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76 lines
3.0 KiB
Markdown
76 lines
3.0 KiB
Markdown
# Build a predictive model with Python using SQL Server 2017 Machine Learning Services
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This sample shows how to create a predictive model in Python and operationalize it with SQL Server vNext.
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### Contents
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[About this sample](#about-this-sample)<br/>
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[Before you begin](#before-you-begin)<br/>
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[Sample details](#sample-details)<br/>
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[Related links](#related-links)<br/>
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<a name=about-this-sample></a>
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## About this sample
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Predictive modeling is a powerful way to add intelligence to your application. It enables applications to predict outcomes against new data.
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The act of incorporating predictive analytics into your applications involves two major phases:
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model training and model operationalization.
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In this sample, you will learn how to create a predictive model in python and operationalize it with SQL Server vNext.
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<!-- Delete the ones that don't apply -->
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- **Applies to:** SQL Server 2017 CTP2.0 or higher
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- **Key features:**SQL Server Machine Learning Services
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- **Workload:** SQL Server Machine Learning Services
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- **Programming Language:** T-SQL, Python
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- **Authors:** Nellie Gustafsson
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- **Update history:** Getting started tutorial for SQL Server ML Services - Python
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<a name=before-you-begin></a>
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## Before you begin
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To run this sample, you need the following prerequisites: </br>
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Download a DB backup file and restore it using Setup.sql. [Download DB](https://deve2e.azureedge.net/sqlchoice/static/TutorialDB.bak)
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**Software prerequisites:**
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<!-- Examples -->
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1. SQL Server 2017 CTP2.0 (or higher) with Machine Learning Services (Python) installed
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2. SQL Server Management Studio
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3. Python Tools for Visual Studio
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## Run this sample
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1. From SQL Server Management Studio or SQL Server Data Tools connect to your SQL Server vNext database and execute setup.sql to restore the sample DB you have downloaded </br>
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2. From SQL Server Management Studio or SQL Server Data Tools, open the Predictive Model Python.sql script </br>
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This script sets up: </br>
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Necessary tables </br>
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Creates stored procedure to train a model </br>
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Creates a stored procedure to predict using that model </br>
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Saves the predicted results to a DB table </br>
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3. You can also try the python script on its own. Just remember to point the Python environment to the corresponding path "C:\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES" if you run in-db Python Server, or
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"C:\Program Files\Microsoft SQL Server\140\PYTHON_SERVER" if you have the standalone Machine Learning Server installed.
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<a name=sample-details></a>
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## Sample details
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This sample shows how to create a predictive model with Python and generate predictions using the model and deploy that in SQL Server with SQL Server Machine Learning Services.
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### rental_prediction.py
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The Python script that generates a predictive model and uses it to predict rental counts
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### rental_prediction.sql
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Takes the Python code in Predictive Model.py and deploys it inside SQL Server. Creating stored procedures and tables for training, storing models and creating stored procedures for prediction.
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