In this project, you will research and obtain a dataset relevant to a public or private organization or a specific problem domain, and will apply analytics techniques covered in the course to yield actionable insights.Questions/Challenges: As online retail continues to grow in popularity, understanding consumer buying activity online is crucial for making effective business decisions and shaping business strategies. By answering questions such as what are the most profitable items, what customers are most likely to buy, who is most likely to respond to specific strategies, how regularly does a customer purchase x item, how much are customers willing to spend in a single trip/single activity, targeting international audiences, we are able suggest strategies that could optimize the store strategies for operations and money for ads.You will be required to submit a 1-page project proposal (due by October 29), a 2-page project outline (due by November 16), deliver a 5-8 minute presentation in class (November 30), and submit a final written report of their work (due by December 7), as detailed below.Projects may incorporate descriptive, predictive, or prescriptive analytics techniques, or any combination thereof. However, purely descriptive analyses are discouraged; there must be a clear explanation of how the results of the project will influence decision making (either an individual decision or a specific type of decision). That is, groups are required to state clearly and specifically how their results will help the people or organization(s) involved.There is no a priori restriction on the setting or domain of the project. Groups are encouraged to choose topics that are of interest to them. If in doubt about whether a topic would make an appropriate project, ask! The quality of the project depends on many factors, including but not limited to: collection of a sufficient quantity of usable data, appropriate application of the tools and methods learned in class, generation of actionable insights, and clear communication of the group’s work both orally and in writing.The dataset is required to have at least 500 usable data points at a minimum (more is preferable). Smaller datasets are unlikely to yield fruitful results from several methods covered in the course.Project Outline (3%): Due November 16 before class via BlackboardThe project outline should be a Word document no longer than two pages that describes the data set and next steps. It must include:A description of the data set (source, number of data points, variables, etc.)What kinds of analytics techniques the group plans to applyWhat kinds of results or insights the group expects/hopes that the analysis will revealPresentation (6%): November 30, in class.A copy of the slides is due by 3:30 PM Eastern via Blackboard.Presentations should be 5-8 minutes in length. No handouts nor supporting files other than a single .ppt file are permitted. It will be graded both on the correctness/appropriateness of the content, and clarity with which it is presented.The presentation should include:A brief description of the organization or problem domain, and the issue being analyzedA description & snapshot of the data setAn explanation of any unusual data or modeling challenges that the group encounteredAn explanation of the methods used to analyze the data setAn explanation of the results and their implications for the organization or decision maker(s)Written Report (14%): Due December 7 by 5:30 PM Eastern via BlackboardThe written report should be considered a complete report of the work done by the group. It will be graded based on the correctness and thoroughness of the analysis, as well as the clarity with which it is explained. It should include all of the items listed above for the presentation, incorporating any additional supporting material or detailed explanations that were left out of the presentation. It should also incorporate any changes made since the presentation was delivered. Reports of more than 12 pages, inclusive of tables and charts, are discouraged. The dataset should not be included in its entirety; a snapshot of it is sufficient.You are encouraged to work on a draft of the written report in parallel with the presentation; this saves time and tends to result in higher quality reports.Recommended format & structure for the written report:Executive Summary (~1 page): a brief summary of the topic, the dataset, the methods used, and the group’s findings.This part of the report should be targeted toward a supervisor or client; a non-technical reader should be able to understand it.Introduction: an explanation of the topic of the project, including the organization(s) involved, and the issue or challenge the analysis will address. This section should be less than two pages.Data: an explanation of the dataset(s) used by the group, including any cleaning and formatting work that was necessary.Analysis: presentation and discussion of the analytics methods used by the group, including all relevant results and their interpretations.Discussion and Conclusion: a wrap-up of the analysis, explaining the insights and important takeaways from the group’s work.Appendix (if necessary): additional tables, charts, or other pieces of information from the analysis that might be of interest to a reader, but are not germane to the group’s main results. Snippets of R code can be included here to highlight anything unusual or of particular interest, but please do not include R scripts in their entirety. The Appendix does not count toward the 12-page limit.Use double spacing, 11-point Times New Roman font, and 1-inch margins. Be sure to reference any external sources used for quotes or information using a common style guide such as APA (http://www.apastyle.org/manual/index.aspx).The written report must look professional. Grammatical mistakes, typos, inconsistent formatting, etc., will result in points being deducted.Please find below a copy of a draft proposal which should be improved upon:Retail - Consumer ActivityQuestions/Challenges: As online retail continues to grow in popularity, understanding consumer buying activity online is crucial for making effective business decisions and shaping business strategies. By answering questions such as what are the most profitable items, what customers are most likely to buy, who is most likely to respond to specific strategies, how regularly does a customer purchase x item, how much are customers willing to spend in a single trip/single activity, targeting international audiences, we are able suggest strategies that could optimize the store strategies for operations and money for ads. Dataset Description: Invoice of online purchases from a home goods store that tracks who (CustomerID) purchased a specific item (StockCode), the price of the items (UnitPrice) and what items a customer bought in a single “trip” (InvoiceNo).Dataset:Source: Kaggle - OnlineRetail.csvNumber of Data Points: 542KVariables: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, CountryAnalytics Techniques:AssociationClusteringHierarchical RegressionForecastingActivityThe impact of online shopping on block retail storesWhat items customers are likely to purchase togetherWhat country would respond to targeted ads for online shoppingThe ideal price point per item to encourage customers to buyWhat kinds of results or insights the group expects/hopes that the analysis will reveal:

Objectives:Practice using quick sort from modules.Investigate the recursion limit of Quick Sort.Visualize the results of your analysis.Use git features such as commit and clone to write and read from your repository.Material from:Syllabus and Program Guidelines.Material from Week 9 part 2 module.Material from Chapter 7 of Weiss Book.Please read the files follow the requirements, make sure the program can run Write the java doc and readme.