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As more enterprises delve into the realm of big data, understanding and applying machine learning algorithms in Databricks using SQL becomes increasingly important. In this article, we will commence a journey to explore some of the intricacies of SQL Machine Learning Libraries in Databricks.
What are SQL Machine Learning Libraries?
Databrick’s Machine Learning Libraries (MLlib) are scalable, powerful tools for data analysis. These libraries not only allow standard, straightforward data queries like:
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SELECT * FROM employee_data; |
But also the training of machine learning models and predicting future trends using these models. For example:
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CREATE OR REPLACE MODEL employee_attrition OPTIONS(model_type='logistic_reg', auto_class_weights='BALANCED') AS SELECT attrition, last_evaluation, satisfaction_level, average_montly_hours, time_spend_company FROM employee_data; |
This SQL command creates a Logistic Regression Model for predicting employee attrition based on several factors.
Getting Started with Databricks Machine Learning Libraries
To utilize the Databricks MLlib, you first need to ensure that you have access to a Databricks cluster that supports the Spark version you intend to use.
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CREATE DATABASE IF NOT EXISTS mllib_db; |
After creating the required database, load the desired dataset into the Databricks file system and create a table for the same. This can be achieved through the following code snippet:
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CREATE TABLE IF NOT EXISTS employee_data (attrition INT, last_evaluation FLOAT, satisfaction_level FLOAT, average_montly_hours INT, time_spend_company INT) USING DELTA LOCATION '/delta/employee_data'; |
Training a Machine Learning Model in Databricks
The next step involves selecting the desired algorithm or machine learning model and training it with the dataset. Here is how we can train a Linear Regression model:
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CREATE OR REPLACE MODEL mllib_db.linear_regression OPTIONS(model_type='linear_reg', solver='newton_cg') AS SELECT attrition, last_evaluation, satisfaction_level, average_montly_hours, time_spend_company FROM employee_data; |
The next section will demonstrate how to use this model to make predictions.
Making Predictions
After training the model, load it with the ML_PREDICT function and then use it. Below is a possible way of doing it:
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SELECT attrition, last_evaluation, satisfaction_level, average_monthly_hours, time_spend_company, ML_PREDICT(mllib_db.linear_regression, STRUCT(last_evaluation, satisfaction_level, average_monthly_hours, time_spend_company as features)) as predicted_attrition FROM employee_data; |
Conclusion
SQL Machine Learning Libraries in Databricks will open a new horizon of scalable, extensive, and powerful data analysis. The true power of MLlib’s can be exploited by integrating it with other features of Databricks like Databricks SQL, Delta Lake, and the Unified Analytics Platform. Go ahead, start your journey in exploring SQL Machine Learning Libraries in Databricks, and unleash the power of big data analysis.