machine learning with r and tableau
TRANSCRIPT
Machine Learning with R and Tableau
Tableau User Group (TUG)
Greg Armstrong Blast Analytics & Marketing [email protected]
TUG | Machine Learning with R and Tableau
AgendaMachine Learning with R and Tableau
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1. What is Machine Learning? 2. What is R? 3. Live Examples using Tableau and R
TUG | Machine Learning with R and Tableau
Machine LearningWhat is machine learning?
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Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
• Classification • Regression • Segmentation
Common Methods
TUG | Machine Learning with R and Tableau 4
Regression
Machine LearningSupervised Learning
Classification
X
Y
X
Y
TUG | Machine Learning with R and Tableau 5
Segmentation (cluster)
Machine LearningUnsupervised Learning
X
Y
TUG | Machine Learning with R and Tableau
Machine LearningMarketing use cases
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• Predicting Lifetime Value (LTV)• Predicting Churn• Customer segmentation• Product recommendations
I like it. I like it a lot!
TUG | Machine Learning with R and Tableau
Machine LearningFinance use cases
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• Predicting credit risk • Treasury or currency risk • Fraud detection • Accounts Payable Recovery
“Because a large font makes profits look bigger.”
TUG | Machine Learning with R and Tableau
Machine LearningHuman Resources use cases
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• 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”
TUG | Machine Learning with R and Tableau
Machine LearningWeb Search
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… 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.
TUG | Machine Learning with R and Tableau
Machine LearningSpam Filtering
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No Spam
TUG | Machine Learning with R and Tableau
Machine LearningResearch - Fishers Iris
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Based on Ronald Fisher’s 1936 paper the idea was to perform statistical classification on the Iris flower data set.
Petal widthPetal length
Sep
al w
idth
Sep
al le
ngth
TUG | Machine Learning with R and Tableau
ahhRRRR!What is R?
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• Data manipulation• Statistical modeling• Visualization tool• Open Source
R is a language for statistical analysis and data visualization.
TUG | Machine Learning with R and Tableau
R Studio, R & TableauA brief introduction
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TUG | Machine Learning with R and Tableau
Tableau + RWhat did we discover?
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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.
TUG | Machine Learning with R and Tableau
Tableau User Group (TUG)Machine Learning with R and Tableau
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Questions?
Thank you!
Phone (888) 252-7866 Email [email protected] www.blastam.com
Roseville Office 6020 West Oaks Blvd, Suite 260
Rocklin, CA 95765
San Francisco Office 625 Second Street, Suite 280
San Francisco, CA 94107
New York Office 261 Madison Ave, 9th Floor
New York, NY 10016
Seattle Office 500 Yale Avenue North
Seattle, WA 98109
Los Angeles Office 7083 Hollywood Boulevard
Los Angeles, CA 90028
TUG | Machine Learning with R and Tableau
Calculated FieldsTableau Calculated Fields for R
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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