Category: Machine Learning

The assignment is about the supervised learning algorithms and their implementa

The assignment is about the supervised learning algorithms and their implementation
• Use the attached notebook to solve the assignment.
• Submit your solution as a python file (.py) not as a notebook. Your submission will not be graded if you submit a notebook (i.e. .ipynb file). Follow the submission direction in the attached notebook to know how to convert a .ipynb file to .py file
The attached file is the file of the assignment as well as the topic slides you may need it for solving.
Using GPT or any AI tool are prohibited.

one page reading response on the book:
[Murphy, Book 1, Chapter 12.1-12.3]
You c

one page reading response on the book:
[Murphy, Book 1, Chapter 12.1-12.3]
You can summarize, or focus and explain the part(s) that you enjoyed reading more in detail. You’re allowed a maximum of one page. No outside resources. Use first person point of view.

I recently graduated in bachelor’s in computer science. i am looking for job as

I recently graduated in bachelor’s in computer science. i am looking for job as qa or java developer. I want the resume that focuses starting of my career as i have 0-3 years’ experience. I have studied the courses ,
software engineering, digital logic comp arch-lab, digital logic-comp architect, operating systems, info systems project mgmt., algorithm analysis & design, principles of data mining, data structures, robotics design & development, database management,
network & data communication, comp org & machine language, comp org & machine language, programming fundamentals ii, applied computational thinking, discrete mathematics, matrix methods, global perspectives in art,
prog fundamentals i-lab, programming fundamentals i, introduction to informatics, information security & ethics.

Dataset
Airbnb
Links to an external site.
is a company that provides an online

Dataset
Airbnb
Links to an external site.
is a company that provides an online marketplace for short-term rentals of homes and apartments. Much of the data from Airbnb’s website has been compiled and made publicly available on the website Inside Airbnb
Links to an external site.
. For this assignment, you will analyze a sample of the Airbnb listings from Washington, DC, scraped in July 2023. Each row in this dataset represents a single Airbnb listing. You can download the data dictionary here: Data Dictionary.xlsx
Download Data Dictionary.xlsx
.
Goal
Your assignment is to build a predictive model of the price of the listings included in this dataset, AirbnbListings.csv
Download AirbnbListings.csv
, deliver a report, and upload all project files as described below. In your report, be sure to support your responses. Please make sure I can transfer the codes to R, this is a team effort so I would need to be able to share my codes and make sure they open please. Please do the code first and send, before report. I need the codes in 24 hours please.
Key Requirements and Questions
Preprocess the data and prepare it for the running neural network. (30 points)
Train two different neural network models using the ‘neuralnet’ package and the ‘caret’ package (based on the ‘nnet’ method or another neural network package that caret supports). (30 points)
Compare the results of these models by their model evaluation metrics (RMSE, R-squared, and MAE). Which one is a better model, and why? (Hint: caret package has a function that calculates these three regression measures.) (15 points)
You previously ran two different regression models on the price of the listing (on the same dataset) as part of your Programming I final project. Revisit your findings and comment on the difference between your new models compared to what you ran before. Are the results comparable? What are the shortcomings and the advantages of each approach? Which approach or model is more reliable for prediction? Explain. (15)
Important notes:
Neural networks are extremely sensitive to the scale of the variables. Make sure all the variables, even the predictor, are scaled. The most common scaling for neural nets is min-max normalization.
The evaluation metrics of the test data should be calculated based on the actual scale of the target variable. So if predicted values are in a range of 0 to 1, they should be scaled back to the original scale before calculating predictive measures.
Your report should include plots of actual prices versus predicted prices for both of the two trained models.
The instructor and the TAs may not answer debugging questions regarding other neural network packages that are not discussed in class.
Deliverables
A written report answering the questions and explaining your findings (submitted as a PDF document). This report should clearly define the problem statement, data processing steps, your approach and any assumptions you make, the results of analyses you have performed, and the insights you have gained by performing these analyses. In writing it, imagine that you are a consultant submitting the report to your client. This report should not be more than 3 pages long (excluding the cover page or table of contents). If needed, you can have up to 2 pages of an appendix with supporting exhibits. Include your names on top of the first page, or add a cover page with the names.
Your fully-functional and annotated R code(s) with proper name(s). In your R file(s), highlight the different steps/questions by including annotations or section titles. Also, make sure your submitted code can be executed (with no errors) by just reading the AirbnbListings.csv file.
Rubric:
Your project will be graded based on:
Timeliness:
Submitting the complete assignment on or before the deadline. (faculty may not accept late submissions or penalize them in other ways.)
Analysis and Recommendations:
Clearly stating the scope and objectives.
Answering all study questions clearly and completely.
Demonstrating appropriate use of concepts and techniques and properly following machine learning training and testing steps.
Depth of the analysis.
Clarity and quality of the findings and recommendations.
To summarize:
Correctness of your approach, your code, answering the questions, and following the steps. (90 points)
Format of the report and your R code. (10 points)

Prior to beginning work on this discussion forum,
Review section 6.8 Networking

Prior to beginning work on this discussion forum,
Review section 6.8 Networking Careers of the TestOut IT Fundamentals Pro course.
Review the Career Resources at the University of Arizona Global Campus interactive in your online classroom.
Review IT Jobs: Career Options, Job Titles, and Descriptionsfor information on current IT jobs and for the various job titles used for each position.
Search for IT or computer networking jobs using one of the following websites:Indeed
Monster
Dice
TechCrunch
ZipRecruiter
If you are interested in a career in information technology (IT), you need to understand the different titles that may exist for that career, as well as what skills employers seek. This career-focused mindset should guide you even when you study or choose your courses. For this discussion forum, you will review current job opportunities and compile a list of skills that you believe will be helpful in landing a job in the field of IT or computer networking.
In your initial discussion forum post,
Indicate the IT or networking job titles or positions you searched for.
Discuss at least two important skills needed to acquire a job in the IT or networking field.
Explain why those two skills could benefit your professional goals.
List at least three IT or networking-related skills you already possess.
Be sure to indicate your level of proficiency with those skills: basic, intermediate, or advanced.

Analyzing brain tumors with deep learning for medical diagnosis: methods, challe

Analyzing brain tumors with deep learning for medical diagnosis: methods, challenges, and advancements overview.

Analyzing brain tumors with deep learning for medical diagnosis: methods, challe

Analyzing brain tumors with deep learning for medical diagnosis: methods, challenges, and advancements overview.

Using the TreePlan/SensIt chapters from the module resources as your guide, perf

Using the TreePlan/SensIt chapters from the module resources as your guide, perform a sensitivity analysis on the decision tree that you developed in the previous module’s assignment.
TYPOSIn item “L”, it should say:
Recall the value for p(YV) used in Problem Set 9. How was this value calculated? It was your X%, which we said was “How many properties are worth at least $150,000 and a market index of at least 1.1 in year 1 and then, of those, how many go on to be worth more than $200,000 with a market index of at least 1.2 in year 5?”
TIPSIf you get a slightly different root node error (like 79/140 instead of 74/140), that’s also okay. It depends on the version you have and the seed number used when you randomly split the data.
In regards to the add-in, you can also try alternate add-ins like these:
The trial version from here: https://www.add-ins.com/sensitivity-analyzer.htm
Alternate: http://mba.tuck.dartmouth.edu/toolkit/
Alternate: http://www.sensitivity-analysis.de
See attached references and “problem_set-11-model_diagnostics” document

Using the TreePlan/SensIt chapters from the module resources as your guide, perf

Using the TreePlan/SensIt chapters from the module resources as your guide, perform a sensitivity analysis on the decision tree that you developed in the previous module’s assignment.
TYPOSIn item “L”, it should say:
Recall the value for p(YV) used in Problem Set 9. How was this value calculated? It was your X%, which we said was “How many properties are worth at least $150,000 and a market index of at least 1.1 in year 1 and then, of those, how many go on to be worth more than $200,000 with a market index of at least 1.2 in year 5?”
TIPSIf you get a slightly different root node error (like 79/140 instead of 74/140), that’s also okay. It depends on the version you have and the seed number used when you randomly split the data.
In regards to the add-in, you can also try alternate add-ins like these:
The trial version from here: https://www.add-ins.com/sensitivity-analyzer.htm
Alternate: http://mba.tuck.dartmouth.edu/toolkit/
Alternate: http://www.sensitivity-analysis.de
See attached references and “problem_set-11-model_diagnostics” document

Please submit your Milestone Two: Introduction and Exploratory Data Analysis (ED

Please submit your Milestone Two: Introduction and Exploratory Data Analysis (EDA) assignment as a Word Document here.
Here is my Research Topic:
Predicting Bank Failures in USA
Here is my Research Question:
Which U.S. state is most likely to experience the greatest number of bank failures in the future, and what are the main factors that contribute to this likelihood?
Here is my Dataset
Dataset Name: FDIC Failed Bank List
FDIC Failed Bank List Dataset
Download the FDIC Failed Bank List (CSV)
Here is my chosen ML Method:
Classification machine learning method
Introduction and Exploratory Data Analysis (EDA)
In this milestone, you will write the introduction to your final paper. Specifically, the introduction should explain and discuss your research, including the final research question, dataset, and the chosen machine learning model. It should give the appropriate context of the analysis for your reader, and explicitly state your final research question as well as your hypothesis and the machine learning model you’ve selected. A good introduction not only gives background material on the machine learning method and dataset, but it also dives into how data analysis principles specifically apply to the situation: why did you decide on that particular machine learning model for this specific dataset and research question?
In particular, now that you have selected your dataset and formulated a research question, you should explore and characterize the dataset by doing an Exploratory Data Analysis. This data appraisal will inform your introduction, which will provide the context for your data analysis. Finally, it should consider the metrics you will utilize to evaluate the results of your analysis.
Deliverable
For milestone 2, please ensure you have the following REQUIRED sections ONLY:

Introduction: Describe the purpose, type, intended populations, and uses of the analysis report to establish an appropriate context for the data analysis plan. Appraise the data within the context of the problem to be solved and industry standards. How will you use the data? For example, expound upon the limitations of the data set in the context of your needs.
Please do especially ensure you clearly state your final, possibly revised, research question in a separate sub-section
Exploratory Data Analysis: Characterize the data set. For example, what is the purpose such data are generally used for? You should do the data preparation for analysis and compute and present the summary statistics and the Exploratory Data Analysis (EDA) for the dataset.
Measurable Metrics and ML Methods/Utilities: Explain the ML methods/utilities that you will be using and how the data supports that choice. The utilities, in this case, are the different machine learning algorithms you are considering, along with any particular software implementations you are thinking of using for it. You should also think of measurable metrics to evaluate the results of your analysis, as well.
I’ve attached the sample “template-milestone_2” and the previously submitted Milestone-2 where I was partially graded. Please take a look at the template and previously submitted doc and re-write the entire ask.
Instructor’s Comments:

Please ensure your RQ has the explicit independent and dependent variables and, if possible, also mentions the ML approach. In addition. Your EDA is lacking the actual EDA and the metrics should be for the specific chosen ML algorithms only.
Please make sure that the revised document include all the above as stated by professor. I am also attaching the Milestones that I’ve submitted after this (Milestone3 and 4) for your reference.
Please answer/re-write Milestone-2 without any overlapping from Milestones 3 and 4.