| title | Data Science Scenarios and Solution Templates | Microsoft Docs | |
|---|---|---|
| ms.custom | ||
| ms.date | 04/18/2016 | |
| ms.prod | sql-server-2016 | |
| ms.reviewer | ||
| ms.suite | ||
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| ms.tgt_pltfrm | ||
| ms.topic | article | |
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| ms.assetid | 49e54fa9-9b28-44ba-b256-06dad4e8dece | |
| caps.latest.revision | 17 | |
| author | jeannt | |
| ms.author | jeannt | |
| manager | jhubbard |
Templates are sample solutions that demonstrate best practices and provide building blocks to help you implement a solution fast. Each template is designed to solve a specific problem, and includes sample data, R code (Microsoft R Server) and SQL stored procedures. The tasks in each template extend from data preparation and feature engineering to model training and scoring. The code can be run in an R IDE, with computations done in SQL Server, or by using a SQL client tool such as SQL Server management Studio.
You can use these templates to learn how [!INCLUDErsql_productname] works, and build and deploy your own solution by customizing he template to fit your own scenario.
For download and setup instructions, see How to Use the Templates at the end of this topic.
Online Fraud Detection Template (SQL Server R Services)
One of the important tasks for online business is to detect fraudulent transactions, and to identify the transactions made by stolen payment instruments or credentials, in order to reduce charge back losses. When fraudulent transactions are discovered, businesses typically take measures to block certain accounts as soon as possible, to prevent further losses. In this scenario, you'll learn how to use data from online purchase transactions to identify likely fraud. This methodology is one that you can easily apply to fraud detection in other domains.
In this template, you'll learn how to use data from online purchase transactions to identify likely fraud. Fraud detection is solved as a binary classification problem. The methodology used in this template can be easily applied to fraud detection in other domains.
Customer Churn Prediction Template (SQL Server R Services)
Analyzing and predicting customer churn is important in any industry where the loss of customers to competitors must be managed and prevented: banking, telecommunications, and retail, to name a few. The goal of churn analysis is to identify which customers are likely to churn, and then take appropriate actions to retain such customers and keep their business.
This template get you started with churn prevention by formulating the churn problem as a binary classification problem. It uses sample data from two sources, customer demographics and customer transactions, to classify customers as likely or unlikely to churn.
Predictive Maintenance Template (SQL Server 2016)
The goal of "data-driven" predictive maintenance is to increase the efficiency of maintenance tasks by capturing past failures and using that information to predict when or where a device might fail. The ability to forecast device obsolescence is particularly important for applications that rely on distributed data or sensors, as exemplified by the Internet of Things (IoT).
This template focuses on answering the question of “When will an in-service machine fail?” The input data represents simulated sensor measurements for aircraft engines. Data obtained from monitoring the engine’s current operation conditions, such as the current working cycle, settings, sensor measurements and so forth, are used to create three types of predictive models:
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Regression models, to predict how much longer an engine will last before it fails. The sample model predicts the metric Remaining Useful Life (RUL), also called Time to Failure (TTF).
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Classification models, to predict whether an engine is likely to fail.
The binary classification model predicts if an engine will fail within a certain time frame (number of days).
The multi-class classification model predicts whether a particular engine will fail, and if it will fail, provides a probable time window of failure. For example, for a given day, you can predict whether any device is likely to fail on the given day, or in some time period following the given day.
Energy Demand Forecasting Template with SQL Server R Services
This template demonstrates how to use SQL Server R Services to predict demand for electricity. The solution includes a demand simulator, all the R and T-SQL code needed to train a model, and stored procedures that you can use to generate and report predictions.
To download the files included in each template, you can use GitHub commands, or you can open the link and click Download Zip to save all files to your computer. When downloaded, the solution typically contains these folders:
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Data: Contains the sample data for each application.
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R: Contains all the R development code you need for the solution. The solution requires the libraries provided by Microsoft R Server, but can be opened and edited in any R IDE. The R code has been optimized so that computations are performed "in-database", by setting the compute context to a SQL Server instance.
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SQLR: Contains multiple .sql files that you can run in a SQL environment such as [!INCLUDEssManStudioFull] to create the stored procedures that perform related tasks such as data processing, feature engineering, and model deployment.
The folder also contains a PowerShell script that you can run to invoke all scripts and create the end-to-end environment.
Be sure to edit the script to suit your environment.
SQL Server R Services Tutorials
Announcing the Templates in Azure ML
New Predictive Maintenance Template