Introduction: K-Nearest Neighbors (KNN) is a popular machine learning algorithm used for both classification and regression tasks. It belongs to the supervised learning category of machine learning algorithms, which means it requires labeled data to train the model. KNN is a non-parametric algorithm, meaning it does not make any assumptions about the underlying data distribution. In this blog post, we will explore the basics of K-Nearest Neighbors algorithm, its applications, and provide a Python example using scikit-learn. K-Nearest Neighbors Algorithm: The K-Nearest Neighbors algorithm is a simple yet powerful classification algorithm. The basic idea behind KNN is to classify a new data point by finding the k-nearest data points from the training set and assigning the class label based on the majority vote. The key hyperparameter in KNN is 'k', which represents the number of neighbors to consider when classifying a new data point. The optimal value of 'k' depends on t...