Analyze a dataset in R

Use any techniques (correlation, regression, machine learning, deep learning, etc.) to answer the
following two questions:
 What factors significantly impact the amount of wines (AmountWines)?
Manually set your working directory (from toolbar – Session), DO NOT hardcode the file
path.
 Only use the following code to read data from your local drive
data_independent <- read.csv(file = 'data - for student - independent variables.csv')
data_dependent <- read.csv(file = 'data - for student - dependent variable.csv')
y <- data_dependent$AmountWines
Assign “new_data”, lower-case, to represent all independent variables you decided to use
 Assign “model”, to represent your model’s name
 Assign “preds”, lower-case, to represent your y-predictions based on the results of your
model. If your data has 1500 rows, then you should see 1500 rows of predictions.
 write below code after calculating your predictions
rmse<- sqrt(mean((y - preds)^2))
 Combine, “first_name”, “last_name”, and “rmse” as one single csv output file, name the csv
file as: finaloutput
 At the end of your scripts, write the following code (this is for TA, when testing your code,
don’t run those lines.)
source("predictdata.R")


Submission format: r.script and Single Excel summary output



***use the data provided***