How to use MySQL database for forecasting and predictive analytics?

How to use MySQL database for prediction and predictive analysis?

Overview:
Forecasting and predictive analytics play an important role in data analysis. MySQL, a widely used relational database management system, can also be used for prediction and predictive analysis tasks. This article will introduce how to use MySQL for prediction and predictive analysis, and provide relevant code examples.

1. Data preparation:
   First, we need to prepare relevant data. Suppose we want to do sales forecasting, we need a table with sales data. In MySQL, we can create a simple sales data table using the following statement:

CREATE TABLE sales (
    id INT AUTO_INCREMENT PRIMARY KEY,
    date DATE,
    product_name VARCHAR(255),
    quantity INT,
    price DECIMAL(10,2)
);

Next, we can insert some sample data into the table:

INSERT INTO sales (date, product_name, quantity, price)
VALUES
    ('2020-01-01', 'product1', 100, 10.99),
    ('2020-01-02', 'product2', 200, 20.99),
    ('2020-01-03', 'product3', 300, 30.99),
    ('2020-01-04', 'product4', 400, 40.99),
    ('2020-01-05', 'product5', 500, 50.99);

2. Sales forecasting using linear regression:
   Next, we will use a linear regression model to forecast sales data. In MySQL, we can use the built-in linear regression function "LINEST" to achieve this.

First, we need to create a table to save the coefficients and intercepts of the regression model:

CREATE TABLE sales_regression (
    id INT AUTO_INCREMENT PRIMARY KEY,
    coefficient DECIMAL(10,2),
    intercept DECIMAL(10,2)
);

Then, we can use the following SQL statement to perform linear regression calculations and save the results Go to the table:

INSERT INTO sales_regression (coefficient, intercept)
SELECT
    (n * SUM(x * y) - SUM(x) * SUM(y)) / (n * SUM(x * x) - SUM(x) * SUM(x)),
    (SUM(y) - (n * SUM(x * y) - SUM(x) * SUM(y)) / (n * SUM(x * x) - SUM(x) * SUM(x)) * SUM(x)) / n
FROM (
    SELECT
        @row_number := @row_number + 1 AS n,
        quantity AS x,
        price AS y
    FROM
        sales, (SELECT @row_number := 0) AS t
    ORDER BY
        date
) AS t;

Now, we have obtained the coefficients and intercepts of the linear regression model. We can use these values ​​to make sales forecasts. For example, we can use the following SQL statement to predict sales on a certain day:

SELECT 
    '2020-01-06' AS date,
    coefficient * 600 + intercept AS predicted_sales
FROM
    sales_regression;

3. Use time series analysis for sales forecasting:
   In many cases, sales data has temporal nature. Therefore, it is common to use time series analysis techniques for sales forecasting. MySQL provides some built-in functions for time series analysis, such as "AVG" (average value), "LAG" (time lag) and "LEAD" (time advance).

Suppose we want to use the moving average method for sales forecasting. We can calculate the moving average sales using the following SQL statement:

SELECT
    date,
    AVG(price) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_average
FROM
    sales;

4. Conclusion:
   In this article, we have introduced how to use the MySQL database for forecasting and predictive analytics. We show how to use linear regression and time series analysis for sales forecasting, and provide relevant code examples. I hope this content will be helpful to you in your data analysis tasks.

Reference:

• MySQL official documentation: https://dev.mysql.com/doc/

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