Final Project (R programming)

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)?
o  Due date: Monday 12/13/2021 11:59pm CT
o  Data: Canvas-assignment-Final Project
o  Submit your codes via Canvas-assignment-Final Project
o  Submission format: r.script and Single Excel summary output
You must follow below steps. Otherwise, your code won’t work on my machine
§  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 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") ·  What are your business suggestions/recommendations to the CEO? o  Due date: Monday 12/13/2021 11:59pm CT o  Submit your file via Canvas-assignment-Final Project o  Submission format: Single Word file §  Data visualization §  Executive summary reports §  etc.

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