Create a mini online catalog using visuals Writing Assignment Help

Please complete ALL THREE of the Professional Development exercises.

During rounds Charles encounters a rare condition he has never personally seen, and only vaguely remembers hearing about in nursing school. He takes a few moments to prepare himself by searching the Internet. That evening, he researches even further to treat, administer, and assess the patient safely. He searches clinical databases online and his own school textbooks. Most of the information seems consistent, yet some factors vary. Charles wants to provide the highest quality in patient safety.

• What should Charles do when he encounters information in two sources that is directly contradictory?
• Which resources are the most trusted?
• Which resources are the most accurate?
• What criteria should Charles use to identify credible resources to enhance his clinical practice?
• Practice extracting data from a database at the National Cancer Institute (NCI) State Cancer Profiles web site:
  • Compare data from at least 2 different states
  • Search for incidence by gender
  • Search for incidence by race/ethnicity
• Visit the National Guidelines Clearinghouse web site:
  • Type ‘nursing’ in the search box to limit the results to those that are within the boundaries of nursing practice
  • Choose one of the guidelines and describe how it was developed and summarize the practice recommendations
  • How are these recommendations similar to or different from those contained in your textbook or those currently practiced in your clinical setting?
  • Choose a clinical topic and design a search strategy for searching an online database such as CINAHL or MEDLINE. Reflect on your search strategy, for example, what search terms did you initially use, what new terms did you discover as you searched, how did you limit/refine your search?

Please submit one APA formatted paper between 1700 words, not including the title and reference page. The assignment should have a minimum of four scholarly sources, in addition to the textbook.

“Many people in the United States hold the view that the society is, in fundamental ways, open. They believe that individuals carve out their life paths by drawing on their personal stores of hard work, effort, and talent. All children are seen as having approximately equal life chances. Or, if childrens’ life chances appear to differ, this is seen as due to differences in raw talent, initiative, aspirations, and effort (Anyon, Social Structure and Daily Life).

However, “the promise of schooling providing equality of opportunities to compete for income and wealth is seriously compromised before the child even enters the classroom. Parents develop different forms of social and cultural capital which advantage or disadvantage their children in school and in the labor market” (Spring, 2012, AmericanEducation).

To illustrate, “Families from elite backgrounds tend to participate in institutions serving the elite, and families in poverty tend to be involved with institutions serving the poor” (Anyon, Social Structure and Daily Life).

Thus, “it is no surprise that schools in wealthy communities are better than those in poor communities, or that they better prepare their students for desirable jobs. It may be shocking, however, to learn how vast the differences in schools are – not so much in resources as in teaching, methods, and philosophies of education” (Anyon, From Social Class and the Hidden Curriculum of Work)

With respect to California, 2013 data shows some striking issues to consider:

• 2 million students in California
• 3 million English learners (ELs) nearly equal the combined numbers of ELs in the next four most populous states – Texas, New York, Florida, and Illinois – even through these our states together have roughly twice as many students as California
• More than half (53%) of students come from low-income families
• California schools have more students per staff than schools in the rest of the US, ranking last or close to the bottom.
• California spends $9,139 per pupil on education
• California ranked 51st nationally in students per teacher
• California ranked 51st nationally in students per guidance counselor
• California ranked 51st nationally in students per librarian

Writing Task: After reading the information above, and viewing the documentary: First to Worst, discuss the structural, legal/political, and social factors which have contributed to the cumulative impact on equality of opportunity in California schools. Be detailed and specific about the issues that has shaped California’s educational landscape over the decades.

Q1 Classification

Write a function classify to conduct a classification experiements as follows:

• Take the training and testing file name strings as inputs, e.g. classify(training_file, testing_file). I
• Classify text samples in training file using linear support vector machine as follows:

a. First apply grid search with 6-fold cross validation to find the best values for parameters min_df, stop_words, and C (penality parameter of SVM) that are used the modeling pipeline. Use f1-macro as the scoring metric to select the best parameter values. Potential values for these parameters are:

min_df’ : [1,2,5]

stop_words’ : [None,”english”]

C: [0.5,1,5]

b. Using the best parameter values, train a linear support vector machine classifier with all samples in news_train.csv

• Test the linear support vector classifier created in Step 2.b using the testing file. Compare f1-macro score you obtain from the test dataset with the f1-macro of the best model from grid search, and comment if the model is overfitted or not. Save your comment into a pdf file
• Your function “classify” t has no return. However, when this function is called, the best parameter values from grid search is printed and the testing precision, recall, and f1 score from Step 3 is printed.

Q2. How many samples are enough? Show the impact of sample size on classifier performance

Write a function “impact_of_sample_size” as follows:

Take the full file name path strings for training and test datasets as inputs, e.g.

impact_of_sample_size(train_file, test_file).

Starting with 300 samples from the training file, in each round you build a classifier with 300

more samples. i.e. in round 1, you use samples from 0:300, and in round 2, you use samples from

0:600, …, until you use all samples.

In each round, do the following:

• create tf-idf matrix using TfidfVectorizer with stop words removed
• train a classifier using multinomial Naive Bayes model
• train a classifier using linear support vector machine model
• for each classifier, test its performance using the testing file and collect the following metrics: macro precision, macro recall. Note, make sure you use the same model parameters for all iterations.

Draw a line chart (two lines, one for each classifier) show the relationship between sample size and precision. Similarly, plot another line chart to show the relationship between sample size and recall

Write your analysis on the following:

How sample size affects each classifier’s performance?

How many samples do you think would be needed for each model for good performance?

How is performance of SVM classifier compared with Naïve Bayes classifier, as the sample size increases?

There is no return for this function, but the charts should be plotted.

Q3. Sentiment Classification

You’ll need amazon_review_500.csv for this assignment. This csv file has two columns as follows. The label column provides polarity sentiment, either positive or negative

Write a function detect_sentiment() as follows:

Take the filename string as an input

Create a Multinomial Naive Bayes classifier with 5-fold cross validation as a benchmarking model.

The average testing f1 macro is about 70%.

Try your best to improve average testing f1 macro by 5% out of 5-fold cross validation. You can use any approach. Some possible directions:

Use different classification models, e.g. SVM

Tune model parameters

Add additional features, e.g. word POS or sentiment, phrases, negation words etc.

Combine more than one models (i.e. ensemble).

…

This function has no return. Print out benchmarking performance and the improved performance when it’s called.

Write a paragraph to describe your approach and explain why your approach would work.

Note, This question has no standard answer. Your objective is to improve the average testing f1 macro of 5-fold cross valuation by 5% by whateve means!

If you use additional resources, e.g. sentiment lexicon file, you need to submit these additional resource files

ITS 631 Paper #3 (Chapters 9 – 12)

Criteria:

Open Textbook and Resources

100 Points Possible

Individual Effort Only!

APA format is required.References should be listed immediately after the question that is being answered.Each question lists a minimum number of unique scholarly references; the textbook is considered one unique reference (per question) regardless of how many times it is used.All references should be from the years 2007 to present day.

Review the rubric that will be used to evaluate this paper. All work must be completed individually.

• Describe some factors that contribute to the capacity of an emergency room, as measured by the number of patients served per day.Use at least two unique references. Length: 4-5 paragraphs and minimum 2 references.
• What are the three steps in designing process layouts? Use at least two unique references. Length: 4-5 paragraphs and minimum 2 references.
• What are the characteristics of self-managed teams? Use at least two unique references. Length: 4-5 paragraphs and minimum 2 references.
• Describe advantages and disadvantages of periodic review systems. Use at least two unique references. Length: 4-5 paragraphs and minimum 2 references.

Course textbook: Reid, R.D., and Sanders, N. (2016). Operations management. 6th ed. Wiley. ISBN: 978- 1118952610