Additional uses of PCA and wrap-up

Unsupervised Learning in R

Hank Roark

Senior Data Scientist at Boeing

Dimensionality reduction

dimensionality reduction

Data visualization

data visualization

Interpreting PCA results

interpreting PCA results - biplot and screeplot

Importance of data scaling

comparing feature importance before and after scaling

Up next

# URL to cancer dataset hosted on DataCamp servers
url <- "https://assets.datacamp.com/production/course_1903/datasets/WisconsinCancer.csv"

# Download the data: wisc.df
wisc.df <- read.csv(url)
wisc.data[1:6, 1:5]

         radius_mean texture_mean perimeter_mean area_mean smoothness_mean
842302         17.99        10.38         122.80    1001.0         0.11840
842517         20.57        17.77         132.90    1326.0         0.08474
84300903       19.69        21.25         130.00    1203.0         0.10960
84348301       11.42        20.38          77.58     386.1         0.14250
84358402       20.29        14.34         135.10    1297.0         0.10030
843786         12.45        15.70          82.57     477.1         0.12780

Let's practice!

Unsupervised Learning in R