INFORMATIVE ESSAY
The purpose of the Informative Essay assignment is to choose a job or task that you know how to do and then write a minimum of 2 full pages, maximum of 3 full pages, Informative Essay teaching the reader how to do that job or task. You will follow the organization techniques explained in Unit 6.
Here are the details:
1. Read the Lecture Notes in Unit 6. You may also find the information in Chapter 10.5 in our text on Process Analysis helpful. The lecture notes will really be the most important to read in writing this assignment. However, here is a link to that chapter that you may look at in addition to the lecture notes:
https://open.lib.umn.edu/writingforsuccess/chapter/10-5-process-analysis/ (Links to an external site.)
2. Choose your topic, that is, the job or task you want to teach. As the notes explain, this should be a job or task that you already know how to do, and it should be something you can do well. At this point, think about your audience (reader). Will your reader need any knowledge or experience to do this job or task, or will you write these instructions for a general reader where no experience is required to perform the job?
3. Plan your outline to organize this essay. Unit 6 notes offer advice on this organization process. Be sure to include an introductory paragraph that has the four main points presented in the lecture notes.
4. Write the essay. It will need to be at least 2 FULL pages long, maximum of 3 full pages long. You will use the MLA formatting that you used in previous essays from Units 3, 4, and 5.
5. Be sure to include a title for your essay.
6. After writing the essay, be sure to take time to read it several times for revision and editing. It would be helpful to have at least one other person proofread it as well before submitting the assignment.
Quiz2
# comments start with #
# to quit q()
# two steps to install any library
#install.packages("rattle")
#library(rattle)
setwd("D:/AJITH/CUMBERLANDS/Ph.D/SEMESTER 3/Data Science & Big Data Analy (ITS-836-51)/RStudio/Week2")
getwd()
x <- 3 # x is a vector of length 1
print(x)
v1 <- c(2,4,6,8,10)
print(v1)
print(v1[3])
v <- c(1:10) #creates a vector of 10 elements numbered 1 through 10. More complicated data
print(v)
print(v[6])
# Import test data
test<-read.csv("CVEs.csv")
test1<-read.csv("CVEs.csv", sep=",")
test2<-read.table("CVEs.csv", sep=",")
write.csv(test2, file="out.csv")
# Write CSV in R
write.table(test1, file = "out1.csv",row.names=TRUE, na="",col.names=TRUE, sep=",")
head(test)
tail(test)
summary(test)
head <- head(test)
tail <- tail(test)
cor(test$X, test$index)
sd(test$index)
var(test$index)
plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.
#It consists o ...
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
Ejercicios de estilo en la programaciónSoftware Guru
El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
En esta plática realizaremos el mismo ejercicio con un programa de software. Abarcaremos distintos estilos y paradigmas: programación monolítica, orientada a objetos, relacional, orientada a aspectos, monadas, map-reduce, y muchos otros, a través de los cuales podremos apreciar la riqueza del pensamiento humano aplicado a la computación.
Esto va mucho más allá de un ejercicio académico; el diseño de sistemas de gran escala se alimenta de esta variedad de estilos. También platicaremos sobre los peligros de quedar atrapado bajo un conjunto reducido de estilos a lo largo de tu carrera, y la necesidad de verdaderamente entender distintos estilos al diseñar arquitecturas de sistemas de software.
Semblanza del conferencista:
Crista Lopez es profesora en la Facultad de Ciencias Computacionales de la Universidad de California en Irvine. Su investigación se enfoca en prácticas de ingeniería de software para sistemas de gran escala. Previamente, fue miembro fundador del equipo en Xerox PARC creador del paradigma de programación orientado a aspectos (AOP). Crista es una de las desarrolladoras principales de OpenSimulator, una plataforma open source para crear mundos virtuales 3D. También es fundadora de Encitra, empresa especializada en la utilización de la realidad virtual para proyectos de desarrollo urbano sustentable. @cristalopes
Morel, a data-parallel programming languageJulian Hyde
What would the perfect data-parallel programming language look like? It would be as expressive as a general-purpose functional programming language, as powerful and concise as SQL, and run programs just as efficiently on a laptop or a thousand-node cluster.
We present Morel, a functional programming language with relational extensions, working towards that goal. Morel is implemented in the Apache Calcite community on top of Calcite’s relational algebra framework. In this talk, we describe Morel’s evolution, including how we are pushing Calcite’s capabilities with graph and recursive queries.
A talk given by Julian Hyde at ApacheCon, New Orleans, October 4th 2022.
Data Manipulation with Numpy and Pandas in PythonStarting with NOllieShoresna
Data Manipulation with Numpy and Pandas in Python
Starting with Numpy
#load the library and check its version, just to make sure we aren't using an older version
import numpy as np
np.__version__
'1.12.1'
#create a list comprising numbers from 0 to 9
L = list(range(10))
#converting integers to string - this style of handling lists is known as list comprehension.
#List comprehension offers a versatile way to handle list manipulations tasks easily. We'll learn about them in future tutorials. Here's an example.
[str(c) for c in L]
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
[type(item) for item in L]
[int, int, int, int, int, int, int, int, int, int]
Creating Arrays
Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.
#creating arrays
np.zeros(10, dtype='int')
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
#creating a 3 row x 5 column matrix
np.ones((3,5), dtype=float)
array([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
#creating a matrix with a predefined value
np.full((3,5),1.23)
array([[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23]])
#create an array with a set sequence
np.arange(0, 20, 2)
array([0, 2, 4, 6, 8,10,12,14,16,18])
#create an array of even space between the given range of values
np.linspace(0, 1, 5)
array([ 0., 0.25, 0.5 , 0.75, 1.])
#create a 3x3 array with mean 0 and standard deviation 1 in a given dimension
np.random.normal(0, 1, (3,3))
array([[ 0.72432142, -0.90024075, 0.27363808],
[ 0.88426129, 1.45096856, -1.03547109],
[-0.42930994, -1.02284441, -1.59753603]])
#create an identity matrix
np.eye(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
#set a random seed
np.random.seed(0)
x1 = np.random.randint(10, size=6) #one dimension
x2 = np.random.randint(10, size=(3,4)) #two dimension
x3 = np.random.randint(10, size=(3,4,5)) #three dimension
print("x3 ndim:", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
('x3 ndim:', 3)
('x3 shape:', (3, 4, 5))
('x3 size: ', 60)
Array Indexing
The important thing to remember is that indexing in python starts at zero.
x1 = np.array([4, 3, 4, 4, 8, 4])
x1
array([4, 3, 4, 4, 8, 4])
#assess value to index zero
x1[0]
4
#assess fifth value
x1[4]
8
#get the last value
x1[-1]
4
#get the second last value
x1[-2]
8
#in a multidimensional array, we need to specify row and column index
x2
array([[3, 7, 5, 5],
[0, 1, 5, 9],
[3, 0, 5, 0]])
#1st row and 2nd column value
x2[2,3]
0
#3rd row and last value from the 3rd column
x2[2,-1]
0
#replace value at 0,0 index
x2[0,0] = 12
x2
array([[12, 7, 5, 5],
[ 0, 1, 5, 9],
[ 3, 0, 5, 0]])
Array Slicing
Now, we'll learn to access multiple or a range of elements from an array.
x = np.arange(10)
x
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#from start to 4th position
x[: ...
It covers- Introduction to R language, Creating, Exploring data with Various Data Structures e.g. Vector, Array, Matrices, and Factors. Using Methods with examples.
make sure to discuss the following•your understanding of t.docxcarliotwaycave
make sure to discuss the following
•
your understanding of the purpose of the research
•
what the researchers found (i.e., the results of the research study)
•
the broader implications or practical application of the research
•
any problems you see in the research study
•
what the researchers might have done differently to improve their study
•
future research that might be conducted in this particular research area
.
make sure to discuss the following•your understanding of .docxcarliotwaycave
make sure to discuss the following:
•
your understanding of the purpose of the research
•
what the researchers found (i.e., the results of the research study)
•
the broader implications or practical application of the research
•
any problems you see in the research study
•
what the researchers might have done differently to improve their study
•
future research that might be conducted in this particular research area
.
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El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
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Semblanza del conferencista:
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What would the perfect data-parallel programming language look like? It would be as expressive as a general-purpose functional programming language, as powerful and concise as SQL, and run programs just as efficiently on a laptop or a thousand-node cluster.
We present Morel, a functional programming language with relational extensions, working towards that goal. Morel is implemented in the Apache Calcite community on top of Calcite’s relational algebra framework. In this talk, we describe Morel’s evolution, including how we are pushing Calcite’s capabilities with graph and recursive queries.
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Data Manipulation with Numpy and Pandas in PythonStarting with NOllieShoresna
Data Manipulation with Numpy and Pandas in Python
Starting with Numpy
#load the library and check its version, just to make sure we aren't using an older version
import numpy as np
np.__version__
'1.12.1'
#create a list comprising numbers from 0 to 9
L = list(range(10))
#converting integers to string - this style of handling lists is known as list comprehension.
#List comprehension offers a versatile way to handle list manipulations tasks easily. We'll learn about them in future tutorials. Here's an example.
[str(c) for c in L]
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
[type(item) for item in L]
[int, int, int, int, int, int, int, int, int, int]
Creating Arrays
Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.
#creating arrays
np.zeros(10, dtype='int')
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
#creating a 3 row x 5 column matrix
np.ones((3,5), dtype=float)
array([[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.],
[ 1., 1., 1., 1., 1.]])
#creating a matrix with a predefined value
np.full((3,5),1.23)
array([[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23],
[ 1.23, 1.23, 1.23, 1.23, 1.23]])
#create an array with a set sequence
np.arange(0, 20, 2)
array([0, 2, 4, 6, 8,10,12,14,16,18])
#create an array of even space between the given range of values
np.linspace(0, 1, 5)
array([ 0., 0.25, 0.5 , 0.75, 1.])
#create a 3x3 array with mean 0 and standard deviation 1 in a given dimension
np.random.normal(0, 1, (3,3))
array([[ 0.72432142, -0.90024075, 0.27363808],
[ 0.88426129, 1.45096856, -1.03547109],
[-0.42930994, -1.02284441, -1.59753603]])
#create an identity matrix
np.eye(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
#set a random seed
np.random.seed(0)
x1 = np.random.randint(10, size=6) #one dimension
x2 = np.random.randint(10, size=(3,4)) #two dimension
x3 = np.random.randint(10, size=(3,4,5)) #three dimension
print("x3 ndim:", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)
('x3 ndim:', 3)
('x3 shape:', (3, 4, 5))
('x3 size: ', 60)
Array Indexing
The important thing to remember is that indexing in python starts at zero.
x1 = np.array([4, 3, 4, 4, 8, 4])
x1
array([4, 3, 4, 4, 8, 4])
#assess value to index zero
x1[0]
4
#assess fifth value
x1[4]
8
#get the last value
x1[-1]
4
#get the second last value
x1[-2]
8
#in a multidimensional array, we need to specify row and column index
x2
array([[3, 7, 5, 5],
[0, 1, 5, 9],
[3, 0, 5, 0]])
#1st row and 2nd column value
x2[2,3]
0
#3rd row and last value from the 3rd column
x2[2,-1]
0
#replace value at 0,0 index
x2[0,0] = 12
x2
array([[12, 7, 5, 5],
[ 0, 1, 5, 9],
[ 3, 0, 5, 0]])
Array Slicing
Now, we'll learn to access multiple or a range of elements from an array.
x = np.arange(10)
x
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#from start to 4th position
x[: ...
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make sure to discuss the following•your understanding of t.docxcarliotwaycave
make sure to discuss the following
•
your understanding of the purpose of the research
•
what the researchers found (i.e., the results of the research study)
•
the broader implications or practical application of the research
•
any problems you see in the research study
•
what the researchers might have done differently to improve their study
•
future research that might be conducted in this particular research area
.
make sure to discuss the following•your understanding of .docxcarliotwaycave
make sure to discuss the following:
•
your understanding of the purpose of the research
•
what the researchers found (i.e., the results of the research study)
•
the broader implications or practical application of the research
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any problems you see in the research study
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what the researchers might have done differently to improve their study
•
future research that might be conducted in this particular research area
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your understanding of the purpose of the research
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what the researchers found (i.e., the results of the research study)
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the broader implications or practical application of the research
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any problems you see in the research study
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what the researchers might have done differently to improve their study
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Major Diseases
CHAPTER 10
*
Chapter 10
Major Diseases
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Infectious Diseases
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Viruses Fungi
Bacteria Protozoa
Helminths (Parasitic Worms)
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Main questions of the essay
1. What are types of daily-lived situations that confront undocumented youth sense of identity and belonging?
2. What types of psychological trauma impacts gow undocumented youth negotiate their daily lived situations?
3. How do undocumented youth respond to their daily psychological trauma that they experienced?
Use some examples to describe those experiences happened to those undocument youth, it can be made up.
In the Conclusion, provide some solution. Picture yourself as a policy maker.
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200 words
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Major Approaches to Clinical Psychology PresentationSelect one.docxcarliotwaycave
Major Approaches to Clinical Psychology Presentation
Select
one of the following psychological diagnoses:
·
Depressive disorder
·
Generalized anxiety disorder
·
Attention deficit hyperactivity disorder
·
Obsessive-compulsive disorder
Create
a 9-12 slide Microsoft
®
PowerPoint
®
presentation, with Speaker Notes;
You have been asked to provide a presentation regarding psychological issues for a local community organization. Your audience is made up of adults within the community who are
not
mental health professionals, and who are interested in learning more about a specific mental health issue.
Provide
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Discuss
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Terms and concepts associated with the psychological approach.
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Incorporate
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Cite
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References
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Format
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Submit
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Make a 150 word response to the following. Incorporate what is said in 2. In your response. What factors make a work of art valuable in different ways to different people? Use texbook: Getlein, Mark. Living with Art, 9th Ed., New York: McGraw-Hill, 2010. Chapters 1-5
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What major challenges or barriers exist? Explain.
How do you think they could be overcome?
What types of technologies can be used in determining weaknesses in the integrity of infrastructure construction? Explain.
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How often do you think critical components should be inspected for weaknesses and vulnerabilities? Explain your rationale.
In your own words, please post a response to the Discussion Board and comment on at least two other postings. You will be graded on the quality of your postings.
For assistance with your assignment, please use your text, Web resources, and all course materials.
Unit Materials
.
Major Paper #1--The Point of View EssayWe will be working on this .docxcarliotwaycave
Major Paper #1--The Point of View Essay
We will be working on this paper for the next three units. The final draft of the paper--with all three sections described below--will be due at the end of Unit #4.
Purpose:
This paper assignment has several purposes. As the first major paper for this class, the Point of View Essay is designed to re-engage you with the fundamentals of all good writing, including using lush sensory details to show the reader a particular place (rather than tell them about it), basic organization, clear focus, etc. However, this unit does not function as a mere review. The Point of View Essay will also introduce you to the concept of "thinking and seeing rhetorically, and analyzing writing rhetorically"--using the Writer's Toolbox described in this unit to improve your writing and critical reading skills. Finally, the Point of View Essay allows you to reflect on this process.
The Assignment:
1. Pleasant/Unpleasant Description of the Place:
Choose a place you can observe for an extended period of time (at least 20-30 minutes). Use all of your senses (sight, hearing, touch, smell, even taste if possible) to experience the place, and record all of the sensations that you experience. As you record your data, you may wish to note which details naturally seem more positive, negative, or neutral, in terms of tone. (For instance, a stinky and overflowing trash barrel swarming with flies in a nearby alley might seem more inherently negative than a little white bunny rabbit hopping playfully across the lawn.) Then, you will use this information to help your write two descriptions of the place: one positive, one negative (at least 1-2 well-developed paragraphs or a minimum of 125-150 words each). Both descriptions should be factually true (same real time and real place), but you will want one description to be clearly positive in terms of tone and the other to be clearly negative. In addition to including the information and sensory details you've collected as the basis for these descriptions, you will also use the Writer's Toolbox to create your two contrasting impressions for this assignment. (The Writer's Toolbox is explained in the Lecture Notes section of this unit.) As you revise and refine your descriptions, please be sure you are "showing" your readers your place (really putting the readers "there" in the moment and in this scene), rather than simply "telling" them about it. You will also want to try to eliminate unnecessary linking verbs as much as you can, incorporating verbs that show "action" whenever possible.
2. Rhetorical Analysis:
Looking back at your descriptions, analyze how you created these two very different impressions of the place (one positive, one negative) without changing any of the facts. How did you make your place seem so positive in one paragraph and yet so negative in the other paragraph, without changing the facts? Discuss how you incorporated each of the tools from the Writer's T.
Major Essay for Final needs to be 5 pages long on the topic below an.docxcarliotwaycave
Major Essay for Final needs to be 5 pages long on the topic below and in Mla format with wroks citied AFTER he five pages due at 12:15 today
Requriements: 5 pages long
secondary sources 2 credible , 2 academic
Mla format (in-text ciations + works cited page)
focused specific paper topic
Identifiable methods of compostion choosen wisely
Topic Propsal:
The Media’s Influences on Society
The topic I chose to write my major essay on is the media’s influences on society.
This includes both positive and negative influences that the media portrays which plays a big part in society. I will explain how and why the media is used for much more than just entertainment purposes for society and how the media affects the choices society makes and its outcomes. The media affects society with these influences because it alters the way people think and it plays a role in the choices the people make. The change in peoples thoughts do to influences from what they see creates an opportunity for them to either make a good or bad choice depending on the type of influence that is shown. I believe that most of time the media portrays negative influences upon society. A positive influence from the media would be a commercial or show/clip about stopping bullying that informs people about the topic and why bullying is wrong and how it affects the lives of the victims. This type of media would influence society in a positive way because it would actually get society thinking about the situation and for the bullies some of them will actually realize the harm they are causing there victims and they would probably stop bullying people. A negative influence of the media would be a song with someone talking about how they murder people and take drugs and make it in a way to make people think it’s “cool” and then people who listen to it start imitating the things talked about in the song because they want to be “cool”. What I hope to accomplish with this essay is to open people eyes and help them see that the things they watch and listen to as in media actually alter the way they think and the choices they make so hopefully they change what they listen to and watch to more positive things.
The reason I chose to write about the media and its influences on society is to inform people that media has a bigger purposes than just entertainment for society and to hopefully help people make better choices and actually pay attention to the things they watch and listen to. I see how the media influence our modern society everywhere, at the basketball courts at the park at stores. Some of the people at the basketball courts I go to start listening to music that talk about drugs, gangs, murder and they start acting tough, being stupid and talking reckless and they get into arguments or even worse end up getting into fights and someone gets hurt I see this all the time. My paper is important because it will help shed light on the media motives and hopefully start making people m.
Major AssignmentObjectivesThis assignment will provide practice .docxcarliotwaycave
Major Assignment
Objectives
This assignment will provide practice and experience in:
·
Writing a program – Topic 2
·
Debugging– Topic 3
·
Stepwise Refinement& Modularisation – Topic 4 and Topic 10
·
Selection – Topic 5
·
Iteration – Topic 6
·
Arrays – Topic 7
·
File handling – Topic 9
·
Structs – Topic 11
NB Depend
i
ng on when you start this assignment you may need to read ahead especially on how to use files andstructs.
Suggestions:
Read the assignment specifications carefully first.Write the first version of your program in Week 4 and then create new versions as you learn new topics. Do NOT leave it until Week 11 to start writing the program. Review Topic 4 on stepwise refinement. This is how you should approach the major. Also note that though your program must do something and must compile it does not have to be complete to earn marks.
Specifications
One of the many tasks that programmers get asked to do is to convert data from one form to another. Frequently data is presented to users in well-labelled, tabular form for easy reading. However, it is impossible or very difficult to do further processing of the data unless it is changed into a more useful form.
For the purposes of this assignment I have downloaded and will make available the undergraduate applications to the 37 Australian universities from the Department of Education for 2009 – 2013 data file as a text file.
Your program will load this data into an array of structs, save the data in a form that is directly usable by a database (see below), display the data on the console in its original form and in its database form. It will also allow the user to display the highest number of applications for a given state and year.
Your program will use a menu to allow the user to choose what task is to be done. You will only be required to handle the Applications data. You can ignore the Offers and Offers rates data (see below).
Data
See “undergraduateapplicationsoffersandacceptances2013appendices.txt” for the original data.
This is the data your program should produce and save:
New South Wales Charles Sturt University 4265 4298 4287 4668 4614
New South Wales Macquarie University 6255 6880 7294 7632 7625
New South Wales Southern Cross University 2432 2742 2573 2666 2442
New South Wales The University of New England 1601 1531 1504 1632 1690
New South Wales The University of New South Wales 10572 10865 11077 11008 11424
New South Wales The University of Newcastle 9364 9651 9876 10300 10571
New South Wales The University of Sydney 13963 14631 14271 14486 15058
New South Wales "University of Technology, Sydney" 10155 9906 9854 10621 9614
New South Wales University of Western Sydney 11251 11.
magine that you are employed by one of the followingT.docxcarliotwaycave
magine
that you are employed by one of the following:
The social services division of a state or city government
A citizen action committee made up of community members
A police or fire department
A school or educational organization (public or private)
Develop
a 1,050- to 1,400-word needs statement and management plan that will be part of a proposal for a fictitious, grant-funded project of your choosing on behalf of your agency or organization. Include the following sections in your submission:
Paragraph One: Describe the characteristics of your fictitious agency or organization.
Paragraph Two: Discuss the possible funding sources you might contact for this grant proposal.
Needs Statement: Establish the specific problem the proposed project will address.
Management Plan: Describe the responsibilities of the project director (you) and any staff you will employ to implement the grant.
Format
your paper in accordance with APA guidelines.
Submit
your assignment.
Resources
Center for Writing Excellence
Reference and Citation Generator
Grammar and Writing Guides
Copyright 2018 by University of Phoenix. All rights reserved.
.
M4D1 Communication TechnologiesIn this module, we have focused .docxcarliotwaycave
M4D1: Communication Technologies
In this module, we have focused on understanding and using new communication technologies to be more competent communicators.
Respond to the following:
What social media strategy would you recommend for your current (or previous) workplace?
What areas do you think your organization can still improve?
How would you explain the importance of social media to your employer?
.
Luthans and Doh (2012) discuss three major techniques for responding.docxcarliotwaycave
Luthans and Doh (2012) discuss three major techniques for responding to political risk. Should an international organization always use all three techniques? Why or why not?
Your response should be at least 150 words in length. All sources used must be referenced; paraphrased and quoted material must have accompanying citations.
www.obm.nsaem.ru/.../International%20Management_
Main
Textbook.pd
.
Lyddie by Katherine Paterson1. If you were Lyddie how would you h.docxcarliotwaycave
Lyddie by Katherine Paterson
1. If you were Lyddie how would you have handled the incident with mr marsen?
2. Explain how Charlie's visit is a turning point in the story
3. How does Paterson show how important it is for a person to have goals in life
4. What are three examples that Lyddie supports her self pity with when she feels she has been too late for everything
5. What do we learn about Diana and how does this new development change Lyddies role in the factory
6. What event occurs in chapter 20 that was foreshadowed earlier? What predictions can you make about Lyddie's future
.
Luthans and Doh (2012) discuss feedback systems. Why is it important.docxcarliotwaycave
Luthans and Doh (2012) discuss feedback systems. Why is it important to consider an effective feedback system as an international manager?
Your response should be at least 150 words in length. All sources used must be referenced; paraphrased and quoted material must have accompanying citations.
www.obm.nsaem.ru/.../International%20Management_
Main
Textbook.pdf
use pages 212-215
.
Luthans and Doh (2012) discuss factors affecting decision-making aut.docxcarliotwaycave
Luthans and Doh (2012) discuss factors affecting decision-making authority. Briefly describe at least three factors that affect decision-making authority.
I attached chapter 11 to the reflection paper assignment so you can use that to answer this question
thank you
Your response should be at least 200 words in length. All sources used must be referenced; paraphrased and quoted material must have accompanying citations.
[removed][removed][removed][removed]
.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docx
1. INFORMATIVE ESSAY
The purpose of the Informative Essay assignment is to choose a
job or task that you know how to do and then write a minimum
of 2 full pages, maximum of 3 full pages, Informative Essay
teaching the reader how to do that job or task. You will follow
the organization techniques explained in Unit 6.
Here are the details:
1. Read the Lecture Notes in Unit 6. You may also find the
information in Chapter 10.5 in our text on Process Analysis
helpful. The lecture notes will really be the most important to
read in writing this assignment. However, here is a link to that
chapter that you may look at in addition to the lecture notes:
https://open.lib.umn.edu/writingforsuccess/chapter/10-5-
process-analysis/ (Links to an external site.)
2. Choose your topic, that is, the job or task you want to teach.
As the notes explain, this should be a job or task that you
already know how to do, and it should be something you can do
well. At this point, think about your audience (reader). Will
your reader need any knowledge or experience to do this job or
task, or will you write these instructions for a general reader
where no experience is required to perform the job?
3. Plan your outline to organize this essay. Unit 6 notes offer
advice on this organization process. Be sure to include an
introductory paragraph that has the four main points presented
in the lecture notes.
4. Write the essay. It will need to be at least 2 FULL pages
long, maximum of 3 full pages long. You will use the MLA
formatting that you used in previous essays from Units 3, 4, and
5.
5. Be sure to include a title for your essay.
6. After writing the essay, be sure to take time to read it several
times for revision and editing. It would be helpful to have at
least one other person proofread it as well before submitting the
2. assignment.
Quiz2
# comments start with #
# to quit q()
# two steps to install any library
#install.packages("rattle")
#library(rattle)
setwd("D:/AJITH/CUMBERLANDS/Ph.D/SEMESTER 3/Data
Science & Big Data Analy (ITS-836-51)/RStudio/Week2")
getwd()
x <- 3 # x is a vector of length 1
print(x)
v1 <- c(2,4,6,8,10)
print(v1)
print(v1[3])
v <- c(1:10) #creates a vector of 10 elements numbered 1
through 10. More complicated data
print(v)
print(v[6])
# Import test data
test<-read.csv("CVEs.csv")
test1<-read.csv("CVEs.csv", sep=",")
test2<-read.table("CVEs.csv", sep=",")
write.csv(test2, file="out.csv")
# Write CSV in R
write.table(test1, file = "out1.csv",row.names=TRUE,
na="",col.names=TRUE, sep=",")
3. head(test)
tail(test)
summary(test)
head <- head(test)
tail <- tail(test)
cor(test$X, test$index)
sd(test$index)
var(test$index)
plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/
Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for
summarizing a continuous univariate data set.
#It consists of the data set's: minimum, 1st quartile, median, 3rd
quartile, maximum
#Find the set, L, of data below the median. The 1st quartile is
the median of L.
#Find the set, U, of data above the median. The 3rd quartile is
the median of U.
print(summary(data))
anscombe<-read.csv("anscombe.csv")
print(summary(anscombe))
sd(anscombe$X)
var(anscombe$X)
sd(anscombe$x1)
var(anscombe$x1)
4. sd(anscombe$x2)
var(anscombe$x2)
sd(anscombe$x3)
var(anscombe$x3)
sd(anscombe$x4)
var(anscombe$x4)
sd(anscombe$y1)
var(anscombe$y1)
sd(anscombe$y2)
var(anscombe$y2)
sd(anscombe$y3)
var(anscombe$y3)
##-- now some "magic" to do the 4 regressions in a loop:
ff <- y ~ x
mods <- setNames(as.list(1:4), paste0("lm", 1:4))
for(i in 1:4) {
ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
## or ff[[2]] <- as.name(paste0("y", i))
## ff[[3]] <- as.name(paste0("x", i))
mods[[i]] <- lmi <- lm(ff, data = anscombe)
print(anova(lmi))
}
## See how close they are (numerically!)
sapply(mods, coef)
lapply(mods, function(fm) coef(summary(fm)))
## Now, do what you should have done in the first place:
PLOTS
op <- par(mfrow = c(2, 2), mar = 0.1+c(4,4,1,1), oma = c(0, 0,
2, 0))
for(i in 1:4) {
ff[2:3] <- lapply(paste0(c("y","x"), i), as.name)
plot(ff, data = anscombe, col = "red", pch = 21, bg = "orange",
cex = 1.2,
xlim = c(3, 19), ylim = c(3, 13))
5. abline(mods[[i]], col = "blue")
}
mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex
= 1.5)
par(op)
plot(sort(data$num_of_orders))
hist(sort(data$num_of_orders))
plot(density(sort(data$num_of_orders)))
plot(sort(data$gender))
hist(sort(data$sales_total))
plot(density(sort(data$sales_total)))
library(lattice)
densityplot(data$num_of_orders)
# top plot
# bottom plot as log10 is actually
# easier to read, but this plot is in natural log
densityplot(log(data$num_of_orders))
densityplot(data$sales_total)
densityplot(log(data$sales_total))
hist(data$sales_total, breaks=100, main="Sales total",
xlab="sales", col="gray")
# draw a line for the media
abline(v = median(data$sales_total), col = "magenta", lwd = 4)
# use rug() function to see the actual datapoints
rug(data$sales_total)
#Boxplots can be created for individual variables or for
variables by group.
#The format is boxplot(x, data=), where x is a formula and
data= denotes the data frame providing
#the data.
boxplot(data$sales_total,data=data, main="Dis by Sales",
6. xlab="Sales", ylab="Total")
# Boxplot of MPG by Car Cylinders, using one of R built-in
datasets
boxplot(mpg~cyl,data=mtcars, main="Car Milage Data",
xlab="Number of Cylinders", ylab="Miles Per Gallon")
#in our boxplot above, we might want to draw a horizontal line
at 12 where the national standard is.
abline(h = 12)
boxplot(data$sales_total,data=data, main="Total sales Bplot",
xlab="Sales", ylab="Total")
# Dot chart of a single numeric vector
dotchart(mtcars$mpg, labels = row.names(mtcars),
cex = 0.6, xlab = "mpg")
#install.packages("ROCR")
#library(ROCR)
# Simple Scatterplot
attach(mtcars)
plot(wt, mpg, main="Scatterplot Example",
xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)
#The R function abline() can be used to add vertical, horizontal
or regression lines to a graph
plot(data$sales_total, data$gender)
# Add fit lines
abline(lm(data$sales_total~ data$num_of_orders), col="red") #
regression line (y~x)
lines(lowess(data$sales_total, data$num_of_orders),
col="blue") # lowess line (x,y)
# Basic Scatterplot Matrix
pairs(data)
pairs(data[0:2])
# Scatterplot Matrices from the car Package
7. install.packages("car")
library(car)
install.packages("ggplot2")
library(ggplot2)
quit()
Quiz4
install.packages("tidyverse")
library(tidyverse) # data manipulation
install.packages("cluster")
library(cluster) # clustering algorithms
install.packages("factoextra")
library(factoextra) # clustering algorithms & visualization
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/
Lectures/Week3/RScripts")
getwd()
# Import test data
data<-read.csv("grades_km_input.csv")
print(summary(data))
data1 <- na.omit(data)
columns <- data[1, ]
print(summary(data))
#As we don't want the clustering algorithm to depend to an
arbitrary
#variable unit, we start by scaling data using the R function
scale:
data1 <- scale(data1)
head(data1)
distance <- get_dist(data1)
print(distance)
# plot cluster library
library(cluster)
8. # K-Means Cluster Analysis
# simplest example, just the dataset and number of clusters
fit <- kmeans(data1, 5) # 5 cluster solution
# get cluster means
aggregate(data1,by=list(fit$cluster),FUN=mean)
# append cluster assignment
mydata <- data.frame(data1, fit$cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
fit <- kmeans(data1, 8) # 8 cluster solution
# get cluster means
aggregate(data1,by=list(fit$cluster),FUN=mean)
# append cluster assignment
mydata <- data.frame(data1, fit$cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
# K-Means Clustering with 5 clusters
fit <- kmeans(mydata, 5)
# Determine number of clusters
wss <- (nrow(data1)-1)*sum(apply(data1,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(data1,
centers=i)$withinss)
#A plot of the within groups sum of squares by number of
clusters extracted can help determine the appropriate number of
clusters.
#The analyst looks for a bend in the plot similar to a scree test
in factor analysis
# We want (total within-cluster variation) to be the lowest
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
# Determine number of clusters
9. wss <- (nrow(data1)-1)*sum(apply(data1,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(data1,
centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
# Cluster Plot against 1st 2 principal components
# vary parameters for most readable graph
library(cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
# Centroid Plot against 1st 2 discriminant functions
library(fpc)
plotcluster(mydata, fit$cluster)
fviz_dist(distance, gradient = list(low = "#00AFBB", mid =
"white", high = "#FC4E07"))
# try with 25 attempts, 2 clusters
km <- kmeans(data1, centers = 2, nstart = 25)
str(km)
#The output of kmeans is a list with several bits of information.
The most important being:
# cluster: A vector of integers (from 1:k) indicating the cluster
to which each point is allocated.
#centers: A matrix of cluster centers.
#totss: The total sum of squares.
#withinss: Vector of within-cluster sum of squares, one
component per cluster.
#tot.withinss: Total within-cluster sum of squares, i.e.
sum(withinss).
#betweenss: The between-cluster sum of squares, i.e. $totss-
tot.withinss$.
10. #size: The number of points in each cluster.
# print the clusters
print(km)
# Plot clusters
fviz_cluster(km, data = data1)
(cl <- kmeans(data1, 8))
plot(data1, col = cl$cluster)
points(cl$centers, col = 1:3, pch = 8, cex = 2)
# sum of squares
ss <- function(x) sum(scale(x, scale = FALSE)^2)
## cluster centers "fitted" to each obs.:
fitted.data1 <- fitted(cl); head(fitted.data1)
resid.data1 <- data1 - fitted(cl)
## Equalities : ----------------------------------
cbind(cl[c("betweenss", "tot.withinss", "totss")], # the same two
columns
c(ss(fitted.data1), ss(resid.data1), ss(data1)))
stopifnot(all.equal(cl$ totss, ss(data1)),
all.equal(cl$ tot.withinss, ss(resid.data1)),
## these three are the same:
all.equal(cl$ betweenss, ss(fitted.data1)),
all.equal(cl$ betweenss, cl$totss - cl$tot.withinss),
## and hence also
all.equal(ss(data1), ss(fitted.data1) + ss(resid.data1))
)
kmeans(data1,1)$withinss # trivial one-cluster, (its W.SS ==
ss(x))
## random starts do help here with too many clusters
## (and are often recommended anyway!):
11. (cl <- kmeans(x, 5, nstart = 25))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:5, pch = 8)
Quiz5
install.packages("tidyverse")
library(tidyverse) # data manipulation
install.packages("cluster")
library(cluster) # clustering algorithms
install.packages("factoextra")
library(factoextra) # clustering algorithms & visualization
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/
Lectures/Week3/RScripts")
getwd()
# Import test data
data<-read.csv("grades_km_input.csv")
print(summary(data))
data1 <- na.omit(data)
columns <- data[1, ]
print(summary(data))
#As we don't want the clustering algorithm to depend to an
arbitrary
#variable unit, we start by scaling data using the R function
scale:
data1 <- scale(data1)
head(data1)
distance <- get_dist(data1)
print(distance)
# plot cluster library
library(cluster)
# K-Means Cluster Analysis
# simplest example, just the dataset and number of clusters
12. fit <- kmeans(data1, 5) # 5 cluster solution
# get cluster means
aggregate(data1,by=list(fit$cluster),FUN=mean)
# append cluster assignment
mydata <- data.frame(data1, fit$cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
fit <- kmeans(data1, 8) # 8 cluster solution
# get cluster means
aggregate(data1,by=list(fit$cluster),FUN=mean)
# append cluster assignment
mydata <- data.frame(data1, fit$cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
# K-Means Clustering with 5 clusters
fit <- kmeans(mydata, 5)
# Determine number of clusters
wss <- (nrow(data1)-1)*sum(apply(data1,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(data1,
centers=i)$withinss)
#A plot of the within groups sum of squares by number of
clusters extracted can help determine the appropriate number of
clusters.
#The analyst looks for a bend in the plot similar to a scree test
in factor analysis
# We want (total within-cluster variation) to be the lowest
plot(1:15, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
# Determine number of clusters
wss <- (nrow(data1)-1)*sum(apply(data1,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(data1,
centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters",
13. ylab="Within groups sum of squares")
# Cluster Plot against 1st 2 principal components
# vary parameters for most readable graph
library(cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE,
labels=2, lines=0)
# Centroid Plot against 1st 2 discriminant functions
library(fpc)
plotcluster(mydata, fit$cluster)
fviz_dist(distance, gradient = list(low = "#00AFBB", mid =
"white", high = "#FC4E07"))
# try with 25 attempts, 2 clusters
km <- kmeans(data1, centers = 2, nstart = 25)
str(km)
#The output of kmeans is a list with several bits of information.
The most important being:
# cluster: A vector of integers (from 1:k) indicating the cluster
to which each point is allocated.
#centers: A matrix of cluster centers.
#totss: The total sum of squares.
#withinss: Vector of within-cluster sum of squares, one
component per cluster.
#tot.withinss: Total within-cluster sum of squares, i.e.
sum(withinss).
#betweenss: The between-cluster sum of squares, i.e. $totss-
tot.withinss$.
#size: The number of points in each cluster.
# print the clusters
print(km)
14. # Plot clusters
fviz_cluster(km, data = data1)
(cl <- kmeans(data1, 8))
plot(data1, col = cl$cluster)
points(cl$centers, col = 1:3, pch = 8, cex = 2)
# sum of squares
ss <- function(x) sum(scale(x, scale = FALSE)^2)
## cluster centers "fitted" to each obs.:
fitted.data1 <- fitted(cl); head(fitted.data1)
resid.data1 <- data1 - fitted(cl)
## Equalities : ----------------------------------
cbind(cl[c("betweenss", "tot.withinss", "totss")], # the same two
columns
c(ss(fitted.data1), ss(resid.data1), ss(data1)))
stopifnot(all.equal(cl$ totss, ss(data1)),
all.equal(cl$ tot.withinss, ss(resid.data1)),
## these three are the same:
all.equal(cl$ betweenss, ss(fitted.data1)),
all.equal(cl$ betweenss, cl$totss - cl$tot.withinss),
## and hence also
all.equal(ss(data1), ss(fitted.data1) + ss(resid.data1))
)
kmeans(data1,1)$withinss # trivial one-cluster, (its W.SS ==
ss(x))
## random starts do help here with too many clusters
## (and are often recommended anyway!):
(cl <- kmeans(x, 5, nstart = 25))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:5, pch = 8)
16. #
https://github.com/Deepaknatural/Training/blob/master/MarketB
asket_Latest.R
#https://rstudio-pubs-
static.s3.amazonaws.com/267119_9a033b870b9641198b19134b
7e61fe56.html
# First lets use the AdultUCI dataset that comes bundled with
the arules package.
data()
data("Groceries")
summary(Groceries)
rules <- apriori(Groceries,parameter=list(support=0.002,
confidence = 0.5))
print(summary(rules))
print(rules)
inspect(head(sort(rules, by = "lift")))
plot(rules)
head(quality(rules))
plot(rules, method = "grouped")
plot(rules,method = "scatterplot")
plot(rules,method = "graph")
# Import test data
df <- read.csv("OnlineRetailSmall.csv")
head(df)
df <- df[complete.cases(df), ] # Drop missing values
# Change Description and Country columns to factors
# Factors are the data objects which are used to categorize the
data and store it as levels.
df %>% mutate(Description = as.factor(Description),
17. Country = as.factor(Country))
# Change InvoiceDate to Date datatype
df$Date <- as.Date(df$InvoiceDate)
df$InvoiceDate <- as.Date(df$InvoiceDate)
# Extract time from the InvoiceDate column
TransTime<-
format(as.POSIXct(df$InvoiceDate),"%H:%M:%S")
# Convert InvoiceNo into numeric
InvoiceNo <- as.numeric(as.character(df$InvoiceNo))
# Add new columns to original dataframe
cbind(df, TransTime, InvoiceNo)
glimpse(df)
# Group by invoice number and combine order item strings with
a comma
transactionData <- ddply(df,c("InvoiceNo","Date"),
function(df1)paste(df1$Description,collapse =
","))
transactionData$InvoiceNo <- NULL # Don't need these
columns
transactionData$Date <- NULL
colnames(transactionData) <- c("items")
head(transactionData)
write.csv(transactionData,"market_basket_transactionsSmall1.cs
v", quote = FALSE, row.names = TRUE)
# MBA analysis
# From package arules
tr <- read.transactions('market_basket_transactionsSmall.csv',
format = 'basket', sep=',')
18. summary(tr)
# plot the frequency of items
itemFrequencyPlot(tr)
itemFrequencyPlot(tr,topN=20,type="absolute",col=brewer.pal(
8,'Pastel2'), main="Absolute Item Frequency Plot")
arules::itemFrequencyPlot(tr,
topN=20,
col=brewer.pal(8,'Pastel2'),
main='Relative Item Frequency Plot',
type="relative",
ylab="Item Frequency (Relative)")
# Generate the a priori rules
association.rules <- apriori(tr, parameter = list(supp=0.001,
conf=0.8,maxlen=10))
summary(association.rules)
inspect(association.rules[1:10]) # Top 10 association rules
# Select rules which are subsets of larger rules -> Remove rows
where the sums of the subsets are > 1
subset.rules <- which(colSums(is.subset(association.rules,
association.rules)) > 1) # get subset rules in vector
# What did customers buy before buying "METAL"
metal.association.rules <- apriori(tr, parameter =
list(supp=0.001, conf=0.8),appearance =
list(default="lhs",rhs="METAL"))
inspect(head(metal.association.rules))
# What did customers buy after buying "METAL"
metal.association.rules2 <- apriori(tr, parameter =
list(supp=0.001, conf=0.8),appearance =
list(lhs="METAL",default="rhs"))
19. inspect(head(metal.association.rules2))
# Plotting
# Filter rules with confidence greater than 0.4 or 40%
subRules<-
association.rules[quality(association.rules)$confidence>0.4]
#Plot SubRules
plot(subRules)
# Top 10 rules viz
top10subRules <- head(subRules, n = 10, by = "confidence")
plot(top10subRules, method = "graph", engine = "htmlwidget")
# Filter top 20 rules with highest lift
# Paralell Coordinates plot - visualize which products along
with which items cause what kind of sales.
# Closer arrows re bought together
subRules2<-head(subRules, n=20, by="lift")
plot(subRules2, method="paracoord")
ITS-836 Course Paper, a total of 60 points (60% of the total
course points)
Izzat Alsmadi
GuidelinesRubrics to deliver Course Paper
Three deliverables
Deliverable 1, 10 points
· The deliverable should contain the following components:
(1) Overall Goals/Research Hypothesis (20 %)
1-3 research questions to navigate/direct all your project.
· You may delay this section until (1) you study all previous
work and (2) you do some analysis and understand the
dataset/project
(2) (Previous/Related Contributions) (40 %)
As most of the selected projects use public datasets, no doubt
there are different attempts/projects to analyze those datasets.
20. 30 % of this deliverable is in your overall assessment of
previous data analysis efforts. This effort should include:
· Evaluating existing source codes that they have (e.g. in
Kernels and discussion sections) or any other refence. Make
sure you try those codes and show their results
· In addition to the code, summarize most relevant literature or
efforts to analyze the same dataset you have picked.
· For the few who picked their own datasets, you are still
expecting to do your literature survey in this section on what is
most relevant to your data/idea/area and summarize those most
relevant contributions.
(3) A comparison study (40 %)
Compare results in your own work/project with results from
previous or other contributions (data and analysis comparison
not literature review)
The difference between section 3 and section 2 is that section 2
focuses on code/data analysis found in sources such as Kaggle,
github, etc. while section 3 focuses on research papers that not
necessary studied the same dataset, but the same focus area