1. Module 14: Performing Predictive Analysis with Data Mining
Course 10778A
Module 14: Performing Predictive Analysis with Data Mining
Module 14
Performing Predictive Analysis with Data Mining

2. Module 14: Performing Predictive Analysis with Data Mining
Course 10778A
Module Overview
Module 14: Performing Predictive Analysis with Data Mining
Overview of Data Mining
Creating a Data Mining Solution
Validating a Data Mining Model
Consuming Data Mining Data

3. Lesson 1: Overview of Data Mining
Course 10778A
Lesson 1: Overview of Data Mining
Module 14: Performing Predictive Analysis with Data Mining
What Is the Purpose of Data Mining?
Components of an Analysis Services Data Mining Solution
Analysis Services Data Mining Algorithms
Data Mining Add-ins for Excel

4. What is the Purpose of Data Mining?
Course 10778A
What is the Purpose of Data Mining?
Module 14: Performing Predictive Analysis with Data Mining
Analysis of large data sets to reveal hidden patterns and trends
Data mining algorithms perform different types of statistical analyses for different scenarios
Data mining has a wide range of applications, for example:
Sales forecasting
Targeted advertising
Providing online recommendations
Risk assessment .

5. Components of an Analysis Services Data Mining Solution
Course 10778A
Components of an Analysis Services Data Mining Solution
Module 14: Performing Predictive Analysis with Data Mining
Data Mining Structure
Contains data source view
Contains case table and mining structure columns
Contains data mining models
Specifies training set and testing set
Case Table
Stores source data for data mining models
Columns have defined data types and content type
Data Mining Model
Uses a single data mining algorithm
Includes columns from data mining structure

6. Analysis Services Data Mining Algorithms
Course 10778A
Analysis Services Data Mining Algorithms
Module 14: Performing Predictive Analysis with Data Mining
Classification algorithms
Microsoft Decision Trees
Microsoft Neural Network
Microsoft Naive Bayes
Regression algorithms
Microsoft Time Series
Microsoft Linear Regression
Microsoft Logistic Regression
Segmentation or clustering algorithms
Microsoft Clustering
Association algorithms
Microsoft Association
Sequence analysis algorithms
Microsoft Sequence Clustering

7. Data Mining Add-ins for Excel
Course 10778A
Data Mining Add-ins for Excel
Module 14: Performing Predictive Analysis with Data Mining
Data Mining Client for Excel
Build, validate, and manage data models
Browse and query data mining models
You may also want to draw students’ attention to the new data mining cloud functionality. For more information, see the BILabs blog at
Table Analysis Tools for Excel
Perform a range of table analyses
No knowledge of data mining required

8. Demonstration: Performing Table Analysis in Excel
Course 10778A
Demonstration: Performing Table Analysis in Excel
Module 14: Performing Predictive Analysis with Data Mining
In this demonstration, you will see how to:
Create a connection to Analysis Services
Use the Excel table analysis tools
Task 1: Create a connection to Analysis Services
Ensure that the MIA-DC1 and MIA-SQLBI virtual machines are both running, and then log on to MIA- SQLBI as ADVENTUREWORKS\Student with the password Pa$$w0rd. Then in the D:\10778A\Demofiles\Mod14 folder, run Setup.cmd as Administrator.
In the D:\10778A\Demofiles\Mod14 folder, double-click Table Analysis.xlsx to open it in Excel.
On the ribbon, in the Table Tools group, click the Analyze tab, and then click <No Connection>.
In the Analysis Services Connections dialog box, click New, in the Connect to Analysis Services dialog box, in the Server name field, type (local), in the Catalog name drop-down list, click DMAddinsDB, and then click OK.
In the Analysis Services Connections dialog box, click Close.
Task 2: Use the Excel table analysis tools
On the ribbon, click Analyze Key Influencers.
In the SQL Server Data Mining - Analyze Key Influencers dialog box, in the Column Selection drop- down list, click Purchased Bike, and then click Run.
In the SQL Server Data Mining – Discrimination based on key influencers dialog box, in the Compare Value 1 drop-down list, click Yes, and then in the to Value 2 drop-down list, click No.
Click Add Report, and then click Close.
Review the Key Influencers Report for ‘Purchased Bike’ report. Note the values that most strongly correlate with a customer purchasing a bike.
Close Excel, and do not save changes to Table Analysis.xlsx.

9. Lesson 2: Creating a Data Mining Solution
Course 10778A
Lesson 2: Creating a Data Mining Solution
Module 14: Performing Predictive Analysis with Data Mining
Creating Data Mining Solutions
Editing Data Mining Structures and Models

10. Creating Data Mining Solutions
Course 10778A
Creating Data Mining Solutions
Module 14: Performing Predictive Analysis with Data Mining
SQL Server Data Tools
Data Mining Wizard
Data Mining Designer
Data Mining Client for Excel

11. Demonstration: Using the Data Mining Wizard
Course 10778A
Demonstration: Using the Data Mining Wizard
Module 14: Performing Predictive Analysis with Data Mining
In this demonstration, you will see how to:
Create a data mining project in SQL Server Data Tools
Create a data mining structure and a data mining model
Task 1: Create a data mining project in SQL Server Data Tools
Ensure that you have completed the previous demonstration.
Click Start, click All Programs, click Microsoft SQL Server 2012, and then click SQL Server Data Tools.
In SQL Server Data Tools, on the File menu, click New, and click Project.
In the New Project dialog box, click Analysis Services Multidimensional and Data Mining Project, in the Name field, type, Mine AW, in the Location field, browse to D:\10778A\Demofiles\Mod14, and then click OK.
In Solution Explorer, right-click Data Sources, and then click New Data Source.
In the Data Source Wizard, on the Welcome to the Data Source Wizard page, click Next, and then on the Select how to define the connection page, click New.
In the Connection Manager dialog box, in the Server name field, type localhost, in the Select or enter a database name drop-down list, click AdventureWorksDW, and then click OK.
In the Data Source Wizard, on the Select how to define the connection page, click Next, on the Impersonation Information page, click Use a specific Windows user name and password, in the User name field, type ADVENTUREWORKS\Student, in the Password field, type Pa$$w0rd, click Next, and then on the Completing the wizard page, click Finish.
In Solution Explorer, right-click Data Source Views, and then click New Data Source View.
In the Data Source View Wizard, on the Welcome to the Data Source View Wizard page, click Next, on the Select a Data Source page, ensure that Adventure Works DW is selected, and then click Next.
On the Select Tables and Views page, in the Available objects list, click ProspectiveBuyer (dbo), hold the Ctrl key and click vTargetMail (dbo), and click the > button to move the selected objects to the Included objects list. Then click Next.
On the Completing the Wizard page, in the Name field, type AW DW View, and then click Finish.
Task 2: Create a data mining structure and a data mining model
In Solution Explorer, right-click Mining Structures, click New Mining Structure, and then in the Data Mining Wizard, on the Welcome to the Data Mining Wizard page, click Next.
On the Select the Definition Method page, ensure that From existing relational database or data warehouse is selected, and then click Next.
On the Create the Data Mining Structure page, ensure that the Create mining structure with mining model radio button is selected, in the Which data mining technique do you want to use? drop-down list, ensure that Microsoft Decision Trees is selected, and then click Next.
On the Select a Data Source View page, select AW DW View and click Next.
On the Specify Table Types page, in the vTargetMail row, select the check box in the Case column, and then click Next.

12. Editing Data Mining Structures and Models
Course 10778A
Editing Data Mining Structures and Models
Module 14: Performing Predictive Analysis with Data Mining
SQL Server Data Tools
Data Mining Designer
Data Mining Client for Excel

13. Demonstration: Modifying a Data Mining Structure
Course 10778A
Demonstration: Modifying a Data Mining Structure
Module 14: Performing Predictive Analysis with Data Mining
In this demonstration, you will see how to:
Connect to a data mining model
Add a model to a data mining structure
Task 1: Connect to a data mining model
Ensure that you have completed the previous demonstrations in this module.
Click Start, click All Programs, click Microsoft Office, and then click Microsoft Excel 2010.
On the ribbon, click the Data Mining tab, in the Connection area, click DMAddinsDB ((local)), and then in the Analysis Services Connections dialog box, click New.
In the Connect to Analysis Services dialog box, in the Server name field, type (local), in the Catalog name drop-down list, select Mine AW, and then click OK.
In the Analysis Services Connections dialog box, click Close.
Task 2: Add a model to a data mining structure
On the ribbon, in the Data Modeling area, click Advanced, and then click Add Model to Structure.
In the Add Model to Structure Wizard, on the Getting Started with the Add Model to Structure Wizard page, click Next, on the Select Structure or Model page, ensure that the Purchase Prediction structure is selected, and then click Next.
On the Select Mining Algorithm page, in the Algorithm drop-down list, click Microsoft Naive Bayes, and then click Next.
On the Select Columns page, in the Bike Buyer row, in the Usage column, click Predict Only, in the Name Style row, in the Usage column, click Do not use, and then click Next.
On the Finish page, clear the Browse Model checkbox and click Finish.
On the ribbon, in the Management section, click Manage Models to verify that the model has been added to the data mining structure, and then click Close.
Close Excel without saving the workbook.

14. Lesson 3: Validating a Data Mining Model
Course 10778A
Lesson 3: Validating a Data Mining Model
Module 14: Performing Predictive Analysis with Data Mining
Overview of Data Mining Validation
Criteria for Validating Data Mining Models
Tools for Validating Data Mining Models

15. Overview of Data Mining Validation
Course 10778A
Overview of Data Mining Validation
Module 14: Performing Predictive Analysis with Data Mining
Validate data mining models to assess accuracy, reliability, and usefulness
Use prediction queries against a testing data set
Choose the most suitable data mining model for any given scenario

16. Criteria for Validating Data Mining Models
Course 10778A
Criteria for Validating Data Mining Models
Module 14: Performing Predictive Analysis with Data Mining
Accuracy
Strength of correlations between inputs and predictable outcomes
Accurate data mining models are not always reliable or useful
Reliability
Consistency of data mining model when used with multiple data sets
Reliable data is not always useful
Usefulness
Does the data mining model produce information that is useful to the business?

17. Tools for Validating Data Mining Models
Course 10778A
Tools for Validating Data Mining Models
Module 14: Performing Predictive Analysis with Data Mining
Validation tools are available in:
SQL Server Data Tools
Data Mining Add-Ins for Excel
SQL Server Management Studio
Lift chart – Compare model accuracy
Profit chart –Estimated profit gained by using a model
Classification matrix – Count true and false positives
Cross-validation report – Analyze data set by partitions

18. Demonstration: Validating Data Mining Models
Course 10778A
Demonstration: Validating Data Mining Models
Module 14: Performing Predictive Analysis with Data Mining
In this demonstration, you will see how to:
View a lift chart
View a profit chart
View a classification matrix
View a cross-validation report
Task 1: Create a lift chart
Ensure that you have completed the previous demonstrations in this module.
Start SQL Server Management Studio, and when prompted, connect to the localhost instance of Analysis Services by using Windows authentication.
In Object Explorer, expand Databases, expand Mine AW, and expand Mining Structures. Then right- click the Purchase Prediction mining structure and click View Lift Chart.
On the Input Selection tab, note that both mining models are selected with the Bike Buyer column as predictable, and that the test cases defined in the models themselves will be used for the validation.
Click the Lift Chart tab, and view the lift chart
Review the scores in the Mining Legend tab to see which of your models is the most accurate for this test data.
Task 2: Create a profit chart
In the Chart type drop-down list, select Profit Chart.
In the Profit Chart Settings dialog box, enter the following values, and then click OK: Population: , Fixed cost: 1000, Individual cost: 3, Revenue per individual: 10.
Review the chart and the Mining Legend tab to evaluate which mining model is likely to generate the most profitable marketing campaign based on the test data.
Task 3: Create a classification matrix
Click the Classification Matrix tab.
Review the matrix.
Task 4: Create a cross validation report
Click the Cross Validation tab.
Enter the following values, and click Get Results: Fold Count: 5, Max Cases: 5, Target Attribute: Bike Buyer, Target State: 1, Target Threshold: 0.1
View the resulting report, and note that for each mining model, the results include classifications for true positives, false positives, true negatives, and false negatives; and the likely lift gained by using the model.
Minimize SQL Server Management Studio – you will use it in the next demonstration.

19. Lesson 4: Consuming Data Mining Data
Course 10778A
Lesson 4: Consuming Data Mining Data
Module 14: Performing Predictive Analysis with Data Mining
Viewing Data Mining Results
Introduction to DMX
Using Data Mining Data in Reporting Services Reports

20. Viewing Data Mining Results
Course 10778A
Viewing Data Mining Results
Module 14: Performing Predictive Analysis with Data Mining
Mining Model Viewer in SQL Server Data Tools
Data Mining Client for Excel

21. Module 14: Performing Predictive Analysis with Data Mining
Course 10778A
Introduction to DMX
Module 14: Performing Predictive Analysis with Data Mining
Data Definition Language statements
CREATE
ALTER
DROP
IMPORT
EXPORT
SELECT INTO
Data Manipulation Language statements
SELECT
PREDICTION JOIN
INSERT INTO

22. Demonstration: Querying Data Mining Models
Course 10778A
Demonstration: Querying Data Mining Models
Module 14: Performing Predictive Analysis with Data Mining
In this demonstration, you will see how to:
Browse a data mining model
Query a data mining model
Task 1: Browse a data mining model
Ensure that you have completed the previous demonstrations in this module.
Maximize SQL Server Management Studio, and in Object Explorer, right-click the Purchase Prediction data mining structure and click Browse.
In the Mining Model drop-down list, ensure that Purchase - Bayes is selected, and on the Dependency Network tab, move the slider gradually from All Links to Strongest Links.
On the Attribute Profiles tab, view the color-coded indicators of the values for each column when compared to customers with a Bike Buyer value of 0 or 1.
On the Attribute Characteristics tab, in the Attribute drop-down list, ensure that Bike Buyer is selected, and in the Value drop-down list, select 1. Then view the probability for each other column value when the Bike Buyer value is 1.
On the Attribute Discrimination tab, in the Attribute drop-down list, ensure that Bike Buyer is selected, in the Value drop-down list, select 1, and in the Value 2 drop-down list, select 0. Then note how values for each other column favor a Bike Buyer value of 1 or 0.
In the Mining Model drop-down list, select Purchase Decision tree, and on the Decision Tree tab, in the Background drop-down list, select 1. Then view the decision tree to see how the other column values influence a value of 1 for Bike Buyer.
Task 2: Query a data mining model
In Object Explorer, right-click the Purchase Prediction mining structure and click Build Prediction Query.
In Query Designer, in the Mining Model pane, click Select Model, in the Select Mining Model dialog box, expand Purchase Prediction, click Purchase - Bayes, and then click OK.
In the Select Input Table(s) pane, click Select Case Table, in the Select Table dialog box, click ProspectiveBuyer (dbo), and then click OK.
Under the Mining Model pane, in the Source column, click ProspectiveBuyer table, and then in the Field column, click Address.
Under the row you just added, in the Source column, click Purchase – Bayes mining model, in the Field column, click Bike Buyer, and in the Criteria/Argument column, type =1.
Under the row you just added, in the Source column, click Prediction Function, in the Field column, click PredictProbability, in the Alias column, type Purchase Probability, and then drag the Bike Buyer column from the Purchase – Bayes model in the Mining Model pane to the Criteria/Argument column so that it contain the value [Purchase - Bayes].[Bike Buyer].
On the Mining Model menu, click Query to view the DMX code that has been generated.
On the Mining Model menu, click Result to view the query results.
Close SQL Server Management Studio without saving any changes.

23. Using Data Mining Data in Reporting Services Reports
Course 10778A
Using Data Mining Data in Reporting Services Reports
Module 14: Performing Predictive Analysis with Data Mining
Easy to understand even for non-technical users
Wide range of options for formatting and displaying data
Include the results from multiple models in a single report

24. Module 14: Performing Predictive Analysis with Data Mining
Course 10778A
Lab Scenario
Module 14: Performing Predictive Analysis with Data Mining
Students will perform the lab in the role of a data analyst in the Adventure Works Cycles company, and:
Use Excel table analysis tools to find key influencers for purchasing a bike.
Use SQL Server Data Tools to create a data mining model for predicting potential bike buyers.
Use Excel to create a data mining structure.
Validate the data mining structures using a variety of validation techniques.
Create a report that uses the data mining model to return a list of potential bike buyers.
Point out that the instructions in the lab are deliberately designed to be high-level so that students need to think carefully about what they are trying to accomplish and work out how best to proceed for themselves. Encourage students to read the scenario information carefully and collaborate with each other to meet the scenario requirements. Remind students that if they find a particular task or exercise too challenging, they can find step-by-step instructions in the lab answer key.
The marketing department at Adventure Works Cycles is planning a direct mail campaign. In order to maximize the effectiveness of the campaign, you have been asked to create a report that uses data mining techniques to identify the subset of potential customers who are most likely to purchase a bike.

25. Lab: Using Data Mining to Support a Marketing Campaign
Course 10778A
Lab: Using Data Mining to Support a Marketing Campaign
Module 14: Performing Predictive Analysis with Data Mining
Exercise 1: Using Table Analysis Tools
Exercise 2: Creating a Data Mining Model
Exercise 3: Using the Data Mining Add-in for Excel to Modify the Data Mining Structure
Exercise 4: Validating Data Mining Models
Exercise 5: Using a Data Mining Model in a Report
In this lab, students will create data mining models, use the Data Mining Add-in for Excel, validate data mining models, and create a Reporting Services report that contains data mining data.
Exercise 1
In this exercise, students use the table analysis tools in the Data Mining Add-in for Excel.
Exercise 2
In this exercise, students use SQL Server Data Tools to create a data mining model, and then deploy the model to the MIA-SQLBI Analysis Services instance.
Exercise 3
In this exercise, students use the Data Mining Add-in for Excel to create a new model and to review it.
Exercise 4
In this exercise, students use the Data Mining Add-in for Excel to validate the models.
Exercise 5
In this exercise, students create a Reporting Services report that uses data from the AW Data Mining database and then format the report.
Logon information
Virtual machine
MIA-SQLBI
User name
ADVENTURWORKS\Student
Password
Pa$$w0rd
Estimated time: 75 minutes

26. Module Review and Takeaways
Course 10778A
Module Review and Takeaways
Module 14: Performing Predictive Analysis with Data Mining
What is the purpose of data mining?
What are the major components of an Analysis Services data mining solution?
What are the main criteria for validating data mining models?
Review Questions
Point the students to the appropriate section in the course so that they are able to answer the questions presented in this section. Some guidance for discussing the answers to the questions is included below.
What is the purpose of data mining?
The purpose of data mining is to reveal patterns and trends that cannot be discovered by using standard analysis techniques.
What are the major components of an Analysis Services data mining solution?
Data Mining Structure, Data Mining Model, case table.
What are the main criteria for validating data mining models?
Accuracy, reliability, and usefulness.

27. Module 14: Performing Predictive Analysis with Data Mining
Course 10778A
Course Evaluation
Module 14: Performing Predictive Analysis with Data Mining
Remind students to complete the course evaluation.