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Regression is a form of predictive modeling technique to determine the strength of the relationship between a dependent and independent variable. One of these variables is called a predictor variable whose value is gathered through experiments. The other variable is called the response variable whose value is derived from the predictor variable.

Y=aX+b

Linear regression (X is predictor variable and Y is response variable)
Regression is used for the following:-
-It is used to derive a significant relationship between variables
-Regression analysis also allows us to compare the effects of variables measured on different scales, such as the effect of price changes and the number of promotional activities.
Basically there are two types of variables in a regression equation. The one whose value is found from experiments is called the predictor variable. The one whose value is to be calculated is the response variable. In linear regression, the strength of relationship between response variable and predictor variable is calculated.
Y=ax+b
Y is response variable
X= Predictor variable
A and b are constants
Multiple regression is an extension of linear regression into the relationship between more than two variables. In simple linear relation, we have one predictor and one response variable, but in multiple regression, we have more than one predictor variable and one response variable.

Y=a1x1+a2x2+..+b
Sometime a column can have an only Boolean variable in it(True/False or 0/1) etc. and you need to know the relationship between the response variable(Boolean value) with predictor variable. The Logistic Regression is a regression model in which the response variable (dependent variable) has categorical values such as True/False or 0/1. It actually measures the probability of a binary response as the value of the response variable based on the mathematical equation relating it with the predictor variables.

  y = 1/(1+e^-(a+b1x1+b2x2+b3x3+...)


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