Dear Student:

In order to give you an opportunity to self-assess your skills and improve your grade, you can create a tri-fold brochure related to measures we need to take to protect against COVID- 19. Please follow the instructions bellow:

1. Visit (LA County Public Health print materials)

2. Find a tri-fold brochure template in Microsoft Word.

3. Use the information from the site to create your brochure. Remember you will need to create the front and the back of the brocure

4. Proofread, save, and submit here.

Use Excel to develop a worksheet showing the monthly payment of a real estate loan as the amortization table for it showing zero balance at the end of its term (place – before the loan/principal amount in the function. You will need to show 360 pay period in your amortization table:

Loan Amount: $ 500,000
Down Payment: 20%
Loan Amount: to be calculated
Annual Interest Rate: 6%
Term of the loan: 30 years fixed- loan will be fully paid off at the end of the term.
Monthly Payment: To be calculated
Amortization Table: to be created
Useful Functions: PMT, IPMT, PPMT
Please submit the Excel file (do not zip)

import java.util.Scanner;

public class EvenOdd {

public static void main(String[] args) {

Scanner sc = new Scanner(System.in);

System.out.print(“Enter a number: “);

int num = sc.nextInt();

if(num % 2 == 0) {

System.out.println(num + ” is Even”);

} else {

System.out.println(num + ” is Odd”);

}

sc.close();

}

}

Unit 2 – Discussion Board 1 (75 points)

Due: Thu, May 14 |

Description

Primary Response is due by Thursday (11:59:59pm Central), Peer Responses are due by Saturday (11:59:59pm Central).

Primary Task Response: Within the Discussion Board area, write 400-600 words that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. Be substantive and clear, and use examples to reinforce your ideas.

The ability to evaluate structured and unstructured data will provide an organization with a competitive edge. Using the Internet, journals, or peer-reviewed articles, select 1 organization that has adopted the use of big data analytics, and assess how it has given this organization an advantage in its market.

Responses to Other Students:

Respond to at least 2 of your fellow classmates with at least a 200-word reply about their Primary Task Response regarding items you found to be compelling and enlightening. To help you with your discussion, please consider the following questions:

• How would you justify a different perspective from your classmates on the topic?
• How, additionally, would you defend your classmates position?
• What critique do you offer your classmate in regard to clarity and thoroughness of their post?
• Please address all prompts. When offering counterargument or justification, consider practice, theory, and examples from your own experience, reading, or current events in presenting your position.

For assistance with your assignment, please use your textbook, all course resources, and any external research and resources you have gathered.

Discussion Board Rubric

Expectation	Points Possible	Points Earned	Comments
Application of Learning Material Content: Initial post demonstrates understanding of Learning Material content.	25
Application of Course Knowledge: Initial post contributes unique perspectives or insights gleaned from text/learning resources, or specified by assignment.	25
DB Responses: Responds substantively to two posts. Responses encourage interaction in the Discussion Board and classroom community.	10
Academic Writing: Initial post presents information logically and is clearly relevant to discussion topic. Posts contain accurate grammar, spelling, and/or punctuation with few or no errors. All resources should be cited in current APA format.	15
Total Points	75
Total Points Earned

Assignment Details

Unit 2 – Individual Project (125 points)

Due: Sun, May 17 |

Description

Assignment Details:

You are a data analyst working for a major bank. Over the past 10 years, the bank has invested a considerable amount of money in the use of big data analytics. The bank is considering ceasing financial support of this program. In a 4-6-page report, evaluate the impact that big data analytics has had on the banking industry and why it is important to continue investing into this program. Key areas to consider are the following:

• Fraud detection
• Marketing
• Credit risk management
• Understanding Customer Behavior
• Large Language Models, such as Financial Advice or BloombergGPT

Please submit your assignment.

For assistance with your assignment, please use your textbook, all course resources, and any external research and resources you have gathered.

Individual Project Rubric

The Individual Project (IP) Grading Rubric is a scoring tool that represents the performance expectations for the IP. This Individual Project Grading Rubric is divided into components that provide a clear description of what should be included within each component of the IP. Its the roadmap that can help you in the development of your IP.

Expectation	Points Possible	Points Earned	Comments
Assignment-Specific: Evaluates the impact that big data analytics has had on fraud detection in the banking industry and why it is important to continue investing into this program	35
Assignment-Specific: Evaluates the impact that big data analytics has had on marketing in the banking industry and why it is important to continue investing into this program	35
Assignment-Specific: Evaluates the impact that big data analytics has had on credit risk management in the banking industry and why it is important to continue investing into this program	35
Assignment-Specific: Demonstrates the utilization of the course readings and other scholarly or professional materials to complete the assignment	10
Professional Language: Contains accurate grammar, spelling, and punctuation with few or no errors (APA formatting or the style specified in the assignment is required.)	10
Total Points	125
Total Points Earned

Solve it as required inuploaded fil without plagiarism

Solve it as required inuploaded fil without plagiarism

Solve it as required inuploaded fil without plagiarism

Solve it as required inuploaded fil without plagiarism in word file

Use your laptop/computer and complete In-Class Exercises. In-Class Exercises are

clearly outlined in the weekly PowerPoint Presentation.

Data File: Salaries. xls

Week 2 Problem Solving Assignment: Answer the following questions. To get full credit for

your work, your responses to each section must be clearly marked and explained. Where needed,

show your work with a brief explanation for each part of the question (Due in GAP Sunday by

11:59 p.m.) (Rubric).

Q1- In 2017, the Restaurant Hospitality website reported that only 11% of surplus food is being

recovered in the food service and restaurant sector, leaving approximately 1.5 billion meals per

year uneaten. Assume that this is the true population proportion and that you plan to take a

sample survey of 525 companies in the food service and restaurant sector to further investigate

their behavior. You suspect that restaurants are doing a far better job in recovering surplus food

than what has been reported by the website.

Suppose that based on the sample you collected restaurants report an average of 59 surplus

meals per day of which an equivalent of 51 meals is thrown away. Respond to the following

questions:

a. Set up the research question and hypotheses.

b. Calculate the p-value using the Norm.dist Excel command.

c. What is your conclusion?

Q2- A computer manufacturer needs 7,500 units of a component to complete an order for a

distributor. If done in-house, the fixed cost would be$325,000 with variable cost at $25 per unit.

Alternative two is to outsource for a total cost of $75 per unit with a set-up/tooling cost of

$50,000 (fixed cost). The selling price for the item is $110. Answer the following questions:

a. Determine the breakeven quantity for each alternative.

b. Determine the breakeven quantity between the two alternatives. In other words, at what level

of production the company would be indifferent between outsourcing the order and in-house

production.

c. For the current order, should they make the item in-house or outsource it?

d. At what order quantity would it become optimal to make the item in-house rather than

outsourcing it?

COSC 3337 – Data Science I

Clustering

The goal of this assignment is to:

1. Learn to use popular clustering algorithms, K-means and DBSCAN.
2. Learn how to interpret and summarize results of clustering.
3. Learn to write evaluation functions to better understand clustering results.
4. Learn to use cross-validation techniques to assess model performance.

Dataset – Patient Clinical Records

You are given a dataset of patient clinical records. The dataset contains 300 records, each with 13 attributes. The attributes are as follows:

Age: Age of the patient

Anaemia: Whether the patient has anaemia (decrease in hemoglobin) (0 = no, 1 = yes)

Creatinine Phosphokinase: Level of the CPK enzyme in the blood (mcg/L)

Diabetes: Whether the patient has diabetes (0 = no, 1 = yes)

Ejection Fraction: Percentage of blood leaving the heart at each contraction (percentage)

High Blood Pressure: Whether the patient has hypertension (0 = no, 1 = yes)

Platelets: Platelets in the blood (kiloplatelets/mL)

Serum Creatinine: Level of serum creatinine in the blood (mg/dL)

Serum Sodium: Level of serum sodium in the blook (mEq/L)

Sex: Whether the patient is male of female (0 = female, 1 = male)

Smoking: Whether the patient smokes or not (0 = no, 1 = yes)

Time: Follow-up period (days)

Death Event: Whether the patient died during the follow-up period (0 = no, 1 = yes)

Assignment Tasks

The last attribute, Death Event, is the class label. The goal of this assignment is to cluster the patients into two groups: those who died during the follow-up period and those who did not. This attribute is to be ignored and you will use the other 12 attributes to cluster the patients.

The class label is to be used in the post analysis of the clusters generated by running K-means and DBSCAN. In addition, ignore the Time attribute as well when clustering.

Task 1 [10 points]

Write a function purity(y_true,y_pred) that computes the purity of a clustering result based on the class labels of the data points. The function takes two arguments: a list of class labels, ytrue, and a list of cluster labels, ypred. The function returns a single number, the purity of the clustering result. The purity is defined as the number of data points that were assigned to the correct class label divided by the total number of data points. For example, if there are 1000 data points and 800 of them were assigned to the correct class label, then the purity is 0.8. The purity is a number between 0 and 1, with 1 being the best possible purity score. Use the starting code given in the provided clustering.py. There should only be 1 – 2 more lines of code you need to write

Task 2 [10 points]

Run K-means on the dataset with k=2. Use the default parameters for the algorithm. Compute the purity of the clustering result. Compute the purity of the clustering result for each of the two clusters. Which cluster has the highest purity? What percentage of the data points were assigned to this cluster? What percentage of the data points were assigned to the other cluster?

Task 3 [10 points]

Run K-means on the dataset with k=3 and k = 5. Compute the overall purity of clustering and the purity of each cluster for each value of k. Which value of k gives the best clustering result? Explain why.

Task 4 [10 points]

Run DBSCAN on the dataset with minPts=5 and eps=0.5. Compute the purity of the clustering result. Compute the purity of the clustering result for each of the two clusters. Which cluster has the highest purity? What percentage of the data points were assigned to this cluster? What percentage of the data points were assigned to the other cluster?

Task 5 [10 points]

Develop a search procedure to find the best parameters for DBSCAN. The parameters to search over are minPts and eps. The procedure should maximize the purity of the clustering result, subject to the following constraints:

1. There should be between 2 and 18 clusters.
2. The percentage of outliers should be less than 10%.

Which parameters give the best clustering result? What is the purity of the clustering result? What is the purity of the clustering result for each of the clusters? Which cluster has the highest purity?

Hint: Consider if you want to do a grid search or search random combinations of minPts and eps.

Deliverables

1. A report that contains the results of your analysis for each of the tasks. The report should be a pdf file or a jupyter notebook
2. The code for all of the tasks within the provided shell file clustering.py or a jupyter notebook. If submitting a Jupyter notebook, you can use the same for both code and analysis. Submit all the code in a single file for this assignment
3. The code should be well documented and easy to follow. The code should generate all the plots required for the report.

Submission Instructions

Please submit on canvas. Please ensure that your Github username, your full name, and PSID are filled in at the start of the file.

Data:

Code stub: