Microsoft Analysis Services provides an integrated environment for creating and working with data mining models. You can easily bind to data sources, create and test multiple models on the same data, and deploy models for use in predictive analysis.
In the Basic Data Mining Tutorial, you learned how to use SQL Server Data Tools (SSDT) to create a data mining solution, and you built three models to support a targeted mailing campaign for analyzing customer purchasing behavior and for targeting potential buyers.
This intermediate tutorial builds on that experience and introduces several new scenarios, including common business requirements such as forecasting and market basket analysis. You will learn how to create a time series model, an association model, and a sequence clustering model. Finally, you will learn how to use neural network to explore correlations in data and to use logistic regression for predictions.
The lessons are independent and can be completed separately.
To complete the following tutorials, you should to be familiar with the data mining tools and with the mining model viewers that were introduced in the Basic Data Mining Tutorial.
All scenarios use the AdventureWorksDW2012 data source, but you will create different data source views for different scenarios. You can do the lessons in any order as long as you create the data source first.
After your success with the targeted mailing campaign, you have been asked to apply your knowledge of data mining to develop several new models for use in business planning. These include the following tasks:
Forecasting: You will create a time series model, to forecast the sales of products in different regions around the world. You will develop individual models for each region and learn how to use cross-prediction.
Market basket analysis: You will create an association model, to analyze groupings of products that are purchased during visits to the Adventure Works Cycles e-commerce site. Based on this market basket model, you can recommend products to customers.
Sequence analysis: You build a sequence clustering model, to analyze the order in which customers buy products. Based on this model, you can plan changes in Web site design or new product offerings.
Factor analysis: You use a neural network model to explore the possible causes of poor service quality in call center data. Based on the insights from the preliminary model, you will create a logistic regression model to predict strategies for improving customer experience.
Make sure that the following are installed:
Microsoft SQL Server 2014
Microsoft SQL Server Analysis Services
SQL Server with the AdventureWorksDW2012 database.
By default, the sample databases are not installed, to enhance security. To install the official databases for Microsoft SQL Server, visit the Microsoft SQL Sample Databases page and select the appropriate version of the sample database.