ITS632 Harvard University Data Mining Questions
I need to post this question as it is due this week
as well as 2 exams as I told you i’m going to post bro.
Please ping me when you are available for bidding mean while please start working on this please let me know soon 🙂
ITS 632 –Introduction to Data Mining Final Term Paper Assignment (Individual Project): 1. Write an APA formatted paper on a business problem that requires data mining. 2. Paper deliverable should be 4-6 page (title page and references included) 3. Explain why the problem is of interest 4. Explain the general approach you plan to use 5. Explain the kind of data you plan to use 6. Explain how you plan to get the data. 7. You should describe your problem, approach, dataset, data analysis, evaluation, discussion, and conclusion in sufficient details. 8. You need to show supporting evidence in tables and/or figures. 9. Clickable auto-generated Table of Content is required 10.Include an abstract, a conclusion, and a reference page with 3-5 references. Requirements: Part 1 – Posting of the paper topic and preliminary references – Discussion Board Assignment is due Week 5 – June 9, 2019 (NOTE: Late papers (up to 24 hours late) will receive a 50% penalty. Papers not turned in within 24 hours of the due date will receive a grade of ZERO!) All written reports should be submitted in Word Doc format.
This is what bro you gave me to post in week 5 based on this we need to write paper
Introduction
According to a 2014 ACFE report, organizations lose 5% of revenues each year to fraud. The bigger the company, the more vulnerable to fraud it seems to be. Another survey by PWC reported that the average financial damage experienced produced due to fraud was the US $1.7 million by the company. Not only fraud affects companies economically but it also makes a psychological impact on them. Reputation and trust of customers and partners get heavily diminished whenever they get involved in fraud. Even if the company can be redeemed for the fraudulent act, emotional shock produced in their clients can go away so easily.
Also, fraud can occur without being noticed, we can think of this to be even more dangerous. As technology constantly evolves, fraudulent methods also do, and detection of fraudulent transactions becomes harder. Traditional methods for fraud detection such as internal controls and external audit have become archaic and the appearance of modern techniques turns out to be necessary.
In this fashion, this paper reviews successfully applied data mining techniques for fraud detection and prevention. Technical details are reviewed at the deepest way possible and interesting conclusions can be inferred from it, such as general methodology for fraud detection and prevention, the kind of industry where this problem occurs and so on.
Overview
As mentioned above, this paper objective is to review current knowledge from recent academic and industrial research of data mining techniques for fraud detection and prevention. Fraud is hereby defined as a deception to achieve unjust gains and its very basic components and elements are going to be reviewed on this paper. Information regarding fraudsters’ profile and target industries is also addressed. There is also the difference between internal and external fraud, and why the former is the most important to detect and prevent. This paper also pretends to encounter both traditional and new methods for fraud detection, one of them being the use of algorithms to identify patterns in big loads of gathered data, which by the way sets to be the perfect definition for data mining.
Some of the techniques reviewed include decision trees, neural networks, and Bayesian belief networks. Methods are expected to be compared and described the best possible way, so then the best conclusions can be attained. We can foresee two main classifications among all of these methods; supervised and unsupervised. The former uses the aid of labels to identify potential patterns whereas the latter does not. The important role of supervised learning in data mining should be also mentioned as well. Regardless of what method is used, it is worth to be mentioned that the results of data mining techniques only state the likelihood of an operation to be fraudulent since there is no way to know for sure whether a transaction is going to be a fraud.
Although one of the main challenges faced when applying data mining to fraud detection is the lack of data for experimental runs, this paper pretends to adopt an analytical approach to attack this problem and therefore find the best solution for overcoming it. More specific topics for research opportunities regarding data mining for fraud detection must be also reviewed and take into consideration for conclusions. One example of these topics is the research of data mining techniques for detecting asset misappropriation. Also, there are other problems not necessarily related to business that share similar characteristics with fraud and therefore can use the same techniques of data mining for its solution. These applications will also be mentioned and taken into consideration for conclusions. Examples are Terrorist Detection, Financial Crime Detection, Intrusion and Spam Detection, among others.
References:
Arwa Abu Shmais, R. H. (n.d.). Data Mining for Fraud Detection. Semantics Scholar.
Banarescu, A. (2015). Detecting and Preventing Fraud with Data Analytics. Procedia Economics and Finance, 1826-1836.
Jans, M., Lybaert, N., & Vanhoof, K. (2008, February). Data Mining for Fraud Detection. Retrieved June 2019, from ResearchGate: https://www.researchgate.net/publication/241153108
Phua, C., Lee, V., Smith, K., & Gayler, R. (n.d.). A Comprehensive Survey of Data Mining-based Fraud Detection Research. Arxiv.
Thiruvadi, S., & Patel, S. C. (2011). Survey of Data-mining Techniques used in Fraud Detection and Prevention. Information Technology Journal, 710-716.
Exam for data mining
Description
The final exam covers Chapters 6 – 10 and will consist of multiple choice and matching questions. This exam will be worth approximately 15% of your course grade. You will have 150 minutes to complete the exam. Please make sure that you have a good Internet connection when you start the exam. Once you have started it, you’ll need to complete it at that time. Please do not try to take the exam on your phone or tablet. I strongly encourage you to use a computer that is connected via ethernet cable to the Internet. You will have one attempt at the exam.
Final exam for organization leadership
K-FINAL EXAM
This exam consists of 100 true/false, fill-in-the-blank, multiple-choice questions. You will have 150 minutes to complete the exam. You only have ONE chance to take this exam, and once you start the exam, you MUST finish it once you start. Do not open 2 exam windows at the same time. If you do, there is a good chance the exam will self-submit before you are finished. The questions are worth .25 points each, and cover chapters 1 to 21 in your text.
All due on saturday and sunday bro
please go through the chapters meanwhile