---
title: Get a Single Prediction
sidebarTitle: Get a Single Prediction
---

## Description

The `SELECT` statement fetches predictions from the model table. The data is returned on the fly and the result set is not persisted.

But there are ways to save predictions data! You can save your predictions as a view using the [`CREATE VIEW`](/sql/create/view/) statement. Please note that a view is a saved query and does not store data like a table. Another way is to create a table using the [`CREATE TABLE`](/sql/create/table/) statement or insert your predictions into an existing table using the [`INSERT INTO`](/sql/api/insert/) statement.

## Syntax

Here is the syntax for fetching a single prediction from the model table:

```sql
SELECT target_name, target_name_explain
FROM mindsdb.predictor_name
WHERE column_name = value 
AND column_name = value;
```

<Warning>
    **Grammar Matters**

    Here are some points to keep in mind while writing queries in MindsDB:<br/>
    &nbsp;&nbsp;&nbsp;1. The `column_name = value` pairs may be joined by `AND` or `OR` keywords.<br/>
    &nbsp;&nbsp;&nbsp;2. Do not use any quotations for numerical values.<br/>
    &nbsp;&nbsp;&nbsp;3. Use single quotes for strings.
    &nbsp;&nbsp;&nbsp;4. The tables and column names are case sensitive.
</Warning>

On execution, we get:

```sql
+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| target_name       | target_name_explain                                                                                                                           |
+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| predicted_value   | {"predicted_value": 4394, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 4313, "confidence_upper_bound": 4475} |
+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
```

Where:

| Name                                     | Description                                                                                                                                                                          |
| ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `target_name`                            | Name of the column to be predicted.                                                                                                                                                  |
| `target_name_explain`                    | Object of the JSON type that contains the `predicted_value` and additional information such as `confidence`, `anomaly`, `truth`, `confidence_lower_bound`, `confidence_upper_bound`. |
| `predictor_name`                         | Name of the model used to make the prediction.                                                                                                                                       |
| `WHERE column_name = value AND ...`      | `WHERE` clause used to pass the data values for which the prediction is made.|

## Example

Let's predict the `rental_price` value using the `home_rentals_model` model for the property having `sqft=823`, `location='good'`, `neighborhood='downtown'`, and `days_on_market=10`.

```sql
SELECT sqft, location, neighborhood, days_on_market, rental_price, rental_price_explain
FROM mindsdb.home_rentals_model1
WHERE sqft=823
AND location='good'
AND neighborhood='downtown'
AND days_on_market=10;
```

On execution, we get:

```sql
+-------+----------+--------------+----------------+--------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| sqft  | location | neighborhood | days_on_market | rental_price | rental_price_explain                                                                                                                          |
+-------+----------+--------------+----------------+--------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
| 823   | good     | downtown     | 10             | 4394         | {"predicted_value": 4394, "confidence": 0.99, "anomaly": null, "truth": null, "confidence_lower_bound": 4313, "confidence_upper_bound": 4475} |
+-------+----------+--------------+----------------+--------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
```
