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- 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 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
- 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

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