SAS VISUAL STATISTICS 6.4 QUICK START GUIDE

Transcription

1 SAS VISUAL STATISTICS 6.4 QUICK START GUIDE

2 WELCOME TO SAS VISUAL STATISTICS SAS Visual Statistics is an extension of SAS Visual Analytics that s designed for statisticians, data scientists and business analysts who need to analyze diverse data as well as create and evaluate predictive models to quickly get precise insights. In addition to its visual analytics capabilities, which include exploratory analysis, data visualization, descriptive statistics and the ability to uncover relevant patterns, SAS Visual Statistics enables creating, testing and comparing models based on the patterns discovered in the explorer. This brief overview provides a sample case study for SAS Visual Statistics. It s meant to get you started quickly, with a relatively modest data set example (1.6 million rows). For more in-depth information, please consult the online user guide, available via the Help menu. Or visit sas.com/en_us/software/analytics/visual-statistics.html.

3 BUT FIRST, A WORD ABOUT VISUAL ANALYTICS Since data exploration and data summary are an integral part of the model building process, we recommend you first learn the exploratory capabilities available in SAS Visual Analytics. Visit sas.com/en_us/offers/13q2/va-try-before-you-buy/register.html to learn more about visual analytics and try it yourself.

4 OVERVIEW OF THE DATA In the scenario we re about to explore, a supermarket offers a new line of organic products. The store s management wants to determine which customers are likely to purchase these products. The supermarket has a customer loyalty program. As an initial buyer incentive plan, participants received coupons for the organic products. The store collected data that revealed whether they purchased any of the organic products. For each row of customer data, we have: Information about the customer (gender, age, affluence grade, geographic characteristics). Information about their buying behavior. Loyalty card status and length of time the customer has had the card. Total purchase amounts over the last 3, 6, 9 and 12 months. The data source is made up of 1.6 million rows and 25 columns. NOTE: a complete data dictionary is available at the end of this document

5 GETTING STARTED IN SAS VISUAL STATISTICS THE ESSENTIALS The toolbar enables you to select your modeling technique. The data pane lists the available categories and measures in your selected data. The model pane enables you to access created models and displays results plots for the current model. The right pane contains tabs that help you change the properties of your models, filter the data and create and view comments. The summary table displays detailed statistics for the current model. For more information, please consult the SAS Visual Statistics User Guide, available under the Help menu.

6 CUSTOMER CLASSIFICATION EXAMPLE This example demonstrates modeling purchasing behavior with SAS Visual Statistics. It covers the following: Exploring data with visual analytics in preparation for model building. Predicting purchase of organic products using logistic regression. Predicting purchase of organic products using a decision tree. Comparing the two different models. Creating stratified logistic regression models from the results of the customer segmentation in the decision tree.

7 STARTING WITH EXPLORATION Your first step is to explore the data set to understand trends, relationships and/or identify data issues. The initial visualization depicts a bar chart with the frequency of various customer age groups by whether they purchased organic products.

8 STARTING WITH EXPLORATION STEP 1: Start with a blank canvas by minimizing the current visualization. Drag Age onto the workspace Notice age variable is skewed left with the majority of customers being older. This makes sense in the context of a supermarket store loyalty program.

9 STARTING WITH EXPLORATION STEP 2: Select Box Plot from the toolbar. STEP 3: Drag Organics Purchase onto the workspace. Note that the ages of those who bought organic products tend to be younger than those who did not. This variable may be important in our model.

10 MOVING FROM EXPLORING DATA TO BUILDING MODELS After exploring the data, the next step is to use SAS Visual Statistics to model whether a customer will purchase organic food. Right click anywhere in the box plot workspace, click Extended Features, then Model Responses in SAS Visual Statistics.

11 LOGISTIC REGRESSION GETTING STARTED SAS Visual Statistics creates a logistic regression model with the categorical variable Organics Purchase modeled as the response variable and the measure Age as a continuous effect. Notice a logistic regression model on nearly 1.6 million records was built very quickly.

12 LOGISTIC REGRESSION SELECTING THE EVENT LEVEL Logistic regression requires a discrete or categorical variable be assigned as the Response. Click Advanced under the Roles tab in the Right pane. The default behavior is to choose the first value alphabetically for the response variable (in this case, Bought). Click OK to exit the dialogue box.

13 LOGISTIC REGRESSION ADDING ADDITIONAL VARIABLES STEP 4: Using the Ctrl key to multi-select variables, drag Gender, Home Owner and Loyalty Card Class into Classification Effects box. Drag Affluence Grade, Loyalty Card Tenure and all four variables for Recent Purchases into the Continuous Effects box.

14 LOGISTIC REGRESSION FIT STATISTICS SAS Visual Statistics delivers a variety of fit statistics for model evaluation. Click on R-Square to see the list of available fit statistics.

15 LOGISTIC REGRESSION FIT SUMMARY The Fit Summary window plots the relative importance of each variable as measured by its p- value displayed on a log scale. Notice that Gender and Age are among the variables with p values less than This indicates they have a strong influence on the model, and therefore can predict whether a customer will buy organic products. You can always maximize the size of any individual window by clicking the maximize icon in the top right corner.

16 LOGISTIC REGRESSION REMOVING VARIABLES Often it makes sense to remove insignificant variables to predict the model target. In this case, the variable Recent 3 Month Purchase is not significant and can be removed. New results First, click on the dot at the end of the blue bar next to Recent 3 Month Purchase. Then right click the dot and select it to remove variable.

17 LOGISTIC REGRESSION ASSESSMENT PLOT WINDOW If you haven t done so already, minimize the Fit Summary window to return to all windows, as shown here. The bottom left pane displays a dropdown box with three different model assessment charts: Lift. ROC. Misclassification.

18 LOGISTIC REGRESSION ASSESSMENT PLOT WINDOW LIFT Lift is the ratio of the percentage of captured events within each percentile bin to the average percentage of responses for the model. Cumulative lift is calculated by using all the data up to and including the current percentile bin. In this example, the model has a cumulative lift of at the 20th percentile. This low value indicates additional variables or interaction effects should be considered to improve the model. This lift value means that if the supermarket chain sent coupons to the top 20 percent of customers selected by this model, it could expect to see 2.6x more customers purchasing organic products than if the same number of customers were randomly selected.

19 LOGISTIC REGRESSION ASSESSMENT PLOT WINDOW ROC Select ROC from the Assessment plot dropdown. A receiver operating characteristic (ROC) chart displays the ability of a model to avoid false positive and false negative classifications. A good ROC chart has a steep initial slope and levels off quickly.

20 LOGISTIC REGRESSION ASSESSMENT PLOT WINDOW MISCLASSIFICATION Select Misclassification from the Assessment plot dropdown. False Positives The misclassification plot displays how many observations were correctly and incorrectly classified as bought (positive) or did not buy (negative) organic products. Since more than half of the customers who bought organics were misclassified (false negatives), this model should be considered preliminary and additional variables and interactions should be considered. False Negatives

21 LOGISTIC REGRESSION SUMMARY TABLE Click Show Summary Table to display the detailed statistics for the model. Click Hide Summary Table to hide it. Take a moment to review each of the tabs. The next few pages will review select tabs of the Summary Table.

22 LOGISTIC REGRESSION SUMMARY TABLE PARAMETER ESTIMATES Click on the Parameter Estimates tab in the summary table. Click on the z Value column 2x to sort descending. Affluence Grade and Gender (F and M) have high influence in this model and are useful in predicting whether a customer will buy organic products.

23 LOGISTIC REGRESSION EXPORTING SCORE CODE Once a particular model has been built, you can easily export the score code to be used by other systems. Step 1: Select File > Export > Model Score Code from the menu. Step 2: Select the Logistic 1 model and click OK. Choose a location on your local file system to save the code file.

24 LOGISTIC REGRESSION EXPORTING SCORE CODE Score code snippet This score code can later be deployed on production data using a variety of methods, including: - LASR Analytic Server. - SAS Model Manager. - Base SAS. - SAS Data Integration Server.

25 DECISION TREE

26 DECISION TREES GETTING STARTED This section demonstrates building a decision tree to model whether a customer will buy organic products. A decision tree creates a hierarchical segmentation of the input customer data based on a series of rules applied to each customer record.

27 A COMMENT ON DECISION TREES Decision trees are available in both SAS Visual Analytics and SAS Visual Statistics. The decision trees in SAS Visual Analytics allow for users to segment their data to aid in exploratory analysis. SAS Visual Statistics adds an additional level of capabilities for the data scientist, allowing for interactive modeling and predictive modeling in addition to segmentation.

28 DECISION TREE GETTING STARTED STEP 1: Click on the New Decision Tree icon in the toolbar. STEP 2: Update the following parameters in the Properties tab: Increase the Leaf Size parameter to 1,000 so enough customers are in each leaf to be actionable. Decrease the Maximum Levels from six to four to evaluate actionable and manageable customer segments for this example. Uncheck Reuse Predictors to allow each variable to only be used once. This is to simplify this example. Uncheck Include Missing so the missing values are included in the model. This ensures consistency with the Logistic Regression when we compare their results in a later step.

29 DECISION TREE GETTING STARTED STEP 3: Back on the Roles tab, drag Organics Purchase into the Response field and all other variables (Categories and Measures) into the Predictors field.

30 DECISION TREE THE BASICS RESULTS PANE The Tree pane shows a graphical representation of the splits generated to divide up the customers in the decision tree. Treemap shows an overview of the splits of the tree. The color of each block indicates whether the majority of the customers in that block bought (blue) or did not buy (green) organics.

31 DECISION TREE TREE OVERVIEW STEP 4: Right click and select Show tree overview. The tree overview enables quick navigation of large decision trees. STEP 5: Using the tree overview pane, zoom to the top of the tree. Note the large split on the variable Age. As seen in our earlier exploratory analysis, organic product purchasers were younger than those who did not buy organic products. This tree split confirms that finding.

32 DECISION TREE INTERPRETING LEAF STATISTICS Hover your pointer over the grey area of a leaf to reveal the leaf statistics. The leaf statistics for node 0 (root node) show an overview of all the data sets where only percent of customers bought organic products. After the first split Age, the leaf statistics for node 2 show that for customers younger than 42.4 years, the likelihood of organic product purchase increased to percent.

33 DECISION TREE REVIEWING RESULTS The next split is on Affluence Grade. Customers with higher Affluence Grades (>=11.9) are more likely to buy organic products. Female customers with lower Affluence Grades are more likely to buy organics than men or customers whose genders are unknown.

34 DECISION TREE DIAGNOSTIC PLOTS Right click and select show diagnostic plots. Note the addition of two different panes: Leaf Statistics plot and Assessment plots (Lift, ROC and Misclassification).

35 DECISION TREE LEAF STATISTICS Leaf statistics shows the distribution of customers across the terminal leaves. This chart can display the results by customer count or percentage (Look for the leaf statistics parameter in the right pane under the Parameters tab). Count Percent

36 DECISION TREE SPLITTING The supermarket s marketing department wants to use customer loyalty card classes (gold, silver, etc.) in its marketing efforts. Although this variable isn t included in the original model, it can be added by splitting a node. Notice that leaf 6 has a nearly equal distribution of customers who bought or did not buy organics. This is a good candidate for splitting. STEP 1: Right click node 6 and select Split. STEP 2: Select Loyalty Card Class and click OK.

37 DECISION TREE SPLITTING RESULTS This decision tree shows a new split from node 6 with loyalty card class. Marketing can now see that customers younger than 42.4, with an Affluence Grade < 10.2, who are female and hold silver and tin loyalty card classes are more likely to buy organic products than the same group of customers with the gold or platinum class.

38 MODEL COMPARISON

39 MODEL COMPARISON THE BASICS The model comparison tool enables you to compare the performance of competing models using various criteria. The criteria depend on the models and response variables used in your analysis. A model comparison requires you to build at least one other model before performing a comparison.

40 MODEL COMPARISON Step 1: Click on the Model Comparison button in the toolbar. Step 2: Select Bought in the Level dropdown box. Step 3: Move both models from the Available Models pane to the Selected Models pane. Click OK.

41 MODEL COMPARISON RESULTS The Logistic Regression model is selected as the champion model as shown in the table at the bottom of the screen. Logistic 1 has a lower misclassification rate (18.5%) than the Decision Tree (21.0%). In the Properties tab, a variety of fit statistics are available to the modeler for comparison purposes.

42 MODEL COMPARISON ASSESSMENT PLOTS MISCLASSIFICATION Select Misclassification from the dropdown on the Assessment plot. Note that the Decision Tree has more total misclassifications (green bar segments). Specifically, the Logistic model has fewer false negatives than the Tree; those are customers who bought organic products even though they were predicted not to.

43 MODEL COMPARISON ASSESSMENT PLOTS ROC Select ROC from the Assessment plot dropdown. The ROC plot reaffirms the selection of the logistic regression model as the champion model. The logistic model performs better in this assessment plot as the red line is higher and steeper than the yellow line (which represents the decision tree model).

44 CREATING MULTIPLE MODELS BY CUSTOMER SEGMENT

45 STRATIFIED MODELS THE BASICS In this section, we ll take the customer segments generated by the decision tree and use the group by functionality to make a separate logistic regression model for each different customer segment.

46 STRATIFIED MODELS GENERATING CUSTOMER SEGMENTS BY LEAF The customer segments developed using the decision tree can now be used to make separate models for each customer segment. Right click in the Decision Tree window and select Derive Leaf ID Variable. Notice a new field called Leaf ID 1. This variable has five different values, one for each terminal leaf.

47 STRATIFIED MODELS MODEL DUPLICATION To preserve the model already built, create a duplicate model of the original Logistic Regression. We will use this new copy to create a separate model for each customer segment (terminal leaf) identified in the decision tree. Click the arrow next to the Logistic 1 model and select Duplicate. Under the Properties tab, change the model name to Logistic with Group by Leaf. Note: This step is optional. The following steps can also be done with your current logistic regression model.

48 STRATIFIED MODELS CREATING MODELS FOR EACH CUSTOMER SEGMENT On the Roles tab, drag Leaf ID 1 into the Group By field.

49 STRATIFIED MODELS MODEL REVIEW Now, separate models were generated for each customer segment created in the decision tree. There is a model for missing values since we checked the box include missing on the Decision Tree.

50 STRATIFIED MODELS VARIABLE ANALYSIS Select Gender from the Variable Importance dropdown. Notice that it is significant for only three of the models. The variable Age is significant for the models covering all customer segments.

51 What other insights can you find in the data? To view video demonstrations of SAS Visual Statistics, visit the Demos and Screenshots section of sas.com/en_us/software/analytics/visual-statistics.html

52 DATA DICTIONARY

53 ORGANICS DATA DICTIONARY CATEGORICAL VARIABLES Field Type Description Age Group Category Age group classification (young, middle, senior, unknown) Bought Beverages Category Flag indicating customer buys beverages Bought Bakery Category Flag indicating customer buys baked goods Bought Canned Category Flag indicating customer buys canned goods Bought Dairy Category Flag indicating customer buys dairy products Bought Baking Category Flag indicating customer buys baking supplies Bought Frozen Category Flag indicating customer buys frozen items Bought Meat Category Flag indicating customer buys meat Bought Fruits Category Flag indicating customer buys fruits Bought Vegetables Category Flag indicating customer buys vegetables Gender Category Customer gender Home Owner Category Flag indicating home ownership Loyalty Card Class Category Classification of customer by use of their loyalty program. The more the customer spends at the supermarket, the higher his class (tin, silver, gold, platinum) Organics Purchase Category Flag indicating customer buys organic products

54 ORGANICS DATA DICTIONARY - MEASURES Field Type Description Affluence Grade Measure Classification of the affluence of the customer Age Measure Customer age Loyalty Card Tenure Measure Length of time customer has had loyalty card Recent 3 Month Purchase Measure Customer s total purchase amount over the last three months Recent 6 Month Purchase Measure Total purchase amount over the last six months Recent 9 Month Purchase Measure Total purchase amount over the last nine months Recent 12 Month Purchase Measure Total purchase amount over the last 12 months