CSC550X/Z: DATA MINING
Week 1 Assignment
Instructor: Dr. Tuan Tran
2.1 Assuming that data mining techniques are to be used in the following cases, identify whether the
task required is supervised or u
Assignment Chapter 4 Problems
Problems 4.1 Breakfast Cereals: Use the data for the breakfast cereal
example in the section 4.7 to explore and summarize the data as follow:(Note
that a few records cont
Week 4 Assignments
Problem
1. A data mining routine has been applied to a transaction dataset and has
classified 88 records as fraudulent (30 correctly so) and 952 as no fraudulent
(920 correctly so).
Assignment week 5 Problems
6.1) Predicting Boston Housing Prices: The le BostonHousing.xls contains information collected by the
US Census Service concerning housing in the area of Boston, Massachuset
Week 2 Problems
Assignment Week 2
Problems
1. Shipment of household appliances: Line Graph. The file Appliance shipments.xls contains
the series of quarterly shipment (in millions $) of U.S. household
Discussion Question
The chapter makes a distinction between using multiple linear regression analysis for explanation versus
prediction. Why is this important? If a set of independent variables can ex
Project Proposal
1
Topic:
The Universal Bank is one of the fast growing young bank especially in acquiring more customers. The
bank mainly focus in providing loans to the customers. Universal Bank com
Week 7 Problems
Problem
8.1 Personal Loan Acceptance: The file Universalfile.xls contains data on 5000
customers of Universal Bank. The data includes customer demographic information
(age, income, etc
LIFT CHARTS
Lift charts is a graphical method to calculate the facility to categorize the catch
observation of a class of importance. [Shm]. Lift charts are also called as gain
curve or gain charts[Sh
Week 9 Problems
Satellite Radio Customers: An analyst at a subscription-based satellite radio company
has been given a sample of data from their customer database, with the goal of finding
groups of c
Multiple Liner Regression may not work in strong local dependency of target variable to explanation
variable, in that case K nearest-neighbor method(K-NN) in regression can be alternative method. k-NN
Chapter 2 Problems
2.1 Problem
A Supervised Learning
B Unsupervised Learning
C Supervised Learning
D Unsupervised Learning
E Supervised Learning
F Supervised Learning
G Supervised Learning
H Supervise
Week8 Problems
9.1 Competitive Auctions on eBay.com. The file eBayAuctions.xls contains
information on 1972 auctions transacted on eBay.com during May-June 2004. The goal
is to use these data to build
Week 6 Problems
Question 1) Perform a k-nearest neighbor classification with all predictors except zip code
using k = 1. Remember to transform categorical predictors with more than 2 categories into
d
UNIVERSAL BANK
Universal Bank Project Proposal
CSC550X-Data Mining and Distributed Computing-Fall 2013
Submitted on November 3, 2013
SULLIVAN UNIVERSITY
1
UNIVERSAL BANK
Topic:
The Universal Bank is a
CLASSIFICATION AND PREDICATION
Classification
Classification is basic form of data analysis. A credit card transaction can be normal or fraudulent.
(Shmueli, 2010). The task which is common in data mi
Discussion Question
The chapter explains that Nave Bayes can only be used for purposes of classification. Why is this so?
Also, is Nave Bayes a supervised or unsupervised method? If there is an import
SULLIVAN UNIVERSITY
Graduate School
Course Syllabus
CSC550X - Data Mining
Credit Hours: 4
This course introduces the basic ideas and techniques of data
mining. Data mining is an emerging interdiscipli
Course topics at statistics.com (each row is a customer, column heads are topics taken [1] or not taken [0] by that customer)
2005 statistics.com
Intro
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Running head: DATA MINING DROP BOX 9.1
Data Mining Drop Box 9.1
Sullivan University
CSC550X
11/27/2017
1
DATA MINING DROP BOX 9.1
2
Problem: Satellite radio customers.
The Association rule determines
Running head: DATA MINING - CHAPTER 8 PROBLEMS
Data Mining - Chapter 8 Problems
Sullivan University
CSC550X
11/11/2017
1
DATA MINING - CHAPTER 6 PROBLEMS
a.
2
Create a pivot table for the training dat
Running head: DATA MINING - CHAPTER 9 PROBLEMS
Data Mining - Chapter 9 Problems
Sullivan University
CSC550X
11/21/2017
1
DATA MINING - CHAPTER 5 PROBLEMS
2
a. Fit a classification tree using all predi
Running head: DATA MINING
1
Data Mining - Chapter 2 Problem Solutions
Sullivan University
Data Mining
2
DATA MINING- CHAPTER 2 PROBLEM SOLUTIONS
2.1 Assuming that data mining techniques are to be used
An Overview of Symmetric
Cryptography
Group 4
Mohammed Samiuddin
Naveen Kumar Chalasani
Nikhil Bhatagalikar
Praful Naram
Rafiq Alam
Syed Usman Ali
Sibtain Hussain
Yue Zhou
SYMMETRIC CRYTOGRAPHY
Symmet
Global Information Incident Management Procedure
1. Purpose and Scope. This Global Information Incident Management Procedure is designed to
ensure our preparedness to manage an Information Incident an
12/9/2017
Review Test Submission: Lab 9: Assessment Quiz 2017_.
Srihari Kavuri 25
Home
H
Content
.
Courses
Lab 9: Protecting Digital Evidence, Documentation, and the Chain of Custody
Review Test Submi
Final Exam Part 2
Garima Agrawal (GAGRAW7465) | Data Mining | 09/06/2017
TOPIC 1: THE DATA MINING PROCESS
Describe the stages of the data mining process. What are they and what happens at each
stage?
Assignment 8 Classification and Regression Trees
Assignment 8 Classification and Regression Trees
Nitin Prakash Majgi
Sullivan University
Data Mining
November 19, 2017
Assignment 8 Classification and
Pivot tables are the powerful interactive tool that can abstract either large or small amount of data.
Pivot tables are simple to create and it can identify patterns and is visually engaging and effec