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)?
Must follow below steps
§  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 §  Enter your name, such as: first_name<-'Yan' last_name<-'Lang' §  Assign new_data, lower-case, to represent all independent variables you decided to use §  Assign model, to represent your models 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, dont run those lines.) source("predictdata.R") ·  What are your business suggestions/recommendations to the CEO?