This document describes exercises completed in SAS Visual Analytics to analyze a bio organics dataset containing over 111,000 observations and 13 variables. The exercises included exploring missing data, imputing missing values, analyzing distributions, identifying relationships between variables, and building and comparing predictive models. Cluster analysis identified clusters with highest affluence, organic purchases, and total spend. Logistic regression and decision tree models were built and compared on metrics like false positive rate, Kolmogorov-Smirnov statistic, and lift for targeting customers for an organic products campaign.

1. Using Taradata SAS Visual Analytics to Analyze Bio Organics Data
Exercise 1:
1) How many observations are there in the BIGORGANICS dataset?
● 111,115 Observations
2) How many variables (or columns) are there? Which variables are Measures? And
Categories?
● There are 13 Variables
● The following Variables are Measures: Affluence Grade, Age, Loyalty Card
Tenure, Organics Purchase Count, Organics Purchase Indicator, Total Spend
● The following Variables are Categories: Customer Loyalty ID, Gender,
Geographic Region, Loyalty Status, Neighborhood Cluster- 55 Level,
Neighborhood Cluster- 7 Level, Television Region
3) Quickly, verify the distributions and the main statistics of each measurable variable.
What are their average values? Which variables present missing values?
●
● Variables presenting missing values are: Television Region, Gender, Geographic
Region, Neighborhood Cluster-55 Level and Neighborhood Cluster-7 Level
4) Using the visualizations, you already have created or by creating new ones, which
categorical variables present missing values?

2. Exercise 2:
Do the imputation for the other categorical variables:
1. Geographic Region substitute the missing values by “South East”

3. 2. Television Region substitute the missing values by “London”
3. Neighborhood Cluster-55 Level - substitute the missing values by “U”
4. Neighborhood Cluster-7 Level -substitute the missing values by “U”

4. Exercise 3:
Do the imputation for the other interval variables (verify exercise 3 on page 9) and check their
new distributions.
1) Age – substitute the missing values by 53:
2) Loyalty Card Tenure – substitute the missing values by 0

5. Exercise 4:
1) Create a Box Plot that displays the Affluence Grade and Age by Organics Purchase
Indicator. Show the averages (include a screenshot of the output as Answer)
2) What conclusions can you make about those who purchase organic products?
● Those who purchase organic products have a higher Affluence Grade than those who
don’t.
Exercise 5:

6. 1) Which cluster appears to have the youngest customers?
It appears that cluster id # 1 have the youngest customer with age 18.
2) Which cluster appears to have the customers with highest Loyalty Card Tenure?
Cluster 4 has the highest Loyalty Card Tenure.
3) Which cluster appears to have the customers with highest Affluence?
Cluster # 2 has the highest Affluent
4) Which cluster appears to have the customers with the highest Organics Purchase?
Cluster # 3, and 2 have the highest Organic purchase.
5) Which cluster appears to have the customers with the highest Total Spend?
Cluster ID 1 has the highest total spent.

7. 6) Which cluster appears to have more customers with Loyalty Status: Gold?
Exercise 6:
1) Which variables are actually been using by the Tree?
It is using Age, Gender, and Affluence variables.

8. 2) Which variable is the first split? On which Value?
Age
3) How many leaves to we have? Which is the purest leaf?
We have had 10 but we changed it to 20 leafs
Exercise 7:

9. 1) What is the cumulative lift for the model at the 5th
percentile? And on the 20th
percentile?
At the 5th percentile the lift is at 3.5 and at the 20th percentile it is 2.5
2) View the ROC chart. What is the KS Statistic?
KS Statistics as follows : .4511
3) View the Misclassification Chart. How many false negatives are there?
4) Display the details table. Which node provides the highest gain?
North provides the highest gain.

10. 5) What are the splitting rules applied to this node?
It is using the following variables as nodes:
Exercise 8:
1) Which variables were not considered important to the model? What would be an underlying
cause? From the graph below we could tell it did not accept variables Geo_IMP and NC-7
Level_IMP. There might be not significant data to change the value.
2) What is the R-Square for the model? What is the number of Observations used?

11. R-sqaure is .2239 and Observations are 111,115
3) What is the cumulative lift for the model at the 5th
percentile? And on the 20th
percentile?
At the 5th percentile the lift is at 3.5 and at the 20th percentile it is 2.5
4) View the ROC Chart. What is the KS Statistic?
KS Statistics as follows : .4511
ROC CHART:
5) View the Misclassification Chart. How many false negatives are there?

12. 6) Select some data points with residuals greater than 25, right-click in the Residual Plot, and
select Exclude Selected. What ist he number of Observations used now? What is the R-Square
now? How many false negatives are there now?
After doing that the new R-Sqaure value is .2161 and Observations changed to 104,545 please
see the image below:
Exercise 9:
1) On which Age group we got the highest R-Square? And which group has more
observations?

13. 2) Is the Total Spend variable relevant for all the models? For which models is it relevant?
3) How any false negatives are there for Young group? And for Middle?Ffalse negative rate is
higher for younger group versus middle group.

14. Exercise 10:
1) Which model was selected by default?
Decision Tree
2) Which model provides the best FPR? And FDR?
FPR= Logistic regression, and FDR is the Decision Tree

16. 3) Which model provides the best KS Statistic? Kolmogorov-Smirnoff statistic is a goodness of
fit metric that represents the maximum separation between the model ROC curve and the
baseline.

17. 4) Suppose we are going to have a new campaign promoting the Organics products and the
budget for the campaign allows us to have only 14,000 customer contacts (around 15% of the
customer base). So, we want to apply this model to select a target population for this campaign,
composed by the 15,000 customers with the greatest propensity to buy Organic products.
Which model would be selected?

18. 5) Suppose the same campaign scenario as the previous, but with 20,000 customers (around
20% of the customer base). Which model would be selected?
6) TIP: Cumulative % of events – cumulative number of events observed up to and including
the specified percentile bin divided by the total number of observations, sorted in descending
order of the predicted event probabilities.