Assignment 5 - Hsiao Ting Yang

Assignment 5.Rmd

@@ -1,22 +1,21 @@
 ---
 title: "Assignment 5 - Decision Trees"
-author: "Charles Lang"
+author: "Hsiao Ting Yang"
 date: "November 9, 2016"
 output: html_document
 ---
 For this assignment we will be using data from the Assistments Intelligent Tutoring system. This system gives students hints based on how they perform on math problems. 
 
 #Install & call libraries
 ```{r}
-install.packages("party", "rpart")
 
 library(rpart)
 library(party)
 ```
 
 ## Part I
 ```{r}
-D1 <- 
+D1 <- read.csv("intelligent_tutor.csv", header = TRUE)
 ```
 
 ##Classification Tree
@@ -40,42 +39,68 @@ We want to see if we can build a decision tree to help teachers decide which stu
 
 #Visualize our outcome variable "score"
 ```{r}
-
+hist(D1$score)
 ```
 
 #Create a categorical outcome variable based on student score to advise the teacher using an "ifelse" statement
 ```{r}
-D1$advice <- 
+
+D1$advice <- ifelse(D1$score <= 0.4, "intervene", ifelse(D1$score >0.4 & D1$score <=0.8, "monitor", "no action"))
+
 ```
 
 #Build a decision tree that predicts "advice" based on how many problems students have answered before, the percentage of those problems they got correct and how many hints they required
 ```{r}
-score_ctree <- 
+
+score_ctree <- ctree(as.factor(D1$advice) ~ prior_prob_count + prior_percent_correct + hints, data = D1)
+
 ```
 
 #Plot tree
 ```{r}
 
+plot(score_ctree)
+
 ```
 
 Please interpret the tree, which two behaviors do you think the teacher should most closely pay attemtion to?
 
+##Comment
+I think the teacher should focus on node 7 and node 9 since those nodes have the higher percentage intervention based on the categorical outcome I setted. For node 7, these group of students have asked for less than 12 hints and answered less than 62.9% correctly. For node 9, these group of students have asked for more than 12 hints. These two behaviors should be what teachers should closely pay attention to since they've asked the most hints and answered the least amount of questions correctly.
+
 #Test Tree
 Upload the data "intelligent_tutor_new.csv". This is a data set of a differnt sample of students doing the same problems in the same system. We can use the tree we built for the previous data set to try to predict the "advice" we should give the teacher about these new students. 
 
 ```{r}
 #Upload new data
 
-D2 <- 
+D2 <- read.csv("intelligent_tutor_new.csv", header = TRUE)
 
 #Generate predicted advice using the predict() command for new students based on tree generated from old students
 
-D2$prediction <- 
+D2$prediction <- predict(score_ctree, D2)
 
 ``` 
 ## Part III
 Compare the predicted advice with the actual advice that these students recieved. What is the difference between the observed and predicted results?
 
+```{r}
+
+D2$advice <- ifelse(D2$score <= 0.4, "intervene", ifelse(D2$score >0.4 & D2$score <=0.8, "monitor", "no action"))
+
+T1 <- table(D2$prediction)
+
+#monitor
+T1[2]/sum(T1)
+
+#no action
+T1[3]/sum(T1)
+
+```
+##Comment
+Our prediction is off but we are still able to capture some patterns. Most of the people are in the "no action" category, so that's why it has the highest percentage with 58%. The next group is the "monitor", which has 42%.
+
+
 ### To Submit Your Assignment
 
 Please submit your assignment by first "knitting" your RMarkdown document into an html file and then commit, push and pull request both the RMarkdown file and the html file.

assignment5.Rproj

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