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Lab1/Lab1.R

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+1+2
+2*3
+6/3
+a=1
+b=4
+a+b
+a-b
+a*b
+a/b
+search()
+a=readline("Enter a number")
+ls()
+getwd()
+list.files()
+
+
+a=c(1,2,3,4,5,6)
+a
+b=c(1+3i,2+4i,5+8i,8+7i)
+b
+d=c("T","F","F","T","F","T","T")
+d
+o=c('a','b','c','d')
+o
+f=c(1,3,5,3+4i,"T",'a')
+f
+
+
+
+
+a=c(1,5,3,87,3,8)
+b=c(4,8,6,5,9,3,9,4)
+a=c(a,b)
+a
+
+
+a=c(1,4,6,7,2,NA,28,NA,NA,7)
+mean(a, na.rm=TRUE)
+sum(a, na.rm=TRUE)
+prod(a, na.rm=TRUE)
+
+
+g=c(23,1,3,54,56,86,23,45,67,87)
+max(g)
+min(g)
+
+
+sort(g,TRUE)[2]
+
+t=c(2,2,4,5,7,4,8,4)
+unique(t)
+t[duplicated(t)]
+
+
+a=c(1,2,3)
+b=c(4,5,6)
+c=c(7,8,9)
+p=cbind(a,b,c)
+p
+
+a=c(500,465,700,478,892,446)
+w=(0.8*5)/100
+p=a*w
+p
+

Lab10/lab10.R

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+df <- iris[, -5]
+set.seed(240)
+kmeans.re <- kmeans(df, centers = 3, nstart = 20)
+kmeans.re
+kmeans.re$cluster
+plot(df[c("Sepal.Length", "Sepal.Width")])
+plot(df[c("Sepal.Length", "Sepal.Width")], col = kmeans.re$cluster)
+plot(df[c("Sepal.Length", "Sepal.Width")], col = kmeans.re$cluster, main = "K-means with 3 clusters")
+kmeans.re$centers
+kmeans.re$centers[, c("Sepal.Length", "Sepal.Width")]
+points(kmeans.re$centers[, c("Sepal.Length", "Sepal.Width")], col = 1:3, pch = 8, cex = 3) 
+y_kmeans <- kmeans.re$cluster
+clusplot(df[, c("Sepal.Length", "Sepal.Width")],y_kmeans,lines = 0,shade = TRUE,color = TRUE,labels = 2,
+         plotchar = FALSE,
+         span = TRUE,
+         main = paste("Cluster iris"),
+         xlab = 'Sepal.Length',
+         ylab = 'Sepal.Width')
+
+
+df<-readingSkills[c(1:105), ]
+split <- sample.split(df, SplitRatio = 0.8)
+split
+train_reg <- subset(df, split == "TRUE")
+test_reg <- subset(df, split == "FALSE")
+logistic_model <- glm(nativeSpeaker ~ age + shoeSize + score, data = train_reg, family = "binomial")
+summary(logistic_model)
+predict_reg <- predict(logistic_model,test_reg, type = "response")
+predict_reg
+predict_reg <- ifelse(predict_reg >0.75, 1, 0)
+table(test_reg$nativeSpeaker, predict_reg)
+missing_classerr <- mean(predict_reg != test_reg$nativeSpeaker)
+print(paste('Accuracy =', 1 - missing_classerr))
+
+
+
+png(file = "decision_tree.png")
+output.tree <- ctree(nativeSpeaker ~ age + shoeSize + score,data = df)
+plot(output.tree)
+dev.off()
+
+df = iris[,c(1,2,5)]
+model <- svm(Species ~ ., data=df)
+summary(model)
+final_svm <- svm(Species ~ ., data=df, kernel="radial", cost=1,gamma=1)
+plot(final_svm , df)
+
+
+df<-hsb
+set.seed(7267166)
+trainIndex=createDataPartition(df$prog, p=0.7)$Resample1
+train=df[trainIndex, ]
+test=df[-trainIndex, ]
+print(table(df$prog))
+NBclassfier=naiveBayes(prog~science+socst, data=train)
+print(NBclassfier)
+Print=function(model){
+  trainPred=predict(model, newdata = train, type = "class")
+  trainTable=table(train$prog, trainPred)
+  testPred=predict(NBclassfier, newdata=test, type="class")
+  testTable=table(test$prog, testPred)
+  trainAcc=(trainTable[1,1]+trainTable[2,2]+trainTable[3,3])/sum(trainTable)
+  testAcc=(testTable[1,1]+testTable[2,2]+testTable[3,3])/sum(testTable)
+  message("Contingency Table for Training Data")
+  print(trainTable)
+  message("Contingency Table for Test Data")
+  print(testTable)
+  message("Accuracy")
+  print(round(cbind(trainAccuracy=trainAcc, testAccuracy=testAcc),3))
+}
+Print(NBclassfier)
+print(table(train$prog))
+newNBclassifier=naive_bayes(prog~ses+science+socst,usekernel=T,data=train)
+Print(newNBclassifier)
+
+df = iris
+df = df[-c(1,8)]
+iris_tr_feat <- df[,1:4]
+set.seed(1)
+train_pred <- knn(iris_tr_feat, iris_tr_feat, df$Species, k=3)
+train_pred[1:10]
+accuracy <- mean(train_pred == df$Species)
+cat("Training Accuracy: ", accuracy, sep='')
+
+df <- read.csv('winequality.csv')
+df <- df[,c(1,9,11,12)]
+split <- sample.split(df, SplitRatio = 0.8)
+split
+train_reg <- subset(df, split == "TRUE")
+test_reg <- subset(df, split == "FALSE")
+logistic_model <- glm( quality ~ fixed.acidity+pH + alcohol,data = df)
+logistic_model
+summary(logistic_model)
+predict_reg <- predict(logistic_model,test_reg, type = "response")
+predict_reg
+predict_reg <- ifelse(predict_reg >0.5, 1, 0)
+table(test_reg$quality, predict_reg)
+missing_classerr <- mean(predict_reg != test_reg$quality)
+print(paste('Accuracy =', 1 - missing_classerr))
+
+
+png(file = "decision_tree1.png")
+output.tree <- ctree(quality ~ fixed.acidity + pH + alcohol,data = df)
+plot(output.tree)
+dev.off()
+
+model <- svm(quality ~., data=df)
+summary(model)
+final_svm <- svm(quality ~., data=df, kernel="radial", cost=1,gamma=1)
+plot(final_svm , df)
+
+split <- sample.split(df, SplitRatio = 0.7)
+trainl <- subset(df, split == "TRUE")
+testl <- subset(df, split == "FALSE")
+train_scale <- scale(trainl[, 1:4])
+test_scale <- scale(testl[, 1:4])
+set.seed(120)
+classifier_cl <- naiveBayes(quality ~ ., data = trainl)
+classifier_cl
+y_pred <- predict(classifier_cl, newdata = testl)
+cm <- table(testl$quality, y_pred)
+cm
+
+df1<- df[,1:4]
+set.seed(1)
+train_pred <- knn(df1, df1, df$quality, k=3)
+train_pred[1:10]
+accuracy <- mean(train_pred == df$quality)
+cat("Training Accuracy: ", accuracy, sep='')