assignment 6

Assignment6.Rmd

@@ -25,19 +25,18 @@ library(rpart)
 #Upload the data sets MOOC1.csv and MOOC2.csv
 M1 <- read.csv("MOOC1.csv", header = TRUE)
 
-M2 <- 
-
+M2 <- read.csv("MOOC2.csv", header = TRUE)
 ```
 
 #Decision tree
 ```{r}
 #Using the rpart package generate a classification tree predicting certified from the other variables in the M1 data frame. Which variables should you use?
 
-c.tree1 <- 
+c.tree1 <- rpart(certified ~ grade + assignment, method = "class", data = M1)
 
 #Check the results from the classifcation tree using the printcp() command
 
-
+printcp(c.tree1)
 
 #Plot your tree
 
@@ -52,10 +51,12 @@ post(c.tree1, file = "tree1.ps", title = "MOOC") #This creates a pdf image of th
 #If we are worried about overfitting we can remove nodes form our tree using the prune() command, setting cp to the CP value from the table that corresponds to the number of nodes we want the tree to terminate at. Let's set it to two nodes.
 
 ```{r}
-c.tree2 <- prune(c.tree1, cp = )#Set cp to the level at which you want the tree to end
+c.tree2 <- prune(c.tree1, cp = 0.058182)#Set cp to the level at which you want the tree to end
 
 #Visualize this tree and compare it to the one you generated earlier
 
+printcp(c.tree2)
+
 post(c.tree2, file = "tree2.ps", title = "MOOC") #This creates a pdf image of the tree
 ```
 
@@ -72,12 +73,55 @@ table(M2$certified, M2$predict2)
 
 ```
 
+I feel like the 2nd tree is better, because despite the 1st tree's accuracy in prediciting those who are not certified, it made a lot of false positives, which is an issue. 
+
 ##Part III
 
 Choose a data file from the (University of Michigan Open Data Set)[https://github.com/bkoester/PLA/tree/master/data]. Choose an outcome variable that you would like to predict. Build two models that predict that outcome from the other variables. The first model should use raw variables, the second should feature select or feature extract variables from the data. Which model is better according to the cross validation metrics?
 
 ```{r}
+library(dplyr)
+library(tidyr)
+library(stringr)
+library(rpart)
+
+M3 <- read.csv("student.record.csv", header = TRUE)
+names(M3) <- c("MAJOR_3", "MAJOR_2", "MAJOR_1", "HSGPA", "ACT_ENGL", "ACT_MATH", "ACT_READ", "ACT_SCIRE", "ACT_COMP", "SAT_VERB", "SAT_MATH", "SAT_COMP", "SEX", "STDNT_GROUP1", "STDNT_GROUP2", "MAJOR1_DEPT", "MAJOR2_DEPT", "MAJOR3_DEPT", "ANONID", "ADMIT_TERM", "MAJOR1_TERM", "MAJOR2_TERM", "MAJOR3_TERM")
+
+#find students with BA in DEGREE_1 title & label accordingly
+majors2 <- select(M3, 3:13, 16, 19)
+
+majors_ART <- majors2
+
+majors_ART$MAJOR_1 <- as.character(majors_ART$MAJOR_1)
+
+majors_ART <- majors_ART %>% 
+  select(MAJOR_1:SEX, ANONID) %>% 
+  filter_all(any_vars(str_detect(MAJOR_1, pattern = "BA")))
+
+majors_ART$DEG_TYPE <- "art"
+
+#find students with BS in DEGREE_1 title & label accordingly
+majors_SCIENCE <- majors2
+majors_SCIENCE$MAJOR_1 <- as.character(majors_SCIENCE$MAJOR_1)
+
+majors_SCIENCE <- majors_SCIENCE %>% 
+  select(MAJOR_1:SEX, ANONID) %>% 
+  filter_all(any_vars(str_detect(MAJOR_1, pattern = "BS")))            
+
+majors_SCIENCE$DEG_TYPE <- "science"
+
+#Bind BA and BS students together
+majors_ALL <- bind_rows(majors_ART, majors_SCIENCE)
+majors_ALL_math <- majors_ALL %>% 
+  select(ANONID, MAJOR_1, DEG_TYPE, ACT_MATH, SAT_MATH, HSGPA)
+
+#Do math scores predict BA or BS? spoiler alert, yes. 
+c.tree4 <- rpart(as.factor(DEG_TYPE) ~ ACT_MATH + SAT_MATH, method = "class", data = majors_ALL_math)
+
+printcp(c.tree4)
 
+post(c.tree4, file = "tree4.ps", title = "degree type") #This creates a pdf image of the tree
 ```