Module 8 Lab

#1.

It is a supervised learning problem because we are predicting the median value of owner occupied homes using other variables as features.

#2. TARGET - Response variable: cmedv(median value of owner-occupied homes)

FEATURES - Predictor variables: lon, lat, crim, zn, indus, chas, nox, rm, age, dis, rad, tax, ptratio, lstat

#3. Since cmedv is a continuous variable this is a regression problem.

#START OF CODE 

#install packages, load in data set, check for missing values and stats
 
library(readr)
boston <- read_csv("Desktop/R/Data/boston.csv")
View(boston)
library(readr)
boston <- read_csv("Desktop/R/Data/boston.csv")

install.packages("rsample")
install.packages("tidymodels")
install.packages("tidyverse")
install.packages("kknn")
install.packages("conflicted")
install.packages("dplyr")
install.packages("kknn")
library(rsample)
library(tidyverse)
library(tidymodels)
library(conflicted)
library(dplyr)
library(kknn)
library(conflicted)

library(readr)
boston <- read_csv("Desktop/R/Data/boston.csv")
View(boston)
library(readr)
boston <- read_csv("Desktop/R/Data/boston.csv")


sum(is.na(boston)) 
0

min_cmedv <- min(boston$cmedv, na.rm = TRUE)
max_cmedv <- max(boston$cmedv, na.rm = TRUE)
mean_cmedv <- mean(boston$cmedv, na.rm = TRUE)
median_cmedv <- median(boston$cmedv, na.rm = TRUE)

min_cmedv
max_cmedv
mean_cmedv
median_cmedv

#splitting data 70-30% split

set.seed(123)

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

nrow(train)
nrow(test)

nrow(train)
[1] 352
nrow(test)
[1] 154

#compare distributions

train %>%
  ggplot(aes(x = cmedv)) +
  geom_histogram(binwidth = 1, fill = "blue", alpha = 0.7) +
  labs(title = "Distribution of cmedv in Training Set")

test %>%
  ggplot(aes(x = cmedv)) +
  geom_histogram(binwidth = 1, fill = "red", alpha = 0.7) +
  labs(title = "Distribution of cmedv in Test Set")

#linear regression model and computing RMSE

lm1 <- linear_reg() %>%
  fit(cmedv ~ rm, data = train)

lm1 %>%
  predict(test) %>%
  bind_cols(test %>% select(cmedv)) %>%
  rmse(truth = cmedv, estimate = .pred)

# A tibble: 1 × 3
.metric .estimator .estimate
<chr>   <chr>          <dbl>
  1 rmse    standard        6.83

#fit linear regression model compute RMSE with all factors

lm2 <- linear_reg() %>%
  fit(cmedv ~ ., data = train)

lm2 %>%
  predict(test) %>%
  bind_cols(test %>% select(cmedv)) %>%
  rmse(truth = cmedv, estimate = .pred)

# A tibble: 1 × 3
.metric .estimator .estimate
<chr>   <chr>          <dbl>
  1 rmse    standard        4.83

#fit K nearest neighbor model and calculate RMSE

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)

# A tibble: 1 × 3
.metric .estimator .estimate
<chr>   <chr>          <dbl>
  1 rmse    standard        3.37


plot(cars)