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Predictive Analytics for Everyone! Building CART Models using R - Chantal D. Larose, Ph.D.
- 1. Predictive Analytics forEveryone! Building CART Models using R Chantal Larose Assistant Professor of Decision Science (Statistics) School of Business, SUNY New Paltz DASH Lab Workshop March 8 2017
- 2. Why Predictive Analytics? Sports,healthcare, customer service – the world is full of data! Fun to pull stories out of a mess of numbers Examples: A Logistic Regression Approach to Predicting Who Will Make the NBA Playoffs 1 Data Mining Major League Baseballs Pace of Play Problem 2 More sports applications at: New England Symposium on Statistics in Sports Saturday, September 23, 2017 1 Ryan Elmore, Department of Business Information and Analytics, Daniels School of Business, University of Denver 2 Aaron Crowley, Zhuolin He, and Rachael Hageman Blair. Department of Biostatistics, State University of New York at Buffalo [email protected] Predictive Analytics for Everyone! Building CART Models using R 1
- 3. Why R? Open source,free to download Active and helpful community Appeals to non-programmers: Different user interfaces (e.g. RStudio) allow for point-and-click interface for some tasks Appeals to programmers: Customizable – program your own functions, etc. [email protected] Predictive Analytics for Everyone! Building CART Models using R 2
- 4. Set-up for theWorkshop Open up RStudio on your laptop (Apps → Other → RStudio) Go to the Workshop’s website: hawksites.newpaltz.edu/dashlab/predictive-analytics-for-everyone/ Download the Churn data set (.csv file) Download the Adult data set (.csv file) Download the Do It Yourself! guide (.R file) The analyses in this workshop are covered in more detail in Data Mining and Predictive Analytics, Second Edition. Larose & Larose, Wiley, 2015. [email protected] Predictive Analytics for Everyone! Building CART Models using R 3
- 5. Getting Acquainted withR Open the Do It Yourself! R file. [email protected] Predictive Analytics for Everyone! Building CART Models using R 4
- 6. Getting Acquainted withR Input the data set: [email protected] Predictive Analytics for Everyone! Building CART Models using R 5
- 7. Getting Acquainted withR Let’s look at some code: [email protected] Predictive Analytics for Everyone! Building CART Models using R 6
- 8. Getting Acquainted withR How do we tell R to run the code? 1. Highlight the code and press the Run button 2. Put your cursor on the line you want to run and press Control+Enter (no need to highlight code) [email protected] Predictive Analytics for Everyone! Building CART Models using R 7
- 9. Activity 1: CARTModels We want to predict the value of one variable, using other variables For our first example: We want to predict the value of Churn, i.e. whether or not a customer leaves our company. We will predict Churn using variables such as: Day Mins: How many minutes during the day a customer uses their phone3 CustServ Calls: How many times a customer has called customer service VMail Plan: Whether or not a customer has the voicemail plan 3 Data is from when day and evening charges were different [email protected] Predictive Analytics for Everyone! Building CART Models using R 8
- 10. Activity 1: CARTModels Wait – What about regression? The data may be too messy to meet the normality requirements, even with transformations Regression interpretations get very complex very fast (especially with transformations) Data set is too large! At some point, you have so many records that the F and t tests from regression will come back significant, no matter what the reality of the situation is CART models generate easy-to-understand “decision rules” (IF this, THEN that) that make intuitive sense [email protected] Predictive Analytics for Everyone! Building CART Models using R 9
- 11. Activity 1: CARTModels - Setup [email protected] Predictive Analytics for Everyone! Building CART Models using R 10
- 12. Activity 1: CARTModels - Setup [email protected] Predictive Analytics for Everyone! Building CART Models using R 11
- 13. Activity 1: CARTModels CART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / Total Day Mins < 264 CustServ Calls < 3.5 Int'l Plan = no Day Mins < 223 Eve Mins < 260 VMail Plan = yes Intl Calls >= 2.5 Intl Mins < 13 Day Mins >= 160 Eve Mins >= 142 Day Mins >= 176 Eve Mins >= 212 VMail Plan = yes Eve Mins < 188 Day Mins < 278 Eve Mins < 144 >= 264 >= 3.5 yes >= 223 >= 260 no < 2.5 >= 13 < 160 < 142 < 176 < 212 no >= 188 >= 278 >= 144 False. 2850 / 3333 False. 2766 / 3122 False. 2642 / 2871 False. 2476 / 2604 False. 2161 / 2221 False. 315 / 383 False. 298 / 332 True. 34 / 51 False. 11 / 11 True. 34 / 40 False. 166 / 267 False. 166 / 216 False. 166 / 173 True. 43 / 43 True. 51 / 51 True. 127 / 251 False. 111 / 149 False. 106 / 130 False. 86 / 96 False. 20 / 34 False. 18 / 18 True. 14 / 16 True. 14 / 19 True. 89 / 102 True. 127 / 211 False. 47 / 53 True. 121 / 158 False. 32 / 57 False. 21 / 25 True. 21 / 32 False. 7 / 8 True. 20 / 24 True. 96 / 101 [email protected] Predictive Analytics for Everyone! Building CART Models using R 12
- 14. Activity 1: CARTModels CART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / TotalCART Model to Predict Churn. # Correct / Total Day Mins < 264 CustServ Calls < 3.5 Day Mins >= 160 VMail Plan = yes >= 264 >= 3.5 < 160 no False. 2850 / 3333 False. 2766 / 3122 False. 2642 / 2871 True. 127 / 251 False. 111 / 149 True. 89 / 102 True. 127 / 211 False. 47 / 53 True. 121 / 158 [email protected] Predictive Analytics for Everyone! Building CART Models using R 13
- 15. Activity 1: CARTModels [email protected] Predictive Analytics for Everyone! Building CART Models using R 14
- 16. Activity 2: OnYour Own! After you complete the Churn example, go to Line 75 to begin Example 2. All the code you need is there. Follow the directions and run the code. Task: After building the CART model, use the model to find at least two decision rules. State the confidence level of each one. [email protected] Predictive Analytics for Everyone! Building CART Models using R 15