Handle missing data - Missing data can be addressed in two main ways: 1. Delete a sample (Row) or column (Feature) with missing data. 2. Use interpolation to predict missing data. - Average, median, frequency, constant, etc. are used. Let's use the code to check. 0. Create a data frame with missing data Calling up required libraries import pandas as pd # When it used to make DataFrame import num..

KNN KNN belongs to a typical 'Lazy Learner'. That is, instead of learning the discriminant function from the training data, we proceed with the learning by storing the training dataset in memory. Thanks to this, there is no cost in the learning process. Instead, the computational cost in the prediction phase is high. Memory-based classifiers have the advantage of being able to adapt immediately ..