diff --git a/hw3/commands b/hw3/commands
new file mode 100644
index 0000000..fab2f17
--- /dev/null
+++ b/hw3/commands
@@ -0,0 +1,44 @@
+library(ISLR)
+library(MASS)
+library(class)
+
+auto = read.table("auto.data",header=T,na.strings="?")
+
+
+auto$mpg01=rep(0,397)
+auto$mpg01[auto$mpg>median(auto$mpg)]=1
+
+sample(auto,size=length(mpg01)/2)
+
+train_bools <- (auto$year %% 2 == 0)
+train_data = auto[train_bools,]
+test_data = auto[!train_bools,]
+
+
+
+lda.fit=lda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data)
+lda.pred=predict(lda.fit, test_data)
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+
+qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
+qda.pred=predict(qda.fit,test_data,na.rm=T)
+mean(qda.pred$class!=test_data$mpg01,na.rm=T)
+
+glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
+glm.probs=predict(glm.fit,test_data,type="response")
+glm.pred=rep(0,199)
+glm.pred[glm.probs>.5]=1
+mean(glm.pred!=test_data$mpg01)
+
+set.seed(1)
+auto <- na.omit(auto)
+train_bools <- (auto$year %% 2 == 0)
+train_data = auto[train_bools,]
+test_data = auto[!train_bools,]
+
+
+train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
+test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
+train.mpg01 = auto$mpg01[train_bools]
+
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)