Evolutional Study on KNN and K-means Algorithms

We are currently in a technological age when fresh innovations are making human job quick and easy. One of these subjects is machine learning, which enables a machine to carry out tasks in accordance with what it has learned and understood. Machine learning is being utilized to guide a lot of programmers online. These devices can recognize our traditions and preferences in the same way that people do. Machine learning is now applied in a wide range of industries, including finance, medicine, retail, social media, robotics, automation, and game apps. These various sectors employ various algorithms. The objective of the work is a comparison of the KNN and K-means algorithms. They differ from one another marginally, according to the evaluation research. K-means clustering is a tool for organizing data into groups without predefined categories, like sorting a mixed bag of items based on their similarities. Conversely, KNN (K-Nearest Neighbors) is a useful machine learning technique in which fresh instances are classified or predictions are made by the algorithm based on labeled examples. We have also implemented and compared the performance of these algorithms using a real-world dataset.