Discuss types of regression | Types of Regression with the help of suitable examples |  Regression with the help of suitable examples and diagram

Definition of Regression:

Regression is just a technique we use in data mining to find out the different prediction of the datasets of different surveys we choose. Like use to find out the cost of the specific product etc. it’s in the form of numeric values only 

Linear:

For predictive analysis, it's used. Linear regression is a method for modeling the relationship between criteria or scalar response and many predictors or explanatory factors using a linear approach. The conditional probability distribution of the response given the values of the predictors is the focus of linear regression. There is a risk of over fitting in linear regression. Y' = bX + A is the formula for linear regression.

Logistic:

When the dependent variable is dichotomous, this method is utilized. Logistic regression is a type of binomial regression that estimates the parameters of a logistic model. When dealing with data having two alternative criterions and the relationship between the criterions and the predictors, logistic regression is used. l = beta 0+beta 1x 1+beta 2x 2 is the equation for logistic regression.

Polynomial:

For curved data, is used. The least squares method is used to fit polynomial regression. Regression analysis is used to predict the value of a dependent variable y in relation to an independent variable x. l = beta 0+beta 0x 1+epsilon is the equation for polynomial regression.

Stepwise

is used to fit regression and predictive models together. It is carried out in an automated manner. The variable is added or deleted from the set of explanatory variables at each stage. Forward selection, backward elimination, and bidirectional elimination are three methods for stepwise regression. Stepwise regression is calculated using the formula b j.std = b j(s x * s y-1).

Ridge:

is a method for assessing data from multiple regression models. Least squares estimates are unbiased when multi collinearity occurs. Ridge regression reduces the standard errors by adding a degree of bias to the regression estimates. Beta = (XTX + lambda * I)-1XTy is the formula for ridge regression.

Lasso:

is a regression analysis technique that includes variable selection as well as regularization. Soft trees holding is used in Lasso regression. Only a subset of the specified covariates is used in the final model with Lasso regression. N-1sumN i=1f(x i, y I, alpha, beta) is the Lasso regression formula.

Regression examples and diagram :

If we conduct the analysis of any business which is selling products like vivo phone analysis. And we want to put it the advertainment the 5 year analysis of the vivo phone on phone model and its cost we pay on the advertisement

Here the table blow for the analysis of vivo phone product and its cost on advertisement:

year	Advertisement	Sales
Year1	50$	1150$
Year2	60$	2150$
Year3	70$	3250$
Year4	80$	1530$
Year5	90$	5430$