K Nearest Neighbor (KNN) In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors. What is KNN? Machine learning models use a set of input values to predict output values. KNN is one of the simplest forms of machine learning algorithms mostly used for classification. It classifies the data point on how its neighbor is classified Introduction: K Nearest Neighbor algorithm falls under the Supervised Learning category and is used for classification (most commonly) and regression.  It is a versatile algorithm also used for imputing missing values and resampling datasets. As the name (K Nearest Neighbor) suggests it considers K Nearest Neighbors (Data points) to predict the class or continuous value for the new Datapoint. How to choose the value for K?¶ Using error curves: The figure below shows error curves for different values of K for training and test data. At low K values, there is overfitting of data...