Let’s consider the following income_table table that stores the income and debt values.

SELECT income, debt
FROM income_table;

On execution, we get:

+------+-----+
|income|debt |
+------+-----+
|60000 |20000|
|80000 |25100|
|100000|30040|
|120000|36010|
+------+-----+

A simple visualization of the data present in the income_table table is as follows:

Querying the income table to get the debt value for a particular income value results in the following:

SELECT income, debt
FROM income_table
WHERE income = 80000;

On execution, we get:

+------+-----+
|income|debt |
+------+-----+
|80000 |25100|
+------+-----+

And here is what we get:

But what happens when querying the table for an income value that is not present there?

SELECT income, debt
FROM income_table
WHERE income = 90000;

On execution, we get:

Empty set (0.00 sec)

When the WHERE clause condition is not fulfilled for any of the rows, no value is returned.

When a table doesn’t have an exact match, the query returns an empty set or null value. This is where the AI Tables come into play!

Let’s create a debt_model model that allows us to approximate the debt value for any income value. We train the debt_model model using the data from the income_table table.

CREATE MODEL mindsdb.debt_model
FROM income_table
PREDICT debt;

On execution, we get:

Query OK, 0 rows affected (x.xxx sec)

MindsDB provides the CREATE MODEL statement. On execution of this statement, the predictive model works in the background, automatically creating a vector representation of the data that can be visualized as follows:

Please note that debt_model is our AI Table.

Let’s now look for the debt value of some random income value. To get the approximated debt value, we query the mindsdb.debt_model model instead of the income_table table.

SELECT income, debt
FROM mindsdb.debt_model
WHERE income = 90000;

On execution, we get:

+------+-----+
|income|debt |
+------+-----+
|90000 |27820|
+------+-----+

And here is how it looks: