This assignment asks you to examine the k$-$NN and Logistic algorithms for classification. Provide your answers to the questions in a Word document named Assign$1-1\_$LastName.doc along with your source code saved as Assign$1-1\_$LastName. If you plan to use R$-$Markdown, please save it as Assign$1-1\_$LastName. Then click the Assignment $1-1$ link above, upload the files, and submit.

Use the attached $$Wisconsin Breast Cancer Data Set,$($wisc$\_$bc$\_$data.csv$)$ which originates from UCI Machine Learning Repository, to answer the following questions:

Question $1$: Explore and prepare data by using the str$()$ function. Display the probability of the attributes $($benign$$ and $$malignant$)$ of the variable named $$diagnosis$$ that we plan to use for prediction. $(20$ pts$)$

I$$ve provided this video: Assignment $1$ for the prepressing of data for the prediction, which you can take a look at before answering the next question.

Question $2$: Create datasets for training and testing the model, and develop the model using the knn classifier algorithm. Evaluate the model with different k$,$ and propose the best value of k$.$

Split the dataset into training and testing. The proportions of training and testing dataset will be $7$:$3.(20$ pts$)$

Develop the model using the knn classifier algorithm. $(20$ pts$)$

Evaluate the model's performance for different K$,$ and suggest the best model. $(20$ pts$)$

Question $3$: Using logistic regression, develop the diagnostic machine learning algorithm to assist the medical team in determining whether the tumor is malignant or not. Provide a detailed explanation of the output. $(20$ pts$)$

Tip$)$ When building a logistic regression model, you need to recode the dependent variable $(\text{'}$diagnosis$\text{'})$ in this dataset. To do so$,$ you can use recode$()$ in the 'car' package to recode values. Here is an example of using recode$()$ for this assignment.

yourData$diagnosis$<-$recode$($BCL$diagnosis, $"\text{'}$M$\text{'}=1")$

yourData$diagnosis$<-$recode$($BCL$diagnosis, $"\text{'}$B$\text{'}=0")$

wisc$\_$bc$\_$data.csv