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.
