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Introduction

In this tutorial, we’ll create and train a machine learning model, or as we call it, an AI Table or a predictor. By querying the model, we’ll predict the real estate sales using a multivariate time series strategy. Make sure you have access to a working MindsDB installation, either locally or at MindsDB Cloud. If you want to learn how to set up your account at MindsDB Cloud, follow this guide. Another way is to set up MindsDB locally using Docker or Python.
Check out the House Sales tutorial using Nixtla’s StastForecast engine here.
Let’s get started.

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Data Setup

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Connecting the Data

There are a couple of ways you can get the data to follow through with this tutorial.

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Connecting as a database

You can connect to a demo database that we’ve prepared for you. It contains the data used throughout this tutorial (the example_db.demo_data.house_sales table). 
CREATE DATABASE example_db
    WITH ENGINE = "postgres",
    PARAMETERS = {
        "user": "demo_user",
        "password": "demo_password",
        "host": "3.220.66.106",
        "port": "5432",
        "database": "demo"
};
Now you can run queries directly on the demo database. Let’s preview the data that we’ll use to train our predictor. 
SELECT *
FROM example_db.demo_data.house_sales
LIMIT 10;

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Connecting as a file

The dataset we use in this tutorial is the pre-processed version of the House Property Sales data. You can download the CSV data file here (we use the ma_lga_12345.csv file) and upload it via MindsDB SQL Editor. Follow this guide to find out how to upload a file to MindsDB. Now you can run queries directly on the file as if it were a table. Let’s preview the data that we’ll use to train our predictor. 
SELECT *
FROM files.house_sales
LIMIT 10;
Pay Attention to the QueriesFrom now on, we’ll use the files.house_sales file as a table. Make sure you replace it with example_db.demo_data.house_sales if you connect the data as a database.

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Understanding the Data

We use the house sales dataset, where each row is one house or one unit, to predict the MA column values. It tracks quarterly moving averages (MA) of house sales aggregated by real estate type and the number of bedrooms in each listing. Below is the sample data stored in the files.house_sales table. 
+----------+------+-----+--------+
|saledate  |MA    |type |bedrooms|
+----------+------+-----+--------+
|30/09/2007|441854|house|2       |
|31/12/2007|441854|house|2       |
|31/03/2008|441854|house|2       |
|30/06/2016|430880|unit |2       |
|30/09/2016|430654|unit |2       |
+----------+------+-----+--------+
Where:
ColumnDescriptionData TypeUsage
saledateThe date of sale.dateFeature
MAMoving average of the historical median price of the house or unit.integerLabel
typeType of property (house or unit).character varyingFeature
bedroomsNumber of bedrooms.integerFeature
Info “Labels and Features” A label is a column whose values will be predicted (the y variable in simple linear regression).
A feature is a column used to train the model (the x variable in simple linear regression).

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Training a Predictor

Let’s create and train the machine learning model. For that, we use the CREATE MODEL statement and specify the input columns used to train FROM (features) and what we want to PREDICT (labels). 
CREATE MODEL mindsdb.house_sales_predictor
FROM files
  (SELECT * FROM house_sales)
PREDICT MA
ORDER BY saledate
GROUP BY bedrooms, type
-- the target column to be predicted stores one row per quarter
WINDOW 8      -- using data from the last two years to make forecasts (last 8 rows)
HORIZON 4;    -- making forecasts for the next year (next 4 rows)
We use all of the columns as features, except for the MA column, whose values will be predicted. MindsDB makes it simple so that we don’t need to repeat the predictor creation process for every group, that is, for every distinct number of bedrooms or for every distinct type of real estate. Instead, we just group by both the bedrooms and type columns, and the predictor learns from all series and enables forecasts for all of them!

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Status of a Predictor

A predictor may take a couple of minutes for the training to complete. You can monitor the status of the predictor by using this SQL command: 
DESCRIBE house_sales_predictor;
If we run it right after creating a predictor, we get this output: 
+------------+
| status     |
+------------+
| generating |
+------------+
A bit later, this is the output: 
+----------+
| status   |
+----------+
| training |
+----------+
And at last, this should be the output: 
+----------+
| status   |
+----------+
| complete |
+----------+
Now, if the status of our predictor says complete, we can start making predictions!

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Making Predictions

You can make predictions by querying the predictor joined with the data table. The SELECT statement lets you make predictions for the label based on the chosen features for a given time period. Usually, you want to know what happens right after the latest training data point that was fed. We have a special keyword for that, the LATEST keyword. 
SELECT m.saledate AS date, m.MA AS forecast, MA_explain
FROM mindsdb.house_sales_predictor AS m
JOIN files.house_sales AS t
WHERE t.saledate > LATEST
AND t.type = 'house'
AND t.bedrooms = 2
LIMIT 4;
On execution, we get: 
+-------------+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| date        | forecast          | MA_explain                                                                                                                                                                                    |
+-------------+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2019-12-31  | 441413.5849598734 | {"predicted_value": 441413.5849598734, "confidence": 0.99, "anomaly": true, "truth": null, "confidence_lower_bound": 440046.28237074096, "confidence_upper_bound": 442780.88754900586}        |
| 2020-04-01  | 443292.5194586229 | {"predicted_value": 443292.5194586229, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 427609.3325864327, "confidence_upper_bound": 458975.7063308131}        |
| 2020-07-02  | 443292.5194585953 | {"predicted_value": 443292.5194585953, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 424501.59192981094, "confidence_upper_bound": 462083.4469873797}       |
| 2020-10-02  | 443292.5194585953 | {"predicted_value": 443292.5194585953, "confidence": 0.9991, "anomaly": null, "truth": null, "confidence_lower_bound": 424501.59192981094, "confidence_upper_bound": 462083.4469873797}       |
+-------------+-------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Please note that in the SELECT statement, we select m.saledate instead of t.saledate because we make predictions for future dates that are not in the data table. Now, try changing the type column value to unit, or the bedrooms column value to any number between 1 to 5, and check how the forecasts vary. This is because MindsDB recognizes each grouping as being its own different time series.

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What’s Next?

Have fun while trying it out yourself! If this tutorial was helpful, please give us a GitHub star here.