This document discusses K-Nearest Neighbors (KNN) classification and linear regression. KNN is a non-linear classification method that predicts the class of a test data point based on the classes of its k nearest neighbors, where k is usually a small odd number like 1 or 3. Linear regression finds the linear relationship between a dependent variable Y and independent variable X by fitting data to a linear equation Y=b0+b1X to make predictions.

1. Ateeq ur Rehman

2.  It is used for classification of data based on
nearest neighbour
 Prediction for test data is done K is an integer
(small), k=1 assign to the class of single nearest
neighbour
 The nearest neighbour is calculated based on
the distance

4. KNN LINEAR REGRESSION
 Non-linear
 Euclidean distance
 Linear
 Linear model
 Y=b0+b1 X

5.  K=1 make noise
 K=large not good(lost
of pattern)
 Equal distance
 K always be odd