0             | 1.0                 | 878  | great    | 8              | 3861          | south_side   | 3861         |
+-----------------+---------------------+------+----------+----------------+---------------+--------------+--------------+
You could also browse the databases of MindsDB using the command below. 
SHOW databases;
On execution, we get: 
+---------------------+
| Database            |
+---------------------+
| information_schema  |
| mindsdb             |
| files               |
| example_data        |
+---------------------+
To learn more about MindsDB tables structure, check out this guide.

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5. Create a Model Using CREATE MODEL

Now you are ready to create your first model. Use the CREATE MODEL statement, as below. 
CREATE MODEL mindsdb.home_rentals_model
FROM example_data
  (SELECT * FROM demo_data.home_rentals)
PREDICT rental_price;
On execution, we get: 
Query OK, 0 rows affected (9.79 sec)

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6. Check the Status of a Model

It may take a couple of minutes until the model is trained. You can monitor the status of your model by executing the following command: 
DESCRIBE home_rentals_model;
On execution, we get: 
+------------+
| status     |
+------------+
| generating |
+------------+
After a short time, we get: 
+----------+
| status   |
+----------+
| training |
+----------+
And finally, we get: 
+----------+
| status   |
+----------+
| complete |
+----------+
Alternatively, you can use the SHOW MODELS command as below. 
SHOW MODELS
[FROM project_name]
[LIKE 'model_name']
[WHERE column_name = value];
Here is an example: 
SHOW MODELS
FROM mindsdb
LIKE 'home_rentals_model'
WHERE status = 'complete';
The status of the model must be complete before you can start making predictions.

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7. Make Predictions Using SELECT

The SELECT statement allows you to make predictions based on features, where features are the input variables, or input columns, that are used to make forecasts. Let’s predict what would be the rental price of a 1000 square feet house with two bathrooms. 
SELECT rental_price
FROM mindsdb.home_rentals_model
WHERE number_of_bathrooms = 2
AND sqft = 1000;
On execution, we get: 
+--------------+
| rental_price |
+--------------+
| 1130         |
+--------------+
Here is how to make batch predictions: 
SELECT m.rental_price, m.rental_price_explain
FROM mindsdb.home_rentals_model AS m
JOIN example_data.demo_data.home_rentals AS d;

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8. Automate the Workflow Using CREATE JOB

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 set the model we’ve created to be retrained 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. In the same job, we will create a table and insert these new predictions back into a database so the predictions are ready to be used by our hypothetical application. 
CREATE JOB retrain_model_and_save_predictions (

   RETRAIN mindsdb.home_rentals_model
   FROM example_data
         (SELECT * FROM demo_data.home_rentals)
   USING
         join_learn_process = true;

   CREATE TABLE my_integration.rentals_{{START_DATETIME}} (
         SELECT m.rental_price, m.rental_price_explain
         FROM mindsdb.home_rentals_model AS m
         JOIN example_data.demo_data.home_rentals AS d
   )

)
END '2023-10-30 00:00:00'
EVERY 2 days;
Please note that my_integration is your database connection name in MindsDB. Before executing this job, make sure to connect your database to MindsDB with a user who has write access to be able to create a table. And there you have it! You created an end-to-end automated production ML system in a few short minutes.
Congratulations! If you got this far, you have successfully trained a predictive model using SQL and got the future data!
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