Machine learning using R and Tableau

1. Machine Learning with R
and Tableau
Tableau User Group (TUG)
Greg Armstrong
Blast Analytics & Marketing
garmstrong@blastam.com

2. TUG | Machine Learning with R and Tableau
Agenda
Machine Learning with R and Tableau
2
1. What is Machine Learning?
2. What is R?
3. Live Examples using Tableau and R

3. TUG | Machine Learning with R and Tableau
Machine Learning
What is machine learning?
3
Machine learning explores
the study and construction
of algorithms that can
learn from and make
predictions on data.
• Classification
• Regression
• Segmentation
Common Methods

4. TUG | Machine Learning with R and Tableau 4
Regression
Machine Learning
Supervised Learning
Classification
X
Y
X
Y

5. TUG | Machine Learning with R and Tableau 5
Segmentation (cluster)
Machine Learning
Unsupervised Learning
X
Y

6. TUG | Machine Learning with R and Tableau
Machine Learning
Marketing use cases
6
• Predicting Lifetime Value (LTV)
• Predicting Churn
• Customer segmentation
• Product recommendations
I like it. I like it a lot!

7. TUG | Machine Learning with R and Tableau
Machine Learning
Finance use cases
7
• Predicting credit risk
• Treasury or currency risk
• Fraud detection
• Accounts Payable Recovery
“Because a large font makes profits look bigger.”

8. TUG | Machine Learning with R and Tableau
Machine Learning
Human Resources use cases
8
• Resume screening
• Employee churn
• Training recommendation
• Talent management
“I pruned a tree once, so technically I’m allowed
to put ‘branch manager’ on my resume”

9. TUG | Machine Learning with R and Tableau
Machine Learning
Web Search
9
… and predictive text
algorithms to fill in the most
common keyword search
terms.
Google uses machine
learning algorithms to serve
up the correct search even
when the search terms are
vastly misspelled.

10. TUG | Machine Learning with R and Tableau
Machine Learning
Social Networks
10

11. TUG | Machine Learning with R and Tableau
Machine Learning
Spam Filtering
11
No Spam

12. TUG | Machine Learning with R and Tableau
Machine Learning
Research - Fishers Iris
12
Based on Ronald Fisher’s 1936 paper
the idea was to perform statistical
classification on the Iris flower
data set.
Petal widthPetal length
SepalwidthSepallength

13. TUG | Machine Learning with R and Tableau
ahhRRRR!
What is R?
13
• Data manipulation
• Statistical modeling
• Visualization tool
• Open Source
R is a language for statistical analysis and
data visualization.

14. TUG | Machine Learning with R and Tableau
R Studio, R & Tableau
A brief introduction
14
+

15. TUG | Machine Learning with R and Tableau
Tableau + R
What did we discover?
15
Customer Segmentation (clusters)
1. There are some big spenders in the Red group,
who may not have purchased in a while.
2. Our most profitable customers seem to be older
with higher incomes. (Blue group)
Forecasting (linear regression)
1. Tableau forecasting is very good.
2. More flexibility with R forecasting.

16. TUG | Machine Learning with R and Tableau
Tableau User Group (TUG)
Machine Learning with R and Tableau
16
Questions?
Thank you!

17. Phone (888) 252-7866 Email sales@blastam.comWeb www.blastam.com
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Rocklin, CA 95765
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18. TUG | Machine Learning with R and Tableau
Calculated Fields
Tableau Calculated Fields for R
18
SCRIPT_INT("
## Sets the seed
set.seed( .arg7[1])
## Studentizes the variables
day <- ( .arg1 - mean(.arg1) )/ sd(.arg1)
mos <- ( .arg2 - mean(.arg2) )/ sd(.arg2)
dis <- ( .arg3 - mean(.arg3) )/ sd(.arg3)
inc <- ( .arg4 - mean(.arg4) )/ sd(.arg4)
age <- ( .arg5 - mean(.arg5) )/ sd(.arg5)
dat <- cbind(day, mos, dis, inc, age)
day <- .arg6[1]
## Creates the clusters
kmeans(dat, day)$cluster
",
MIN([Days Since Last Order]),
[Months as Customer],
AVG([Discount]),
MAX([Income]),
MAX([Age]),
[clusters],
[seed]
)
K-means cluster for customer segmentation
SCRIPT_STR('hello <- "Hello TUG!"', ATTR([R
Result]))
Pass string to R with a parameter
SCRIPT_INT("as.integer(.arg1 * 2)", [R Variable])
Pass calculation to R based on parameter
SCRIPT_BOOL("
print('******************************************
*********************')
print('the vector sent was')
print(.arg1)
print('with length')
print(length(.arg1))
TRUE
",
SUM([Sales])
)
Print to console R arguments