Jiancong Shen Assignment 5

Assignment 5.Rmd

@@ -8,15 +8,14 @@ For this assignment we will be using data from the Assistments Intelligent Tutor
 
 #Install & call libraries
 ```{r}
-install.packages("party", "rpart")
-
 library(rpart)
 library(party)
+library(rpart.plot)
 ```
 
 ## Part I
 ```{r}
-D1 <- 
+D1 <- read.csv("intelligent_tutor.csv",header=TRUE)
 ```
 
 ##Classification Tree
@@ -40,22 +39,25 @@ We want to see if we can build a decision tree to help teachers decide which stu
 
 #Visualize our outcome variable "score"
 ```{r}
-
+boxplot(D1$score,axes = FALSE,staplewex = 1)
+text(y=boxplot.stats(D1$score)$stats, labels = boxplot.stats(D1$score)$stats,x=1.4)
+hist(D1$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$score>=0.8,1,ifelse(D1$score<=0.5,3,2))
+D1$advice<-as.factor(D1$advice)
 ```
 
 #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(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?
@@ -65,16 +67,22 @@ Upload the data "intelligent_tutor_new.csv". This is a data set of a differnt sa
 
 ```{r}
 #Upload new data
-
-D2 <- 
+library(dplyr)
+D2 <- read.csv("intelligent_tutor_new.csv")
 
 #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>=0.8,1,ifelse(D2$score<=0.5,3,2))
+accuracy<-nrow(filter(D2,prediction==1))/nrow(D2)
+
+#The predicted score is 65% accurate. 
+```
 
 ### To Submit Your Assignment
 

assignment5.Rproj

@@ -0,0 +1,13 @@
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