Can I use a multiple linear regression to predict a certain variable

[Jaime]

I am trying to generate a linear regression to predict the sale price of some cars from the library "imports85", My code is as follows: ´

library(tidyverse)
library(rpart)
library(rpart.plot)
library(randomForest)
data("imports85")
db<-imports85
View(db)
db<-db[,-1 ]
db<-db[,-1 ]
set.seed(0)

library(fastDummies)
library(naniar)

vis_miss(db)
db <- na.omit(db)
vis_miss(db)

db2 <-dummy_cols(db, select_columns=c("make", "fuelType", "aspiration", "numOfDoors", 
                                     "bodyStyle", "driveWheels", "engineLocation", 
                                     "engineType", "numOfCylinders", "fuelSystem"),remove_first_dummy=T,
                remove_selected_columns=T )


ind <- sample(2, nrow(db2), replace = TRUE, prob = c(0.5, 0.5))

train2 <- db2[ind==1,]
test2 <- db2[ind==2,]

model <- lm(price ~ ., data = train2)
summary(model)

classPred2 <- predict(object = model, test2)
classPred2

My first question comes from model <- lm(price ~ ., data = train2). Since I have a lot of columns in the matrix, I cannot write all of them at the right side of the ~. Am I using this way the price to predict the price. Should I remove it from the right part somehow?

My second question comes from classPred2 <- predict(object = model, test2), I don't know how the prediction works, since I am using test2, which includes the price, which is the variable that I am trying to predict. Should I remove the column in question?

Any answer is appreciated.
Best regards.

[technocrat]

@Jaime

model <- lm(price ~ ., data = train2)

This runs an ordinary least square regression of price against all other variables in train2 (meaning of ~ .) to create an object named model. Whether all of them are useful depends on the data.

@Jaime

classPred2 <- predict(object = model, test2)

this applies the model named model to the test2 object to assess how well model predicts the fit in terms of some goodness of fit evaluation.

Creating a model with lm is very straightforward; however, interpreting the model, especially if it has many coefficients can be difficult.

The {olsrr} package

provides tools for evaluating the results. The {rms} package and accompanying text provide advanced tools for the same job.

[Jaime]

Thanks for the answer, now I undestand it all way better