KNN algorithm in Python

Description

The k-nearest neighbors (KNN) algorithm can be used to classify animals based on their characteristics or features. Here's how it works:

Data collection:

Collect a dataset of animals, including information such as their size, weight, and other relevant features.

Data preparation:

Prepare the data for use in the KNN algorithm by converting it into a numerical format and scaling the features to a common range.

Training and testing:

Split the data into a training set and a testing set, and use the training set to train the KNN algorithm.

Classification:

Given a new animal, the KNN algorithm identifies the k closest animals in the training set based on their features, and classifies the new animal based on the majority class among those k nearest neighbors.

Evaluation:

Evaluate the performance of the KNN algorithm on the testing set, and use the results to determine the optimal value for k and adjust the algorithm as needed.