Data607 Presentation
Sang Yoon (Andy) Hwang
February 15, 2018
DATA 607 Ch.3 Data Science for Business - Decision Tree
url <- "https://raw.githubusercontent.com/wheremagichappens/an.dy/master/DATA607/fitnessAppLog.csv"
fitness <- read.table(url, sep=",", header=T)
library("rpart")
treeAnalysis <- rpart(PayOrNot~Incomes+GymVisits+State,data=fitness)
library("rpart.plot")## Warning: package 'rpart.plot' was built under R version 3.4.3fitness## Incomes GymVisits State Hours PayOrNot
## 1 100 4 TX 9.3 P
## 2 50 3 CA 4.8 NP
## 3 100 4 TX 8.9 P
## 4 100 2 NY 6.5 P
## 5 50 2 MD 4.2 NP
## 6 80 2 CA 6.2 NP
## 7 75 3 WA 7.4 NP
## 8 65 4 SD 6.0 NP
## 9 90 3 ND 7.6 P
## 10 90 2 TX 6.1 NP
## 11 100 1 TX 9.3 P
## 12 50 3 CA 4.8 NP
## 13 100 4 TX 8.9 NP
## 14 100 2 NY 6.5 NP
## 15 50 2 MD 4.2 NP
## 16 80 0 CA 6.2 NP
## 17 75 3 WA 7.4 NP
## 18 65 4 SD 6.0 NP
## 19 90 3 ND 7.6 NP
## 20 90 2 TX 6.1 NP
## 21 100 4 TX 9.3 P
## 22 50 0 CA 4.8 NP
## 23 100 4 TX 8.9 NP
## 24 100 2 NY 6.5 NP
## 25 50 2 MD 4.2 NP
## 26 80 2 CA 6.2 NP
## 27 75 3 WA 7.4 NP
## 28 65 4 SD 6.0 NP
## 29 90 3 ND 7.6 NP
## 30 90 2 TX 6.1 NP
## 31 100 4 TX 9.3 P
## 32 50 3 CA 4.8 NP
## 33 100 4 TX 8.9 P
## 34 100 2 NY 6.5 NP
## 35 50 2 MD 4.2 NP
## 36 80 2 CA 6.2 P
## 37 75 3 WA 7.4 NP
## 38 65 4 SD 6.0 NP
## 39 90 3 ND 7.6 NP
## 40 90 2 TX 6.1 NP
## 41 100 4 TX 9.3 P
## 42 50 3 CA 4.8 NP
## 43 100 4 TX 8.9 P
## 44 100 2 NY 6.5 NP
## 45 50 2 MD 4.2 P
## 46 80 7 CA 6.2 P
## 47 75 3 WA 7.4 NP
## 48 65 4 SD 6.0 NP
## 49 90 3 ND 7.6 NP
## 50 90 2 TX 6.1 P
## 51 100 4 TX 9.3 P
## 52 50 3 CA 4.8 NP
## 53 100 4 TX 8.9 P
## 54 100 2 NY 6.5 NP
## 55 50 2 MD 4.2 NP
## 56 80 2 CA 6.2 NP
## 57 75 3 WA 7.4 NP
## 58 65 4 SD 6.0 NP
## 59 90 3 ND 7.6 NP
## 60 90 2 TX 6.1 NP
## 61 100 6 TX 9.3 P
## 62 50 3 CA 4.8 NP
## 63 100 7 TX 8.9 P
## 64 100 2 NY 6.5 NP
## 65 50 2 MD 4.2 NP
## 66 80 2 CA 6.2 NP
## 67 75 3 WA 7.4 NP
## 68 65 4 SD 6.0 NP
## 69 90 3 ND 7.6 NP
## 70 90 2 TX 6.1 NP
## 71 100 6 TX 9.3 P
## 72 50 3 CA 4.8 NP
## 73 100 4 TX 8.9 P
## 74 100 2 NY 6.5 NP
## 75 50 2 MD 4.2 NP
## 76 80 2 CA 6.2 NP
## 77 75 3 WA 7.4 NP
## 78 65 4 SD 6.0 NP
## 79 90 3 ND 7.6 NP
## 80 90 2 TX 6.1 NP
## 81 100 4 TX 9.3 P
## 82 50 3 CA 4.8 NP
## 83 100 4 TX 8.9 P
## 84 40 6 NY 6.5 NP
## 85 50 2 MD 4.2 NP
## 86 80 2 CA 6.2 NP
## 87 75 3 WA 7.4 P
## 88 65 4 SD 6.0 NP
## 89 90 3 ND 7.6 P
## 90 90 2 TX 6.1 NP
## 91 100 4 TX 9.3 P
## 92 50 3 CA 4.8 NP
## 93 100 4 TX 8.9 P
## 94 100 2 NY 6.5 NP
## 95 50 2 MD 4.2 NP
## 96 80 2 CA 6.2 NP
## 97 75 3 WA 7.4 P
## 98 65 4 SD 6.0 NP
## 99 90 3 ND 7.6 P
## 100 90 2 TX 6.1 NPrpart.plot(treeAnalysis,extra=4)