Where y is the predicted response value, a is the y-intercept, x is the feature value and b is a slope.

The equation of regression line is given by: y = a + bx The line which best fits is called the Regression line. Now, we have to find a line that fits the above scatter plot through which we can predict any value of y or response for any value of x for n observations (in above example, n=10).Hence, we try to find a linear function that predicts the response value(y) as accurately as possible as a function of the feature or independent variable(x).įor understanding the concept let’s consider a salary dataset where it is given the value of the dependent variable(salary) for every independent variable(years experienced). It is assumed that the two variables are linearly related. One variable denoted x is regarded as an independent variable and the other one denoted y is regarded as a dependent variable. It is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables. Let’s discuss Simple Linear regression using R. There are two types of linear regression. Decision Tree Introduction with example.Removing stop words with NLTK in Python.Regression and Classification | Supervised Machine Learning.Basic Concept of Classification (Data Mining).Gradient Descent algorithm and its variants.

ML | Momentum-based Gradient Optimizer introduction.

Optimization techniques for Gradient Descent.

ML | Mini-Batch Gradient Descent with Python.

Difference between Batch Gradient Descent and Stochastic Gradient Descent.

Difference between Gradient descent and Normal equation.

ML | Normal Equation in Linear Regression.

Mathematical explanation for Linear Regression working.

Linear Regression (Python Implementation).

ISRO CS Syllabus for Scientist/Engineer Exam.

ISRO CS Original Papers and Official Keys.

GATE CS Original Papers and Official Keys.

Simple linear regression equation in r