5 Steps to Build a KNN Classifier

The k-nearest neighbor algorithm is applied to different classification and regression problems. The closest k training samples are used to predict the class of new input data, i.e., the most similar samples already known are used to classify an unknown data sample. Since the sci-kit library provides all the necessary tools to work on this algorithm, you can use these 5 steps to build your own KNN classifier in Python! As usual, start with importing all necessary libraries needed. This command builds an easy to handle data frame and decreases the complexity of working on the data set.