In this assignment, you will select one of the chapters (From the book- Modeling Techniques in Predictive Analytics- where each of the chapters represents a different business problem, dataset and R code to run a predictive model). Apply the CRISP-DM framework (this is the same PDF found in Module 1) to address the data mining scenario and dataset in a Chapter of Modeling Techniques in Predictive Analytics. You should address each of the steps of the CRISP-DM framework. See the grading rubric for a description of what is expected. This should be submitted as a double-spaced (approximately) 5-page document. Due end of Module 01 Requirements: 5 page What is the business problem being addressed in this chapter? What types of data are available for modeling? Are there additional data elements you believe would be valuable to add to the modeling dataset? What approach did the author use to address the problem? In your opinion is this the best approach to this dataset? Were any essential steps skipped? Would you also recommend trying a different approach for contrast and if so, which one? How was the model evaluated? Deployed? Due DateSunday, July 18, 202111:59 PMPoints Possible40 Purpose:In this assignment, you will run the R code associated with your assignment 1 project. You will work through the code and explain the data preparation and analysis. (The code and accompanying datasets are open source, downloadable from the book’s website at http://www.ftpress.com/miller)Directions:
First, create a GitHub repository for ANA 505 – Assignment 2. Create a ReadMe file to accompany your R code.
Download the R code files and the data (if applicable) from the textbook website. R code files have the extension of .R. The data files could have the extension of .csv or .txt. There may not be a data file to download if your code generates the data (i.e. simulation). You do not need to download the files that end in the extension of .py. These are python files.
Save these files in your GitHub repository (code, data, & ReadMe).
Then, using the comment symbol #, annotate the code associated with assignment 1 to explain how the data is prepared and analyzed.
If there are multiple code files, only annotate the main file. If you are unsure of which one to annotate, please ask your professor. YES, you will see code that you haven’t come across in your readings or videos. This will happen throughout your career no matter how many classes on R that you take. You need to learn the skills to find resources to assist you with coding. There is no way to learn every package and every function in R. 5. Save this annotated code in your GitHub repository. 6. Submit your GitHub repository link. 7. Be sure your repository is set to public or that you have given me access to view it Here is a sample of code and how I would comment:

Due End of Week 5, Module 03 First, create a GitHub repository for ANA 505 – Assignment 2. Create a ReadMe file to accompany your R code. Download the R code files and the data (if applicable) from the textbook website. R code files have the extension of .R. The data files could have the extension of .csv or .txt. There may not be a data file to download if your code generates the data (i.e. simulation). You do not need to download the files that end in the extension of .py. These are python files. Save these files in your GitHub repository (code, data, & ReadMe). Then, using the comment symbol #, annotate the code associated with assignment 1 to explain how the data is prepared and analyzed. First, create a GitHub repository for ANA 505 – Assignment 2. Create a ReadMe file to accompany your R code.
Download the R code files and the data (if applicable) from the textbook website. R code files have the extension of .R. The data files could have the extension of .csv or .txt. There may not be a data file to download if your code generates the data (i.e. simulation). You do not need to download the files that end in the extension of .py. These are python files.
Save these files in your GitHub repository (code, data, & ReadMe).
Then, using the comment symbol #, annotate the code associated with assignment 1 to explain how the data is prepared and analyzed.

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