Here you’ll find everything about k-nearest neighbor (k-NN), including tutorials on implementing the algorithm in Python.
The k-nearest neighbor algorithm is a method for classifying data points based on their similarity to other data points. It is a non-parametric, lazy learning algorithm that is used for both classification and regression. In k-NN, a data point is assigned to the class that is most common among its k nearest neighbors. The number of neighbors is a positive integer (k) that is specified by the user. The algorithm determines the neighbors by using a distance metric, such as Euclidean distance, that measures the similarity between data points. k-NN is known for its simplicity and versatility, but it can be computationally expensive. It also may not work well on high-dimensional data. Application areas include image recognition, speech recognition, and gene expression analysis.
Credit card fraud has become one of the most common use cases for anomaly detection systems. The number of fraud attempts has risen sharply, resulting in billions of dollars in losses. Early detection of fraud attempts with machine learning is therefore becoming increasingly important. In this article, we take on … Read more