Skip to content

Determining Body Fat Percentage

Machine Learning powered data analysis can be performed quickly and efficiently by MindsDB to enable individuals to make accurate predictions for certain metrics based on a variety of associated values. MindsDB enables you to make predictions automatically using just SQL commands, all the ML workflow is automated, and abstracted as virtual “AI tables” in your database so you may start getting insights from forecasts right away. In this tutorial, we'll be using MindsDB and a MySQL database to predict body fat percentage based on several body part measurement criteria.


  • A working copy of MindsDB. Check out the Docker or PyPi installation guides to install locally, or get up and running in seconds using MindsDB Cloud.
  • Access to a MySQL Database.
  • The dataset being used for this tutorial. Get it from Kaggle.
  • mysql-client, DBeaver, MySQL Workbench, etc. to connect to the database and also to use MindsDB's MySQL API.

Setting up the Database

In this section you'll initialize your MySQL database and populate it with the Body Fat Prediction dataset.

Data Overview

For this tutorial, we'll be using the Body Fat Prediction dataset available at Kaggle. Each row represents one person and we'll train an ML model to help us predict an individual's body fat percentage using MindsDB. Below is a short description of each feature of the data: - Density: Individual's body density as determined by underwater weighing (float) - BodyFat: The individual's determined body fat percentage (float). This is what we want to predict - Age: Age of the individual (int) - Weight: Weight of the individual in pounds (float) - Height: Height of the individual in inches (float) - Neck: Circumference of the individual's neck in cm (float) - Chest: Circumference of the individual's chest in cm (float) - Abdomen: Circumference of the individual's abdomen in cm (float) - Hip: Circumference of the individual's hips in cm (float) - Thigh: Circumference of the individual's thigh in cm (float) - Knee: Circumference of the individual's knee in cm (float) - Ankle: Circumference of the individual's ankle in cm (float) - Biceps: Circumference of the individual's biceps in cm (float) - Forearm: Circumference of the individual's forearm in cm (float) - Wrist: Circumference of the individual's wrist in cm (float)

First, connect to your MySQL instance. Next, create the database:


Now switch to the newly created database:

USE bodyfat;

Now create the table into which the dataset will be imported with the following schema:

CREATE TABLE bodyfat (
    Density FLOAT,
    BodyFat FLOAT,
    Age INT,
    Weight FLOAT,
    Height FLOAT,
    Neck FLOAT,
    Chest FLOAT,
    Abdomen FLOAT,
    Hip FLOAT,
    Thigh FLOAT,
    Knee FLOAT,
    Ankle FLOAT,
    Biceps FLOAT,
    Forearm FLOAT,
    Wrist FLOAT

Now import the Body Fat Prediction dataset into the newly created table with:

LOAD DATA LOCAL INFILE '/path/to/file/bodyfat.csv'
INTO TABLE bodyfat

If successful, you should see output similar to:

Query OK, 252 rows affected (0.028 sec)
Records: 252  Deleted: 0  Skipped: 0  Warnings: 0

To test that the dataset was correctly imported, you can execute:

SELECT Age, BodyFat FROM bodyfat LIMIT 10;

Which should return output similar to:

| Age  | BodyFat |
|   23 |    12.3 |
|   22 |     6.1 |
|   22 |    25.3 |
|   26 |    10.4 |
|   24 |    28.7 |
|   24 |    20.9 |
|   26 |    19.2 |
|   25 |    12.4 |
|   25 |     4.1 |
|   23 |    11.7 |
10 rows in set (0.001 sec)

At this point, you have completed setting up the MySQL Database and loading the Body Fat Prediction dataset!

Connect MindsDB to the Database

In this section, you'll connect MindsDB to the database you've just set up.

Start by heading to the MindsDB GUI. If you use an open-source version, launch MindsDB Studio, but for this tutorial, we'll be using MindsDB Cloud to connect to our database.

Click on Databases in the upper left, then on Add Database in the lower right. In the popup screen, fill in the details for your MySQL database, and specify a name for the integration (here, we chose bodyfat_integration). You can test if the database is connectable by clicking Click Here to Test Connection, and if all is well, click on the Connect button:

MindsDB Database Connection

You should now see your new database integration appear in the MindsDB GUI: MindsDB Database Connection Established

At this point, you have successfully connected MindsDB to the database!

Connect to the MindsDB MySQL API

Now, you'll connect to the MindsDB MySQL API to enable the use of SQL commands to train ML models and make predictions. For this tutorial, we'll be connecting to MindsDB Cloud using a commandline mysql client:

mysql -h --port 3306 -u -p

Execute the above command substituting the email address you used to sign up for MindsDB Cloud, followed by entering your password, and you should see output similar to:

Enter password: 
Welcome to the MariaDB monitor.  Commands end with ; or \g.
Your MySQL connection id is 1
Server version: 5.7.1-MindsDB-1.0 (MindsDB)

Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

MySQL [(none)]> 

You have now connected to the MindsDB MySQL API successfully!

Using SQL Commands to Train ML Models

We will now train a new machine learning model for the dataset we've created. In MindsDB terms it is called a Predictor. We will show how to create it automatically, but there is also a way to fine tune it, if you know what you are doing (check the MindsDB docs). Go to your mysql-client and run the following command:

USE mindsdb;

Now, we have to create a predictor based on the following syntax:

CREATE PREDICTOR predictor_name
FROM integration_name 
(SELECT column_name, column_name2 FROM table_name) as ds_name
PREDICT column_name as column_alias;

For our case, we'll enter the following command:

CREATE PREDICTOR bodyfat_predictor
FROM bodyfat_integration (
        SELECT * FROM bodyfat
) PREDICT Bodyfat;

You should see output similar to the following:

Query OK, 0 rows affected (3.077 sec)

At this point, the predictor will immediately begin training. Check the status of the training by entering the command:

SELECT * FROM mindsdb.predictors WHERE name='bodyfat_predictor';

When complete, you should see output similar to the following:

| name              | status   | accuracy           | predict | select_data_query | external_datasource | training_options |
| bodyfat_predictor | complete | 0.9909730079130395 | BodyFat |                   |                     |                  |
1 row in set (0.101 sec)

As you can see, the predictor training has completed with an accuracy of approximately 99%. At this point, you have successfully trained an ML model for our Body Fat Prediction dataset!

Using SQL Commands to Make Predictions

Now, we can query the model and make predictions based on our input data by using SQL statements.

Let's imagine an individual aged 25, with a body density of 1.08, a weight of 170lb, a height of 70in, a neck circumference of 38.1cm, a chest circumference of 103.5cm, an abdomen circumference of 85.4cm, a hip circumference of 102.2cm, a thigh circumference of 63.0cm, a knee circumference of 39.4cm, an ankle circumference of 22.8cm, a biceps circumference of 33.3cm, a forearm circumference of 28.7cm, and a wrist circumference of 18.3cm. We can predict this person's body fat percentage by issuing the following command:

SELECT BodyFat, BodyFat_confidence, BodyFat_explain AS Info
FROM mindsdb.bodyfat_predictor 
WHERE when_data='{"Density": 1.08, "Age": 25, "Weight": 170, "Height": 70, "Neck": 38.1, "Chest": 103.5, "Abdomen": 85.4, "Hip": 102.2, "Thigh": 63.0, "Knee": 39.4, "Ankle": 22.8, "Biceps": 33.3, "Forearm": 28.7, "Wrist": 18.3}';

This should return output similar to:

| BodyFat           | BodyFat_confidence | Info                                                                                                                                                                                  |
| 8.968581383955318 | 0.99               | {"predicted_value": 8.968581383955318, "confidence": 0.99, "confidence_lower_bound": 5.758912817402102, "confidence_upper_bound": 12.178249950508533, "anomaly": null, "truth": null} |
1 row in set (0.464 sec)

As you can see, with around 99% confidence, MindsDB predicted the body fat percentage for this individual at 8.97%. You can at this point feel free to alter the prospective individual's bodypart measurement parameters and make additional prediction queries if you'd like.

Making Batch Predictions using the JOIN Command

The above example showed how to make predictions for a single individual's bodyfat, but what if you had a table of bodypart measurements for a number of individuals, and wanted to make predictions for them all? This is possible using the JOIN command, which allows for the combining of rows from a database table and the prediction model table on a related column.

The basic syntax to use the JOIN command is:

SELECT t.column_name1, t.column_name2, FROM integration_name.table AS t 
JOIN mindsdb.predictor_name AS p WHERE t.column_name IN (value1, value2, ...);

For our purposes, we'll re-use the original data set, taking the Age, Density, Weight, Height, Neck circumference, Chest circumference, Abdomen circumference, and Hip circumference fields. We'll also include the original BodyFat percentage to compare our predicted values against the originals. Execute the following command:

SELECT t.Age, t.Density, t.Weight, t.Height, t.Neck, t.Chest, t.Abdomen, t.Hip, t.BodyFat, p.BodyFat AS predicted_BodyFat
FROM bodyfat_integration.bodyfat AS t JOIN mindsdb.bodyfat_predictor AS p

This should return an output table similar to the following:

| Age  | Density | Weight | Height | Neck | Chest | Abdomen | Hip   | BodyFat | predicted_BodyFat  |
| 23   | 1.0708  | 154.25 | 67.75  | 36.2 | 93.1  | 85.2    | 94.5  | 12.3    | 12.475132275112655 |
| 22   | 1.0853  | 173.25 | 72.25  | 38.5 | 93.6  | 83.0    | 98.7  | 6.1     | 6.07133439184195   |
| 22   | 1.0414  | 154.0  | 66.25  | 34.0 | 95.8  | 87.9    | 99.2  | 25.3    | 25.156538398443754 |
| 26   | 1.0751  | 184.75 | 72.25  | 37.4 | 101.8 | 86.4    | 101.2 | 10.4    | 10.696461885516461 |
| 24   | 1.034   | 184.25 | 71.25  | 34.4 | 97.3  | 100.0   | 101.9 | 28.7    | 28.498772660802427 |
5 rows in set (1.091 sec)

As you can see, a prediction has been generated for each row in the input table. Additionally, our predicted bodyfat percentages align closely with the original values! Note that even though we chose only to display the Age, Density, Weight, Height, Neck, Chest, Abdomen, and Hip measurements in this example, the predicted_BodyFat field was determined by taking into consideration all of the data fields in the original bodyfat table (as this table was JOINed with the bodyfat_predictor table, from which we selected the specified fields). In order to make predictions based ONLY on the specified fields, we would have to create a new table containing only those fields, and JOIN that with the bodyfat_predictor table!

You are now done with the tutorial! 🎉

Please feel free to try it yourself. Sign up for a free MindsDB account to get up and running in 5 minutes, and if you need any help, feel free to ask in Slack or Github.

For more tutorials like this check out MindsDB documentation.