Computer Science

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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