Follow the steps given in Machine Learning With R, Chapter 3 section “Diagnosing Breast Cancer with the kNN Algorithm.”
***Utilize the Wisconsin Breast Cancer Diagnostic dataset from the UCI Machine Learning Repository at http://archive.ics.uci.edu/ml. This data was donated by researchers of the University of Wisconsin and includes the measurements from digitized images of fine-needle aspirate of a breast mass.***
Follow the steps given in Machine Learning With R, Chapter 9 section “Finding Teen Market Segments Using k-Means Clustering.”
***This dataset was compiled by Brett Lantz while conducting sociological research on the teenage identities at the University of Notre Dame. If you use the data for research purposes, please cite this book chapter. The full dataset is available at the Packt Publishing website with the filename snsdata.csv. ***
Prepare a brief that reports the execution steps and outcomes of the k-NN and k-means lab. In addition, the following questions will be addressed in the brief:
Use the caret package to automatically tune the k parameter for the k-NN algorithm. Were you able to identify a k parameter that increased the accuracy from previous attempts? Show your work and the final result.
Train the k-means model again using k=3 and then k=10. How did this affect the cluster distribution for mean age and proportion of females?
Specifically, the following critical elements must be addressed:
Description of dataset in use
Description of purpose of R commands used
Demonstration of R command execution
Guidelines for Submission: Each report submission should be one to two pages in length. The report should briefly describe the dataset in use and the purpose of the R commands used. The report should also include screenshots demonstrating successful completion of R command steps listed for each lab assignment. Reports should follow these formatting guidelines: double-spacing, 12-point Times New Roman font, one-inch margins, and citations, if any, in APA format when appropriate.
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