Order Now
External link to Question : 11.An activity that would be expensed currently as research and : 1242681
Accounting

Question : 11.An activity that would be expensed currently as research and : 1242681

  11.An activity that would be expensed currently as research and development costs is the a.Testing in search for or evaluation of product or process alternatives b.Adaptation of an existing capability to a particular requirement or customer’s need as a part of continuing commercial activity c.Legal work in connection with patent applications or litigation, and the sale or licensing of patents d.Engineering follow-through in an […]

External link to Question : 52.Hailey Medical Supply Co., which had no beginning balance in : 1254491
Accounting

Question : 52.Hailey Medical Supply Co., which had no beginning balance in : 1254491

  52.Hailey Medical Supply Co., which had no beginning balance in its Accounts Receivable and Allowance for Doubtful Accounts, earned $80,000 of revenue on account during 2013. During 2013, Hailey collected $64,000 of cash from its receivables accounts. The company estimates that it will be unable to collect 1% of revenue on account. The amount of net realizable value of receivables on the December 31, […]

External link to Question : 31. Which of the following uses questions and exercises to : 1234427
Accounting

Question : 31. Which of the following uses questions and exercises to : 1234427

  31. Which of the following uses questions and exercises to guide individuals in determining their strengths and weaknesses, job and career opportunities, and necessary steps for achieving their goals? A) Succession planning B) Realistic recruitment C) Career workbooks D) Skills inventory E) Career center 32. All of the following are benefits of a career development system EXCEPT: A) evaluation of career tools and practices. […]

External link to Question : 51.Which of the following an objective of capital budgeting? A. To eliminate : 1258749
Accounting

Question : 51.Which of the following an objective of capital budgeting? A. To eliminate : 1258749

  51.Which of the following is an objective of capital budgeting?    A. To eliminate all risk. B. To discount all future and past cash flows. C. To earn a satisfactory return on investment. D. To reverse past decisions. E. To reduce the number of investment activities. 52.The time value of money concept:    A. Means that a dollar today is worth less than a dollar tomorrow. B. Means that a dollar tomorrow is […]

External link to Question : 61.Century Corporation issued 400,000 shares of $4 par value common : 1237471
Accounting

Question : 61.Century Corporation issued 400,000 shares of $4 par value common : 1237471

    61.Century Corporation issued 400,000 shares of $4 par value common stock at the time of its incorporation. The stock was issued for cash at a price of $16 per share. During the first year of operations, the company sustained a net loss of $100,000. The year-end balance sheet would show the balance of the Common Stock account to be:    A. $1,600,000.   B. $1,500,000.   […]

External link to Question : 141. Use the following information to answer questions 65-70. 1. $7,000 of merchandise : 1247106
Accounting

Question : 141. Use the following information to answer questions 65-70. 1. $7,000 of merchandise : 1247106

    141. Use the following information to answer questions 65-70.  1. $7,000 of merchandise inventory was ordered on September 2, 2009 2. $3,000 of this merchandise was received on September 5, 2009 3. On September 6, 2009, an invoice dated September 4, 2009, with terms of 3/10, net 30 for $3,250 which included a $250 prepaid freight cost, was received. 4. On September 10, 2009, […]

External link to Question : Objective 5.6 Answer the following questions using the information below: Gregory : 1217197
Accounting

Question : Objective 5.6 Answer the following questions using the information below: Gregory : 1217197

  Objective 5.6   Answer the following questions using the information below:   Gregory Enterprises has identified three cost pools to allocate overhead costs. The following estimates are provided for the coming year:   Cost PoolOverhead CostsCost driverActivity level Supervision of direct labor$320,000Direct labor-hours800,000 Machine maintenance$120,000Machine-hours960,000 Facility rent$200,000Square feet of area100,000 Total overhead costs$640,000   The accounting records show the Mossman Job consumed the following […]

External link to Question : 81. When compared to traditional approaches to labor-management relations, transformational approaches A. have : 1250933
Accounting

Question : 81. When compared to traditional approaches to labor-management relations, transformational approaches A. have : 1250933

    81. When compared to traditional approaches to labor-management relations, transformational approaches  A. have more grievances that go to the third or fourth steps. B. engage in less problem-solving activities. C. have more frequent conflicts. D. have lower costs. Compared with the traditional approach, transformational labor relations were found to be associated with lower costs, better product quality, and higher productivity.     82. Which of the following statements is […]

External link to Question : 6) How much cash will be paid to suppliers in : 1217227
Accounting

Question : 6) How much cash will be paid to suppliers in : 1217227

  6) How much cash will be paid to suppliers in March? A) $46,400 B) $56,000 C) $88,000 D) None of these answers are correct. 7) How much cash will be disbursed in total in March? A) $42,000 B) $50,000 C) $88,400 D) $96,400   8) What is the ending cash balance for March? A) ($50,000) B) $6,000 C) $5,600 D) $6,600 Answer the following […]

External link to Question : 61.During March a firm purchased $22,650 of merchandise and paid : 1220600
Accounting

Question : 61.During March a firm purchased $22,650 of merchandise and paid : 1220600

  61.During March a firm purchased $22,650 of merchandise and paid freight charges of $1,720. If the net delivered cost of purchases for the March is $21,900, what is the total purchase returns for March?   A.$0 B.$970 C.$2,470 D.$3,440 62.During the year, a firm purchased $256,200 of merchandise and paid freight charges of $41,720. If the total purchases returns and allowances were $15,790 and purchase […]

Place your order
(550 words)

Approximate price: $22

July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
July 1, 2023
    

Project 3 - Ensemble Methods and Unsupervised Learning

In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.

I. Choosing Datasets

The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).

II. Coding (10%)

After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.

1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.

2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.

3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).

4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.

Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.

III. Report (80%)

You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.

• A description of your two datasets and why you feel that they are interesting.

• Hypotheses on how you believe the learning algorithms will perform on each

dataset and why.

• How you dealt with different features in your datasets? missing data? different

scalings?

• Training and testing error rates you obtained for your various learning

algorithms (some sort of cross validation is highly recommended)

• The effect of hyperparameters on performance

• Comparing and contrasting results between datasets

• Comparing and contrasting results between learning algorithms

• Training and testing error rates as a function of training dataset size

• Timing analysis of how long it takes to train/test each algorithm

• Conclusions

• Ideas for future analyses

• What you may have done differently

• References

You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.

IV. Presentation (10%)

Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!

What to turn in?

Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.

• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.

• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.

• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.

• data.zip - A zip file that contains the two datasets you have chosen.

Grading

You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be

both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.

default image
Essay (any type)
August 22, 2022
    

They have a magnificent team. These people are always kind and willing to listen to your concerns or issues. Better yet, your assignment is always ready before the time, they usually send you a draft to double-check before they finalize your paper.

 

default image
Customer #32567
Zoology, 8 Pages
Case study
August 22, 2022
    

Completed quickly and followed instructions given. Grammar, spelling, etc. was all good as well. Thank you so much! Will hire in the future.

default image
Customer #30093
Biology (and other Life Sciences), 6 pages
Presentation/PPT
August 22, 2022
    

The work has not been graded but I like the output that was submitted to me. Is it possible for the same prof to do the next assignment I will be submitting? If possible, I will greatly appreciate it.

default image
Customer #37612
Computer science, 8 pages
Journal article
August 22, 2022
    

My third using this writer and absolutely amazing work! Very punctual and very understanding.

default image
Customer #34820
Agriculture, 4 pages
Article
August 22, 2022
    

Outstanding job in writing my paper. I am always thrill to receive a well written paper that follows the instructions and the thorough details put into my paper. The quality of this paper is beyond awesome. Thank you so much.

default image
Customer #17907
Technology, 5 pages
Thesis/Thesis chapter
August 22, 2022
    

I absolutely love this writer! He/she always cranks out a fabulous paper and follows the assignment guidelines/instructions perfectly. Thanks again for always providing such quality work!!!

default image
Customer #32146
Economics, 9 pages
Research paper
August 22, 2022
    

It took some time to achieve the product I was looking for, but we eventually got there. I do feel like a lot of hard work was put into this, and it even exceeded the page requirement. I'm pretty pleased overall. I give my thanks.

default image
Customer #22371
Ecology, 5 pages
Lab Report
August 22, 2022
    

Outstanding work! This writer followed every instruction and my paper came back with an A! I recommend to anyone needing help! Thank you again ❤️

default image
Customer #13712
Nursing, 6 pages
Outline
August 22, 2022
    

Outstanding job in writing my paper. I am always thrill to receive a well written paper that follows the instructions and the thorough details put into my paper. The quality of this paper is beyond awesome. Thank you so much.

default image
Customer #267302
IT, Web, 6 pages
Essay (any type)
August 22, 2022
    

Made 100% on both the outline and the paper. Quick turnaround time and my writer responded within 30 min or less. Any edits my instructor needed, my writer was patient and worked diligently to accomplish the task. Would recommend my writer to anyone!

default image
Customer #14472
International Trade, 8 pages
Essay (any type)
August 5, 2021
    

You are making my life so much easier! I am a single mom that works and goes to school. You guys are helping me with my projects so I can have more free time with my son.

default image
customer #13832
Tourism, 3 pages
Annotated Bibliography
August 5, 2021
    

I was sure it can fit in a single page but the next time I will add one more page because I am impressed with your professionalism and quality. Will use many more times till I graduate.

default image
customer #12008
Management, 7 pages
Essay (any type)
August 5, 2021
    

Simple registration and quality services. I just ordered one paper, and it was good enough to pass the exam. That is what I need the most. Good job.

default image
customer #16257
Communication, 4 pages
Research Proposal
August 5, 2021
    

I couldn’t finish my freaking essay, and I was very desperate. A friend of mine sent me a link, and that is how I found out about you guys. Thank you for professional tips and editing. Now, I understand how to write it properly.

default image
customer #17128
Law, 5 pages
Reflection
August 5, 2021
    

I’m so grateful to you since you managed to complete my term paper in one day! You are definitely the best company ever!

default image
customer #16528
Public Relations, 7 pages
Essay (any type)
August 5, 2021
    

I am sure on your ability to provide any type of paper I will order. I wish I could develop that skill your writers have to be able to have a flow of ideas and excellent sequencing of thoughts in writing.

default image
customer #19552
Medicine, 5 pages
Coursework
August 5, 2021
    

I ordered a thesis paper and was satisfied. All the guys were polite and highly professional, even when I wrote to them several times per day. Thanks for your passion!

default image
customer #20376
Nursing, 6 pages
Discussion Essay
August 5, 2021
    

I turned to the service twice, and my papers always were beyond the expectations. To tell the truth, I was ready to find some grammar errors or something like that. However, everything was neat and tidy.

default image
customer #18529
Marketing, 1 page
August 5, 2021

My friend recommended Doctor My Essay to me. It is convenient! Now we both turn to the service periodically coz wanna save some time and use it more effectively. I know that while I’m developing my startup, an expert is working on paper. Time management in work!

default image
Research Paper
August 5, 2021
    

I write all paper works by myself but have some problems with literacy. Actually, I have enormous problems because English is not my native language. However, with proofreading from Doctor My Essay, I can relax and don’t be ashamed of mistakes.

default image
customer #12924
Business, 7 pages
Argumentative Essay
August 5, 2021
    

Your ability to write concisely is a finding. The paper has a solid argument. It’s clear that the writer understood central claim about the topic. He also presented original ideas to consider.

default image
customer #19304
Management, 3 pages
Coursework
August 5, 2021
    

That paper got the highest grade in my class and the professor thinks I’m the smartest student. Lol.

default image
customer #12387
Accounting, 6 pages
Essay (any type)
August 5, 2021
    

I was convinced that paper writing services are the fiction for naive students. But when it came to my research paper that was to be composed in 72 hours, I had no other options. I made the right choice when I ordered it from you!

default image
customer #12924
Ethics, 4 pages
Business Plan
August 5, 2021
    

I was also satisfied with the service. Usually, I write everything by myself, but this time ( I have no idea how it happens!) I missed the deadline. My writer was very responsible, wrote the essay quickly, and kept the instructions. Nice to know that such experts exist!

default image
customer #15677
Marketing, 10 pages
Annotated Bibliography
August 5, 2021
    

It is really affordable! The prices start from 9$, which is an excellent proposal on the market. Recently I worked with another service that took much more money and provided lower quality papers.

default image
customer #11234
Sociology, 9 pages
Research proposal
August 5, 2021
    

I am absolutely satisfied. I am first timer here. Revision was made quickly, and the author met my expectations. Thanks a lot. Paper is phenomenal.

default image
customer #15327
Economics, 5 pages
Essay (any type)
August 5, 2021
    

Having heard a lot of negative reviews about such services, I was a bit skeptical. However, our cooperation with Doctor My Essay was without pitfalls. I made two comments about the final work, and they re-wrote some parts very promptly. I am satisfied and will turn to them in the future.

default image
customer #17823
Human Resource Management, 2 pages
Research paper
August 5, 2021
    

It was my first experience in online ordering, and I was a bit nervous. However, this was for nothing. I’ve got a high mark!

default image
customer #12372
Public Relations, 3 pages
Dissertation
August 5, 2021
    

I understand that buying essays is a doubtful thing. You don’t put effort, don’t represent your ideas… However, I don’t perceive Doctor My Essay as a standard service. They don’t sell papers. They share high expertise!

default image
customer #12376
Journal article, 20 pages
Coursework
August 5, 2021
    

According to my previous experience, this service is really the best. They support the client and have competitive writers. I give them a perfect score!

default image
customer #19266
Political Science, 6 pages
Admission Essay
August 5, 2021
    

Nice service! It is cool that you can use the calculator on the main page and understand the price policy. For me, it is a huge plus!

default image
customer #11437
Tourism, 1 page
Discussion Essay
August 5, 2021
    

Thanks to God I found this service! If I didn’t send my research to the teacher on time, he would eat me. I tell you! However, with doctor my essay service, I met the deadline, and I am still alive.

default image
customer #12692
Philosophy, 5 pages
Report
August 5, 2021
    

Your service is excellent! Convenient and not expensive. Thanks for that!

default image
customer #10923
Psychology, 4 pages
Essay (any type)
August 5, 2021
    

If you have problems with time management, just like me, you need to cooperate with Doctor My Essay. Once you registered and made the order, you understand how easier it makes your life! Everything is online: ordering, communication… You can control the process everywhere and be sure that an expert will provide a high-level paper.

default image
customer #17823
Business, 8 pages

Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2018 at 10:52 AM
Total price:
$26
The price is based on these factors:
Academic level
Number of pages
Urgency
Basic features
  • Free title page and bibliography
  • Unlimited revisions
  • Plagiarism-free guarantee
  • Money-back guarantee
  • 24/7 support
On-demand options
  • Writer’s samples
  • Part-by-part delivery
  • Overnight delivery
  • Copies of used sources
  • Expert Proofreading
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

Our guarantees

Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.

Money-back guarantee

You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.

Read more

Zero-plagiarism guarantee

Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.

Read more

Free-revision policy

Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.

Read more

Privacy policy

Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.

Read more

Fair-cooperation guarantee

By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.

Read more