Project 1

# load in package we'll use
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

# Read in the test and train data
setwd("/Users/apple/Desktop/361 project")
getwd()

## [1] "/Users/apple/Desktop/361 project"

train <- read.table('train.csv',header = T,sep=",")
test <- read.table('test.csv',header = T,sep=",")

#fit our model using all predictors
model <- lm(medv~ ., data = train)

# use our model to make predictions
predicted_medv <- predict(model, newdata = test)
# look at the first few predicted cost to ensure we have something sensible.
head(predicted_medv)

##         1         2         3         4         5         6 
## 23.161111 22.527861  7.620764 15.538135  9.100576 16.819420

# create a dataframe with our results
my_submission <-tibble('id' = test$id, 'medv' = predicted_medv)
# save our file
write_csv(my_submission, 'submission.csv')

###Cp Criterion