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Baltimore Caroline Carroll Harford Wicomico Prince Georges Baltimore City Prince Georges Anne Arundel Prince Georges Baltimore Baltimore City Baltimore City Penobscot Knox Penobscot Livingston Wayne Wayne Washtenaw Oakland Berrien Ingham Oakland Oakland Macomb Muskegon Kent Monroe Ingham MA MA MA MA MA MA MD MD MD MD MD MD MD MD MD MD MD MD MD MD MD MD MD ME ME ME MI MI MI MI MI MI MI MI MI MI MI MI MI MI 1887 1602 2760 2745 1603 2138 21224 21601 21061 21215 21117 21655 21074 21001 21801 20735 21230 20785 21076 20710 21234 21202 21217 4401 4864 4401 48116 48180 48126 48103 48075 49120 48933 48329 48307 48310 49442 49546 48160 48823 18 16 27 27 16 21 21 21 21 21 21 21 21 21 21 20 21 20 21 20 21 21 21 44 48 44 48 48 48 48 48 49 48 48 48 48 49 49 48 48 Final Project Milestone Two Data mining is the process of studying and analyzing great volumes of data with the objective of establishing patterns or discovering clusters that are useful in a company's decision making process. In analyzing Bubba Gump's data, there are two techniques that will provide useful information in regard to our discovery process. These are use of decision trees and an artificial neural network. A decision tree is a form of map or graph that outlines the various alternatives available to a company and how they affect critical metrics such as sales and profitability. This technique is appropriate for the company's analysis because it accommodates multiple variables and it is possible to carry out a sensitivity analysis of the results. [Tam15] Customer satisfaction depends on several factors such as environmental sanitation, service provided, or quality of food hence the need for a multivariable factor tool. An artificial neural network is also appropriate as it can identify patterns by using nonstatistical information. [Ari15] One of the data components in Bubba's data warehouse is customer information that includes their product preference and their impression of the company's products. The ANN will be useful in evaluating the qualitative data aspects that will complement the quantitative analysis conducted by the decision tree. The report will employ data visualization tools to present the results of the analysis to the management. The tools will include software such as Microsoft Power BI, graphs, and the decision tree. Microsoft Power BI is a web-based platform to be used perform analysis and visualize the data. The software allows the display of trends and data patterns with ease. Graphs, on the other hand, summarize the information while the decision tree outlines in an elaborate manner the various decision options available to the company. [Hwa10] By using the decision tree, it is easy for the company to trace the actions affecting its sales level from the first transaction to the last. This will build a more complete \"picture\" to establish a foundation for our analysis to influence future impacting business decisions. The specific research question for the study is: What factors affect customer satisfaction at Bubba Gump Shrimp Company? To address the above question, the following research question will be used to determine the patterns in the data. What is the similarity in the data provided by the survey correspondents? Throughout the data mining task, we will evaluate whether the research question is adequately answered by determining whether or not the answers are straightforward in nature. If the answers are clear, it will be easy to create the decision tree using the statistical data. There will also be enough qualitative information for an ANN. The time taken to analyze the information will be a good measure of the project's progress. Note that the mining and analysis does not end after the presentation, and several follow-up explorations need to be accomplished. [Hwa10] This includes evaluating whether the results are consistent with the situation on the ground. Conducting the follow-up will help the management identify any inconsistencies with the information in the data houses and give them an opportunity to adjust the situation. There is secondary information that provides useful information for the study. However, they possess several weaknesses including being outdated while some contain information marred with errors. Regardless of these shortcomings, they are useful as they represent a portion of the historical data and the framework to follow for effective results. References Arif, M., Alam, K. A., & Hussain, M. (2015). Application of Data Mining Using Artificial Neural Network: Survey. International Journal of Database Theory and Application, 245-270. Hwang, J., & Zhao, J. P. (2010). Factors Influencing Customer Satisfaction or Dissatisfaction in the Restaurant Business Using AnswerTree Methodology. Journal of Quality Assurance in Hospitality & Tourism, 93-110. Tama, B. A. (2015). DATA MINING FOR PREDICTING CUSTOMER SATISFACTION IN FAST-FOOD RESTAURANT. Journal of Theoretical and Applied Information Technology, 18-24. \f