Student: Stanley

Final: Term Project (Due: 11:59pm, 06/21/2020)

Final Term Project
Posted on: Wednesday, June 10, 2020 7:53:04 PM EDT
Final: Term Project (Due: 11:59pm, 06/21/2020)
Worth: 150 points
Project Topics:
You have been asked by management (manufacturing, healthcare, retail, financial, and etc.,) to create a research report using a data mining tool, data analytic, BI tool. It is your responsibility to search, download, and produce outputs using one of the tools. You will need to focus your results on the data set you select. 
Ensure to address at least one topic covered in Chapters 1-9 with the outputs. The paper should include the following as Header sections. You can find some related topics if you want. Then write the term paper.
Example of topics:
1. Using data mining techniques for learning systems….
2. How to improve Health Care System using data mining techniques…
3. Design and develop Network/Information Security using data mining techniques…
4. How efficiently extract knowledge from a big data using data mining techniques…
5. Using data mining techniques to improve the financial/stock information systems…
Types of Data Analytic Tools:
https://www.octoparse.com/blog/top-30-big-data-tools-for-data-analysis/
Excel with Solver, but has limitations
R Studio
Tableau Public has a free trial
Microsoft Power BI
Search for others with trial options
Examples of Dataset:
http://www.rdatamining.com/
https://www.forbes.com/sites/bernardmarr/2016/02/12/big-data-35-brilliant-and-free-data-sources-for-2016/#4b3e96f1b54d
 
Example: Project Construction Format:
You should follow the following content format:
Title: Topic
Name:  Logan Lee
ID: 123-45-567
I.    Introduction
II.    Background [Discuss tool, benefits, or limitations]
III.    Review of the Data [What are you reviewing?]
IV.    Exploring the Data with the tool
V.    Classifications Basic Concepts and Decision Trees
VI.    Other Alternative Techniques
VII.    Summary of Results
References
(Ensure to use the Author, APA citations with any outside content).
Assignment Instructions:
1. No ZIP file
2. The submitted assignment must be typed by ONE Single MS Word/PDF file.
3. At least 10 pages (not including heading and content list pages) and 5 references.
4. Use 12-font size and 1.5 lines space
5. No more than 4 figures and 3 tables
6. Follow APA style and content format: UC follows the APA (American Psychological Association) for writing style in all its courses which require a Paper or Essay.
http://www.apastyle.org/
 
Grading Rubrics:
This assignment is an individual work. Please do NOT co-working with colleagues to violate the academic integrity. Grading for this assignment will be based on answer of completed the above requirements, quality, logic/organization of the paper, and language and writing skills. Please see as follows:
•    Comprehension of Assignment (Addressed the question completely and thoroughly. Provided additional supporting evidence, demonstrating a full comprehension of subject matter): 20/20 points
•    Application of Course Knowledge and Content (Thorough technical application of course knowledge and content in a complete and concise manner): 20/20 points
•    Organization of Ideas (Original ideas are effectively developed and presented in a logical, sequential order throughout the entire assignment. Includes adequate and appropriate supporting evidence): 20/20 points
•    Writing Skills (Mechanics (spelling, grammar, and punctuation) are flawless, including proficient demonstration of citations and formatting throughout the entire assignment): 20/20 points
•    Research Skills (Accurate and applicable use of resources relevant to the subject matter that enhance the overall assignment): 20/20 points

The topic i have selected:

Abstract:

I will be going with the subject "Fraud detection using Data Mining" Fraud affirmation is an approach to manage shield others from being attacked by designers or to get secure from the cash desperado and cheats with the assistance of progression. Information Mining (DM) blueprint systems in recognizing firms that issue fraudulent financial statements (FFS) and manages the obvious affirmation of parts related to FFS. This evaluation explores the settlement of Decision Trees, Neural Networks, and Bayesian Belief Networks in the prominent proof of sham spending summaries. The information vector is made out of degrees got from budgetary outlines.

Introduction:

Data uncovering is looking for secured, significant, and possibly supportive models in huge enlightening lists. Data Mining is connected to finding unsuspected/already dark associations among the data. 

It is a multi-disciplinary fitness that usages AI, estimations, AI, and database advancement.

Types of data mining

Relational database 

Data warehouse

Text mining data mining

Distributed database

Operational database

End-user database

Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003

Attachments
FinalTermProjectinstructions (1).docx

Budget: $17.00

Due on: June 18, 2020 00:00

Posted: 5 months ago.

Answers (0)