Feature Engineering in MindsDB

The more data you have, the more accurate predictions you get.

We recommend you provide the predictor with as many historical data rows and data columns as possible to make your predictions even more accurate. The examples presented here prove this hypothesis.

If you want to follow the examples, please sign up for the MindsDB Cloud account here.

Prerequisites

The base table is available in the example_db integration in the MindsDB Cloud Editor. In order to be able to use it, you must first create a database 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"
};

On execution, we get:

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

Once that’s done, you can run the following commands with us.

Example: Adding More Data Columns

Introduction

Here, we’ll create several predictors using the same table, increasing the number of data columns with each step.

We start with the base table and create a predictor based on it. Then we add two columns to our base table and again create a predictor based on the enhanced table. At last, we add another two columns and create a predictor.

By comparing the accuracies of the predictors, we’ll find that more data results in more accurate predictions.

Let’s get started.

Let’s Run the Codes

Here, we go through the codes for the base table and enhanced base tables simultaneously.

Data Setup

Let’s prepare and verify the data. Here, we create the views and query them to ensure the input for the predictors is in order.

Let’s start by querying the data from the example_db.demo_data.used_car_price table, which is our base table.

SELECT *
FROM example_db.demo_data.used_car_price
LIMIT 5;

On execution, we get:

+-----+----+-----+------------+-------+--------+---+----+----------+
|model|year|price|transmission|mileage|fueltype|tax|mpg |enginesize|
+-----+----+-----+------------+-------+--------+---+----+----------+
| A1  |2017|12500|Manual      |15735  |Petrol  |150|55.4|1.4       |
| A6  |2016|16500|Automatic   |36203  |Diesel  |20 |64.2|2         |
| A1  |2016|11000|Manual      |29946  |Petrol  |30 |55.4|1.4       |
| A4  |2017|16800|Automatic   |25952  |Diesel  |145|67.3|2         |
| A3  |2019|17300|Manual      |1998   |Petrol  |145|49.6|1         |
+-----+----+-----+------------+-------+--------+---+----+----------+

Where:

Name	Description
`model`	Model of the car.
`year`	Year of production.
`price`	Price of the car.
`transmission`	Transmission (`Manual`, or `Automatic`, or `Semi-Auto`).
`mileage`	Mileage of the car.
`fueltype`	Fuel type of the car.
`tax`	Tax.
`mpg`	Miles per gallon.
`enginesize`	Engine size of the car.

Dropping a View If you want to drop a view, run the command DROP VIEW view_name;.

Creating Predictors

Now, we create predictors based on the example_db.demo_data.used_car_price table and its extensions.

CREATE MODEL mindsdb.price_predictor
FROM example_db
(SELECT * FROM demo_data.used_car_price)
PREDICT price;

On execution, we get:

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

Dropping a Predictor If you want to drop a predictor, run the command DROP MODEL predictor_name;.

Predictor Status

Finally, let’s check the predictor status whose value is generating at first, then training, and at last, complete.

DESCRIBE price_predictor;

On execution, we get:

+---------------+--------+--------+---------+-------------+---------------+------+--------------------------------------+----------------+
|name           |status  |accuracy|predict  |update_status|mindsdb_version|error |select_data_query                     |training_options|
+---------------+--------+--------+---------+-------------+---------------+------+--------------------------------------+----------------+
|price_predictor|complete|0.963   |price    |up_to_date   |22.10.2.1      |[NULL]|SELECT * FROM demo_data.used_car_price|                |
+---------------+--------+--------+---------+-------------+---------------+------+--------------------------------------+----------------+

Accuracy Comparison

Once the training process of all three predictors completes, we see the accuracy values.

For the base table, we get an accuracy value of 0.963.
For the base table with two more data columns, we get an accuracy value of 0.965. The accuracy value increased, as expected.
For the base table with four more data columns, we get an accuracy value of 0.982. The accuracy value increased again, as expected.

True vs Predicted Price Comparison

Let’s compare how close the predicted price values are to the true price.

+-------+-------+---------------+-----------+-----------+--------------+----------------+----------------+---------------+
| model | year  | transmission  | fueltype  | mileage   | true_price   | pred_price_1   | pred_price_2   | pred_price_3  |
+-------+-------+---------------+-----------+-----------+--------------+----------------+----------------+---------------+
| A1    | 2017  | Manual        | Petrol    | 7620      | 14440        | 17268          | 17020          | 14278         |
| A6    | 2016  | Automatic     | Diesel    | 20335     | 18982        | 17226          | 17935          | 19016         |
| A3    | 2018  | Semi-Auto     | Diesel    | 9058      | 19900        | 25641          | 23008          | 21286         |
+-------+-------+---------------+-----------+-----------+--------------+----------------+----------------+---------------+

The prices predicted by the third predictor, having the highest accuracy value, are the closest to the true price, as expected.

Example: Joining Data Tables

Introduction

We start by creating a predictor from the car_sales table. Then, we add more data by joining the car_sales and car_info tables. We create a predictor based on the car_sales_info view.

Let’s get started.

Let’s Run the Codes

Here, we go through the codes using partial tables and the full table after joining the data.

Data Setup

Here is the car_sales table:

SELECT *
FROM example_db.demo_data.car_sales
LIMIT 5;

On execution, we get:

+-----+----+-----+------------+-------+--------+---+
|model|year|price|transmission|mileage|fueltype|tax|
+-----+----+-----+------------+-------+--------+---+
| A1  |2017|12500|Manual      |15735  |Petrol  |150|
| A6  |2016|16500|Automatic   |36203  |Diesel  |20 |
| A1  |2016|11000|Manual      |29946  |Petrol  |30 |
| A4  |2017|16800|Automatic   |25952  |Diesel  |145|
| A3  |2019|17300|Manual      |1998   |Petrol  |145|
+-----+----+-----+------------+-------+--------+---+

Where:

Name	Description
`model`	Model of the car.
`year`	Year of production.
`price`	Price of the car.
`transmission`	Transmission (`Manual`, or `Automatic`, or `Semi-Auto`).
`mileage`	Mileage of the car.
`fueltype`	Fuel type of the car.
`tax`	Tax.

And here is the car_info table:

SELECT *
FROM example_db.demo_data.car_info
LIMIT 5;

On execution, we get:

+-----+----+------------+---------+-----+----------+
|model|year|transmission|fueltype |mpg  |enginesize|
+-----+----+------------+---------+-----+----------+
| A1  |2010|Automatic   |Petrol   |53.3 |1.4       |
| A1  |2011|Manual      |Diesel   |70.6 |1.6       |
| A1  |2011|Manual      |Petrol   |53.3 |1.4       |
| A1  |2012|Automatic   |Petrol   |50.6 |1.4       |
| A1  |2012|Manual      |Diesel   |72.95|1.7       |
+-----+----+------------+---------+-----+----------+

Where:

Name	Description
`model`	Model of the car.
`year`	Year of production.
`transmission`	Transmission (`Manual`, or `Automatic`, or `Semi-Auto`).
`fueltype`	Fuel type of the car.
`mpg`	Miles per gallon.
`enginesize`	Engine size of the car.

Let’s join the car_sales and car_info tables on the model, year, transmission, and fueltype columns.

SELECT * FROM example_db
(
  SELECT s.*, i.mpg, i.enginesize
  FROM demo_data.car_sales s
  JOIN demo_data.car_info i
  ON s.model=i.model
  AND s.year=i.year
  AND s.transmission=i.transmission
  AND s.fueltype=i.fueltype
)
LIMIT 5;

Nested SELECT Statements Please note that we use the nested SELECT statement in order to trigger native query at the MindsDB Cloud Editor. Here, the example_db database is a PostgreSQL database, so we trigger PostgreSQL-native syntax.

On execution, we get:

+-----+----+-----+------------+-------+--------+---+----+----------+
|model|year|price|transmission|mileage|fueltype|tax|mpg |enginesize|
+-----+----+-----+------------+-------+--------+---+----+----------+
| A1  |2010|9990 |Automatic   |38000  |Petrol  |125|53.3|1.4       |
| A1  |2011|4250 |Manual      |116000 |Diesel  |20 |70.6|1.6       |
| A1  |2011|6475 |Manual      |45000  |Diesel  |0  |70.6|1.6       |
| A1  |2011|6295 |Manual      |107000 |Petrol  |125|53.3|1.4       |
| A1  |2011|7495 |Manual      |60700  |Petrol  |125|53.3|1.4       |
+-----+----+-----+------------+-------+--------+---+----+----------+

Now, we create a view based on the JOIN query:

CREATE VIEW car_sales_info 
(
    SELECT * FROM example_db
    (
    SELECT s.*, i.mpg, i.enginesize
    FROM demo_data.car_sales s
    JOIN demo_data.car_info i
    ON s.model=i.model
    AND s.year=i.year
    AND s.transmission=i.transmission
    AND s.fueltype=i.fueltype
    )
);

On execution, we get:

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

Let’s verify the view by selecting from it.

SELECT *
FROM mindsdb.car_sales_info
LIMIT 5;

On execution, we get:

+-----+----+-----+------------+-------+--------+---+----+----------+
|model|year|price|transmission|mileage|fueltype|tax|mpg |enginesize|
+-----+----+-----+------------+-------+--------+---+----+----------+
| A1  |2010|9990 |Automatic   |38000  |Petrol  |125|53.3|1.4       |
| A1  |2011|4250 |Manual      |116000 |Diesel  |20 |70.6|1.6       |
| A1  |2011|6475 |Manual      |45000  |Diesel  |0  |70.6|1.6       |
| A1  |2011|6295 |Manual      |107000 |Petrol  |125|53.3|1.4       |
| A1  |2011|7495 |Manual      |60700  |Petrol  |125|53.3|1.4       |
+-----+----+-----+------------+-------+--------+---+----+----------+

Creating Predictors

Let’s create a predictor with the car_sales table as input data.

CREATE MODEL mindsdb.price_predictor_car_sales
FROM example_db
  (SELECT * FROM demo_data.car_sales)
PREDICT price;

On execution, we get:

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

Now, let’s create a predictor for the table that is a JOIN between the car_sales and car_info tables.

CREATE MODEL mindsdb.price_predictor_car_sales_info
FROM mindsdb
  (SELECT * FROM car_sales_info)
PREDICT price;

On execution, we get:

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

Predictor Status

Next, we check the status of both predictors.

We start with the predictor based on the partial table.

DESCRIBE price_predictor_car_sales;

On execution, we get:

+-------------------------+--------+--------+---------+-------------+---------------+------+---------------------------------+----------------+
|name                     |status  |accuracy|predict  |update_status|mindsdb_version|error |select_data_query                |training_options|
+-------------------------+--------+--------+---------+-------------+---------------+------+---------------------------------+----------------+
|price_predictor_car_sales|complete|0.912   |price    |up_to_date   |22.10.2.1      |[NULL]|SELECT * FROM demo_data.car_sales|                |
+-------------------------+--------+--------+---------+-------------+---------------+------+---------------------------------+----------------+

And now, for the predictor based on the full table.

DESCRIBE price_predictor_car_sales_info;

On execution, we get:

+------------------------------+--------+--------+---------+-------------+---------------+------+----------------------------+----------------+
|name                          |status  |accuracy|predict  |update_status|mindsdb_version|error |select_data_query           |training_options|
+------------------------------+--------+--------+---------+-------------+---------------+------+----------------------------+----------------+
|price_predictor_car_sales_info|complete|0.912   |price    |up_to_date   |22.10.2.1      |[NULL]|SELECT * FROM car_sales_info|                |
+------------------------------+--------+--------+---------+-------------+---------------+------+----------------------------+----------------+

Accuracy Comparison

The accuracy values are 0.912 for both the predictors. The predictor already learns how the combination of model+year+transmission+fueltype affects the price, so joining more data columns doesn’t play a role in this particular example.

Overview

Concepts

Use Cases

Feature Engineering in MindsDB

Prerequisites

Example: Adding More Data Columns

Introduction

Let’s Run the Codes

Data Setup

Creating Predictors

Predictor Status

Accuracy Comparison

True vs Predicted Price Comparison

Example: Joining Data Tables

Introduction

Let’s Run the Codes

Data Setup

Creating Predictors

Predictor Status

Accuracy Comparison

Overview

Concepts

Use Cases

​Prerequisites

​Example: Adding More Data Columns

​Introduction

​Let’s Run the Codes

​Data Setup

​Creating Predictors

​Predictor Status

​Accuracy Comparison

​True vs Predicted Price Comparison

​Example: Joining Data Tables

​Introduction

​Let’s Run the Codes

​Data Setup

​Creating Predictors

​Predictor Status

​Accuracy Comparison

Prerequisites

Example: Adding More Data Columns

Introduction

Let’s Run the Codes

Data Setup

Creating Predictors

Predictor Status

Accuracy Comparison

True vs Predicted Price Comparison

Example: Joining Data Tables

Introduction

Let’s Run the Codes

Data Setup

Creating Predictors

Predictor Status

Accuracy Comparison