Complete the Code of a Given R Script File to Calculate Five (5) Performance Measures of a Customer Churn in a Telecommunication Company Logistic Regression Model

Customer churn occurs when customers stop doing business with a company. Retaining existing customers is less expensive than it is to acquire new customers and hence, building a good predictive model for customer churn is of importance to many companies.

In this assignment, you will use R and RStudio to complete a given R script (*.R) file to calculate two (2) performance measures of a telecommunication company customer churn logistic regression model and to calculate three (3) additional performance measures using only the most significant predictors. You will also provide a one-page summary of what you did, an interpretation of the results you obtained, and a reflection on your learning experience.

To prepare for this assignment:

1. Review the module’s interactive lecture and its references.
2. Download the telecommunication company customer churn data set: MIS470Telcocustomer.csv Edit Download MIS470Telcocustomer.csv.
3. Download the week 5 critical thinking R script file: MIS470-W5-CT.R Edit Download MIS470-W5-CT.R.
4. View the videos from the following section of the LinkedIn Learning course: R Essential Training Part 2: Modeling Data (Links to an external site.):
   1. Predicting Outcomes
5. Review this chapter on the caret package from the R for Statistical Learning online book (Links to an external site.) by David Dalpiaz.

To complete this assignment:

1. Complete the given R Script file (MIS470-W5-CT.R) to calculate two (2) performance measures of a telecommunication company customer churn logistic regression model and to calculate three (3) additional performance measures using only the most significant predictors according to the steps below. Give the completed script file a name that includes your first name and last name like this Solution-W5-FirstName-LastName.R:
   1. Load the provided R Script file (MIS470-W5-CT.R) into RStudio and run its code. The script file builds a logistic regression model of customer churn in a telecommunication company.
   2. Add R code to the script file to calculate and print out the
      logistic model accuracy using misClasificError variable
   3. Add R code to the script file to display a confusion matrix using the table() function.
   4. Add R code to create and evaluate the performance measures of 3 logistic models using the three (3) most significant predictors (either individual or collectively)
2. Execute your *.R script file and display the results of its execution in the RStudio console and/or the Plots tabs.
   1. Take screenshots, showing current date and time, to demonstrate successful completion of your work. The screenshot should show the R commands you applied and the results you obtained. Do not capture trial and error results. Only your final results should be captured.
3. Summarize your work in one page in which you explain what you did, interpret your results, and reflect on your experience:
   1. Explain how you completed this assignment and how you resolved the issues you faced, if any.
   2. Interpret the results you obtained from your actions including a discussion of the pattern you see from the three confidence intervals and an explanation of why such a pattern exists.
   3. Reflect on your experience with this assignment and the lessons you learned.

To submit your response to this assignment:

1. Prepare all the required screenshots.
2. Prepare your summary of your work (what you did, interpretation of results, and reflection).
3. Submit one Word document that:
   1. is at least 4-5 pages in length (including a cover page and a reference page, if any references are needed)
   2. Conforms to the CSU Global Guide to Writing and APA (Links to an external site.).
   3. Includes a cover page with the module number and name of the Critical Thinking assignment.
   4. Does not include an abstract.
   5. Includes all screenshots in the same order of execution of your *.R script.
   6. Includes a one-page summary of explanation of what you did, interpretation of results, and reflection. The one-page summary should follow the screenshot and should refer to them individually.
   7. Meets APA labeling requirements for all figures, including screenshots and code.
4. Submit your *.R script file
7. https://daviddalpiaz.github.io/r4sl/the-caret-pack…
8. https://www.linkedin.com/learning/r-essential-training-part-2-modeling-data/data-science-with-r-a-case-study?autoplay=true&u=2245842