What is K-Nearest Neighbors?
K-Nearest Neighbors is an algorithm used in classification and regression problems; however, this example application uses the algorithm as a classifier. K-nn is a form supervised learning where classification outputs are class membership and is considered lazy learning. This object in question is assigned a class based on the classes of its neighbors. This can be modified by alternating the number of neighbors provided (K). In regression the outputs are the property values for the specified objects. The value is the average of the values of k nearest neighbors.
The goal here was to create…
“If Earth gets hit by an asteroid, it’s game over. It’s control-alt-delete for civilization” — Bill Nye the Science Guy
The inspiration for this project came from a Kaggle competition on predicting asteroid diameter size. You can find the link and more information here.
There has been some extensive research into predicting asteroid diameters due to the potential damage they pose if these objects are to impact Earth. Yet, the lack of sufficient data, inability to get near the actual asteroids, and no clear cut formula for predicting their size leaves the field of predicting asteroids’ size rudimentary at best.