Day #18 - Linear Regression , K Nearest Neighbours

Linear Regression

• Fitting straight line to set of data points
• Create line to predict new values based on previous observations
• Uses OLS (Ordinary Least Squares). Minimize squared error between each point and line
• Maximum likelihood estimation
• R squared - Fraction of total variation in Y
• 0 - R Squared - Terrible
• 1 - R Squared is good
• High R Squared good fit

Linear Regression (Ref - Link )

• ML Model to predict continuous variables based on set of features
• Used where target variable is continuous
• Minimize residuals of points from the line
• Find line of best fit
• y = mx + c
• Residual = sum (y-mx-c)^2
• Reduce residuals
• Assumptions in LR
• Linearity, Residuals Gaussian Distribution, Independence of errors, normal distribution

Updated May 28/ 2020

KNN

• Supervised Machine Learning Technique
• New Data point classify based on distance between existing points
• Choice of K - Small enough to pick neighbours
• Determine value of K based on trial tests
• K nearest neighbours on scatter plot and identify neighbours

Related Read
Recommendation Algo Analysis
Linear Regression
Linear Regression - Concept and Theory
Linear Regression Problem 1
Linear Regression Problem 2
Linear Regression Problem 3

Happy Learning!!!