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For this project we will attempt to use KMeans Clustering to cluster Universities into two groups, Private and Public.

It is very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm.

We use Kmeans algorithm under normal circumstances because we don't have labels. In this case we will use the labels to try to get an idea of how well the algorithm performed, but we won't usually do this for Kmeans, so the classification report and confusion matrix at the end of this project, don't truly make sense in a real world setting!

We will use a data frame with 777 observations on the following 18 variables.

• Private: A factor with levels No and Yes indicating private or public university
• Apps: Number of applications received
• Accept: Number of applications accepted
• Enroll: Number of new students enrolled
• Top10perc: Pct.new students from top 10% of H.S. class
• Top25perc: Pct.new students from top 25% of H.S. class
• F.Undergrad: Number of fulltime undergraduates
• P.Undergrad: Number of parttime undergraduates
• Outstate: Out-of-state tuition
• Room.Board: Room and board costs
• Books: Estimated book costs
• Personal: Estimated personal spending
• PhD: Pct. of faculty with Ph.D.’s
• Terminal: Pct. of faculty with terminal degree
• S.F.Ratio: Student/faculty ratio
• perc.alumni: Pct. alumni who donate
• Expend: Instructional expenditure per student
• Grad.Rate: Graduation rate