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Practical no 1 Aim : Perform the data classification using classification algorithm using R/Python. Timeseries rainfall <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071) timeseris<-ts(rainfall,start=c(2012,1),frequency = 12) print(timeseris) plot(timeseris) b) Decision Tree data(iris) print(iris) library(rpart) model <- rpart(Species ~ ., data = iris, method = "class") prediction <- predict(model, iris, type = "class") table(prediction, iris$Species) new_data <- data.frame( Sepal.Length = 5.1, Sepal.Width = 3.5, Petal.Length = 1.4, Petal.Width = 0.2 ) new_prediction <- predict(model, new_data, type = "class") print(new_prediction) Practical No:2 Aim : Perform the Linear regression on the given data warehouse data using R/Python. data <- data.frame( weight = c(45, 50, 55, 60, 65, 70, 75), height = c(150, 155, 160, 165, 170, 175, 180) ) print(data) model...