Category: Machine Learning

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
Revise and Evaluate Data Analysis Model
In this milestone, you will perform an evaluation of your data analytic model and revise your decision model as needed. For the revision, you can add in additional machine learning models or do feature engineering. You can also create confusion matrices and check for accuracy, precision, recall, or F-Measures. You can do sensitivity analyses, create ROC curves, check error rates and variable selection/feature selection. Please do see the image below for some other options for revisions:
Deliverable
For milestone 4, please ensure you have the following REQUIRED sections ONLY:
Final Research Question: Please state your final research question and describe how it evolved, if it changed, from your Milestone 1 version.
Your final research question should reflect the actual analysis you conducted and the actual insight or prediction you made. For example, if you actually ended up doing a classification task to predict the value of a response variable based on some other predictor variables, that’s what you should state as your precise, quantitative research question.
Model Revision: You should discuss how you revised your model(s) and perhaps how you narrowed down the scope of the project to something coherent and managable. In addition, you should give the details of your final machine learning model and present your final results.
Concomitantly, you should assess the robustness of your model, especially noting what makes it strong or what breaks it or the distribution of uncertainty in it. As such, you’ll likely want to utilize some variant of sensitivity analysis, depending on your particular model, to demonstrate true understanding of what the model’s doing rather than just a rote implementation thereof.
Final Results and Initial Version of Final Conclusion: You should show the initial validation of metrics and also decide upon any additional machine learning algorithms you might want to try. In addition, please ensure you answer the following questions:
What did you learn about the data?
How did you answer the questions?
How can you justify your answers?

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)

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
Revise and Evaluate Data Analysis Model
In this milestone, you will perform an evaluation of your data analytic model and revise your decision model as needed. For the revision, you can add in additional machine learning models or do feature engineering. You can also create confusion matrices and check for accuracy, precision, recall, or F-Measures. You can do sensitivity analyses, create ROC curves, check error rates and variable selection/feature selection. Please do see the image below for some other options for revisions:
Deliverable
For milestone 4, please ensure you have the following REQUIRED sections ONLY:
Final Research Question: Please state your final research question and describe how it evolved, if it changed, from your Milestone 1 version.
Your final research question should reflect the actual analysis you conducted and the actual insight or prediction you made. For example, if you actually ended up doing a classification task to predict the value of a response variable based on some other predictor variables, that’s what you should state as your precise, quantitative research question.
Model Revision: You should discuss how you revised your model(s) and perhaps how you narrowed down the scope of the project to something coherent and managable. In addition, you should give the details of your final machine learning model and present your final results.
Concomitantly, you should assess the robustness of your model, especially noting what makes it strong or what breaks it or the distribution of uncertainty in it. As such, you’ll likely want to utilize some variant of sensitivity analysis, depending on your particular model, to demonstrate true understanding of what the model’s doing rather than just a rote implementation thereof.
Final Results and Initial Version of Final Conclusion: You should show the initial validation of metrics and also decide upon any additional machine learning algorithms you might want to try. In addition, please ensure you answer the following questions:
What did you learn about the data?
How did you answer the questions?
How can you justify your answers?

These are questions for an Intro to Machine Learning course. Please make sure to follow the instructions/guidelines provided to complete the assignment! This is very important! Please make sure to provide an explanation for your answers! Please show all your work, step by step! Also, please make sure to give me the solutions as a pdf file. I have attached the assignment instructions in the hw4(2).pdf file. Additionally, I have attached all the necessary files such as the data, code, and utilities files needed to complete this assignment in the hw4_code.zip file. I have also attached the Latex solution template, which is labeled as hw4_clean.tex. Let me know if you have any questions or if there’s something that you find confusing about the instructions that you would like me to clear up with you!

These are questions for an Intro to Machine Learning course. Please make sure to follow the instructions/guidelines provided to complete the assignment! This is very important! Please make sure to provide an explanation for your answers! Please show all your work, step by step! Also, please make sure to give me the solutions as a pdf file. I have attached the assignment instructions in the hw4(2).pdf file. Additionally, I have attached all the necessary files such as the data, code, and utilities files needed to complete this assignment in the hw4_code.zip file. I have also attached the Latex solution template, which is labeled as hw4_clean.tex. Let me know if you have any questions or if there’s something that you find confusing about the instructions that you would like me to clear up with you!

These are questions for an Intro to Machine Learning course. Please make sure to follow the instructions/guidelines provided to complete the assignment! This is very important! Please make sure to provide an explanation for your answers! Please show all your work, step by step! Also, please make sure to give me the solutions as a pdf file. I have attached the assignment instructions in the hw4(2).pdf file. Additionally, I have attached all the necessary files such as the data, code, and utilities files needed to complete this assignment in the hw4_code.zip file. I have also attached the Latex solution template, which is labeled as hw4_clean.tex. Let me know if you have any questions or if there’s something that you find confusing about the instructions that you would like me to clear up with you!

These are questions for an Intro to Machine Learning course. Please make sure to follow the instructions/guidelines provided to complete the assignment! This is very important! Please make sure to provide an explanation for your answers! Please show all your work, step by step! Also, please make sure to give me the solutions as a pdf file. I have attached the assignment instructions in the hw4(2).pdf file. Additionally, I have attached all the necessary files such as the data, code, and utilities files needed to complete this assignment in the hw4_code.zip file. I have also attached the Latex solution template, which is labeled as hw4_clean.tex. Let me know if you have any questions or if there’s something that you find confusing about the instructions that you would like me to clear up with you!

https://github.com/OmkarNevse/Lung-Cancer-detectio… this is the code I want to run, train and use on my device. it has a description of that. I want to a-z instructions for running that on my device. I have provided my project proposal. and there is several python files i want detailed instruction on each files. and also your suggestion.

Please see the attached file for project description and format for the project. The project must be at least 7-8 pages.

There will be various topics the assignment is bases on regarding Excel Functions, providing instructions and/or activities to all.