Business Analytics Rstudio Presentation

3) You are expected to search for answers from google/stackoverflow/R-documentation/our R-reference book, etc. rather than just stick to the class R-code. This is the process of “learning to learn”.

The grading rubric includes the following:

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Phase 1 – exam will be graded for 40 points.

Phase 2 – Presentation on final exam day and submission of your report. 60%

1. Report – problem solution – 40%

I will look for the Readability of the code – if the code is not annotated you lose points., Repeatability of the code i.e., we should be able to run your R code and get similar results are you have stated in your report. Understandability and matching the results in R-runs with what you have depicted in your report. Any graphs, dendrograms, plots if you have these please share them. Any predictions, simulations, etc. that you could do for the course. (Optional)f) An appendix consisting of screenshots of your computer and important runs that you conducted with your source code.

2. R- Code – 20%

SOLUTION SUBMISSION

Please submit your team’s solution as an MS Word document (or pdf file). Attach the R source code separately.

I. Dataset & Problem statement

In the dataset link -googleplaystore.csv.zip download – you will see data pertaining to applications from the Google AppStore.

You are an Application analyst responsible for understanding different aspects of apps so as to help the marketing team to create a sales and promotions strategy for these apps. As an analyst – you are tasked with understanding and deciding how you would promote the apps, and what is your rationale for such a decision. Use your imagination combined with the tools you have learned in the Business Analytics course.

Given the toolkit of 12 different analytics techniques taught in the Business Analytics course of Dr. Subramanian’s do the following:

II. Problem Statements

1) As a first step your goal is to classify the apps. Obtain groups of apps based on similar feature sets (or input variables) using any machine learning algorithm you learned in this course. How many groups will you ideally derive? & justify your answer.

2) Understand the characteristics of the groups by presenting their feature descriptions through analyzing means, standard deviations, and distributions using bar graphs, scatter plots, dendrograms, or other visuals. From each group of apps predict the sales

3) Based on your analysis – Present a plan with an actual dollar value (assumed) and a strategy for promoting your applications via online advertisements or TV ad placement?

 

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