MODULE 8 LAB / PART 2—————————————————–

install.packages(“tidymodels”) library(tidymodels)

#Are there any missing values? What is the minimum and maximum values of cmedv? What is the average cmedv value?

library(readr) boston <- read_csv(“~/Documents/boston.csv”) View(boston)

#Fill in the blanks to split the data into a training set and test set using a 70-30% split. Be sure to include the set.seed(123) so that your train and test sets are the same size as mine.

set.seed(123) split <- initial_split(boston, prop = 0.7 , strata = cmedv ) train <- training(split) test <- testing(split)

#How many observations are in the training set and test set? dim(train) dim(test)

#Compare the distribution of cmedv between the training set and test set. Do they appear to have the same distribution or do they differ significantly?

ggplot(train, aes(x = cmedv)) + geom_line(stat = “density”, trim = TRUE) + geom_line(data = test, stat = “density”, trim = TRUE, col = “blue”)

#Fill in the blanks to fit a linear regression model using the rm feature variable to predict cmedv and compute the RMSE on the test data. What is the test set RMSE? LM1 <- linear_reg() %>% fit(cmedv ~ ., data = train)

LM1 %>% predict(test) %>% bind_cols(test %>% select(cmedv)) %>% rmse(truth = cmedv, estimate = .pred)

rmse standard 6.831405

#Fill in the blanks to fit a linear regression model using all available features to predict cmedv and compute the RMSE on the test data. What is the test set RMSE? Is this better than the previous model’s performance?

LM2 <- linear_reg() %>% fit(cmedv ~ ., data = train)

LM2 %>% predict(test) %>% bind_cols(test %>% select(cmedv)) %>% rmse(truth = cmedv, estimate = .pred)

rmse standard 4.829261

Fit a K-nearest neighbor model that uses all available features to predict cmedv and compute the RMSE on the test data. What is the test set RMSE? Is this better than the previous two models’ performances?

install.packages(“kknn”) library(kknn)

KNN <- nearest_neighbor() %>% set_engine(‘kknn’) %>% set_mode(“regression”) %>% fit( cmedv ~ . , data = train)

KNN %>% predict(test) %>% bind_cols(test %>% select(cmedv)) %>% rmse(truth = cmedv, estimate = .pred) rmse standard 3.37

plot(cars)

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