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
@@ -40,41 +39,50 @@ 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?
-
+The teacher should pat attention to prior_prob_count and hints, since they are two deteermining factors in the graph.
 #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 <- 
+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. The model is too generalized we can have more variables add to the graph. The error rate is 35%.
+```
 
 ### To Submit Your Assignment
 

assignment5.Rproj

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