Hello, I need help with a machine learning project using R Studion and in .RMD file.

The project is due on November 30th, and I have attached a sample example of the project who did 1st, 2nd place, and still a good example of the project, which means that it is almost perfect expectation so you can get some hint or understanding of it; thank you.

The final project is intended to showcase your analytical abilities and model-building skills on your chosen dataset.

And you have to bring the dataset from Kaggle (https://www.kaggle.com/); my previous tutor, who I met at the StudyPool, copied the code from Kaggle that another person did, and I have reported, and I got the money refunded. I don’t want to meet a tutor who is irresponsible and plagiarism; thank you.

PROJECT SUBMISSION CONTENTS(USING R LANGUAGE) DUE NOVEMBER 29th

You should submit a text file with a link to a public GitHub repository that contains the following:

– an .Rmd (R Markdown) file containing your project in the form of a written report

– the knitted .html or .pdf file containing your project

– any .R files (R scripts) containing work on your project. The degree of organization of these can vary, but should at least have meaningful file titles, like “eda.R” or “missing_data_analyses.R,” etc.

– any raw data files. Exceptions can be made. For instance, if your data files are huge in terms of megabytes, you don’t have to submit them. If your data is proprietary or confidential, you don’t have to submit it.

– a codebook. This should take the form of a document (either .doc, .html, .pdf, or .txt) that, at minimum, identifies and defines each column in your final data set. If a variable takes on different values (for example, 1 = “single,” 2 = “married,” etc.), those values should be defined in the codebook.

Layout example: <Attatched>

REPORT CONTENTS

Your final project report should be written similarly to a paper, with figures, code, and results included throughout to illustrate your points and findings. Text should be included to guide the reader. I recommend reading through the example report to get an idea of this layout. More specifically, your report should contain:

– An introduction section: Describes the data, the research questions, provides any background readers need to understand your project, etc.

– A conclusion section: Discusses the outcome(s) of models you fit. Which models performed well, which performed poorly? Were you surprised by model performance? Next steps? General conclusions?

– A table of contents

– A section for exploratory data analysis: This should contain at least 3 to 5 visualizations and/or tables and their interpretation/discussion. At minimum your group should create a univariate visualization of the outcome(s), a bi-variate or multivariate visualization of the relationship(s) between the outcome and select predictors, etc. Part of an EDA involves asking questions about your data and exploring your data to find the answers.

– A section discussing data splitting and cross-validation: Describe your process of splitting data into training, test, and/or validation sets. Describe the process of cross-validation.

– A section discussing model fitting: Describe the types of models you fit, their parameter values, and the results.

– Model selection and performance: A table and/or graph describing the performance of your best-fitting model on testing data. Describe your best-fitting model however you choose, and the quality of its predictions, etc.