# Predicting and visualizing user-defined data point with K-Nearest Neighbors

**R-posts.com**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

K-nearest neighbors is easy to understand supervised learning method which is often used to solve classification problems. The algorithm assumes that similar objects are closer to each other.

To understand it better and keeping it simple, explore this **shiny app**, where user can define data point, no. of neighbors and predict the outcome. Data used here is popular Iris dataset and ggplot2 is used for visualizing existing data and user defined data point. Scatter-plot and table are updated for each new observation.

Data is more interesting in overlapping region between versicolor and virginica. Especially at K=2 error is more vivid, as same data point is classified into different categories. In the overlapping region, if there is a tie between different categories outcome is decided randomly.

One can also see accuracy with 95% Confidence Interval for different values K neighbors with 80/20 training-validation data . While classification is done with ‘class’ package, accuracy is calculated with ‘caret’ library.

**leave a comment**for the author, please follow the link and comment on their blog:

**R-posts.com**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.