In previous stories, I have given a brief of Linear Regression and showed how to perform Simple and Multiple Linear Regression. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

Learn to build a Polynomial Regression model to predict the values for a non-linear dataset. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

This course is targeted for Beginner Python Developers who want to kickstart their journey in Machine Learning. In this course, we are going to use a linear regression model from scikit-learn library in Python to predict the total no. of positive cases for COVID19 in a particular state in India. After completing this course, you'll be able to:

The CRAN task view: "Robust statistical methods" gives a long list of regression methods, including many that Vincent mentions. Here a some that are not mentioned there: Regression in unusual spaces. It is usually addressed under the title "Compositional data" (see Wikipedia entry). The late John Aitchison founded this area of statistics. Googling his name "compositional data" gives access to a number of his articles.

This Tutorial talks about basics of Linear regression by discussing in depth about the concept of Linearity and Which type of linearity is desirable. Linear regression however always means linearity in parameters, irrespective of linearity in explanatory variables. Here the variable X can be non linear i.e X or X² and still we can consider this as a linear regression. However if our parameters are not linear i.e say the regression equation is A function Y f(x) is said to be linear in X if X appears with a power or index of 1 only. Y is linearly related to X if the rate of change of Y with respect to X (dY/dX) is independent of the value of X. B2 is Linear but B1 is non-linear but if we transform?