K-Nearest Neighbors (KNN) is a supervised machine learning algorithm primarily used for classification tasks. It operates by identifying the ‘k’ closest data points (neighbors) to a given input and classifying the input based on the majority class among those neighbors. This algorithm is particularly effective for both binary and multi-class classification problems, making it versatile for various applications, including economic forecasting and other domains where classification is required ²³.

One of the key advantages of KNN is its simplicity and ease of implementation. It does not require any specific assumptions about the data, allowing it to work with various types of datasets. However, KNN can be computationally intensive, especially as the size of the dataset increases, since it calculates the distance between the input and all other points in the dataset ¹³.

Additionally, KNN is suitable for multiclass classification, which means it can handle datasets with more than two labels effectively ³. However, it is recommended to use KNN for datasets with fewer than 50,000 samples to maintain efficiency ³.