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LOGISTIC REGRESSION

 INTRODUCTION: It is a statistic method that is used for building machine learning models where the dependent variable is binary. It is used to describe data and the relationship between one dependent variable and one or more independent variables. The name "logistic regression" is derived from the concept of the logistic function that is uses. The logistic function is also known as the sigmoid function. The value of this logistic function lies between zero(0) and one(1). It is Supervised Classification Algorithm. The value of the logistic regression must be between 0 and 1, which cannot go beyond this limit, so it forms a curve like the "S" form. The S-form curve is called the Sigmoid function or the logistic function.   LOGISTIC REGRESSION EQUATION: The Logistic regression equation can be obtained from the Linear Regression equation. The mathematical steps to get Logistic Regression equations are given below: The equation of the straight line can be written as:   ...
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LINEAR REGRESSION

            

INTRODUCTION:

                 Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent variable and dependent variable (finds the linear relationship between the independent and dependent variable).

        Linear Regression Analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.


TYPES OF LINEAR REGRESSION:

                 1. Simple Linear Regression: Simple Linear Regression is where only one independent variable is present and the model has to find the linear relationship of it with the dependent variable. Equation for Simple Linear Regression-

                                                  y = a + bx

                       2. Multiple Linear Regression: In Multiple Linear Regression, there are more than one independent variables for the model to find the relationship. Equation of Multiple Linear Regression-

                                                 y = a + b1x1 + b2x2 +....+ bnxn



**A LINEAR REGRESSION MODEL'S MAIN AIM IS TO FIND THE BEST FIT LINEAR LINE AND THE OPTIMAL VALUES OF INTERCEPT AND COEFFICIENTS SUCH THAT THE ERROR IS MINIMIZED.**

               Error is the difference between the actual value and predicted value. The goal is to reduce this difference.

                   Error = Actual Value - Predicted Value

                   Sum of Residuals = Sum (Actual Value - Predicted Value)

                   Square of Sum of Residuals = (Sum [Actual Value -  Predicted Value])2


        

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