Linear Regression is still the most prominently used statistical technique in data science industry and in academia to explain relationships between features. A total of 1,355 people registered for this skill test. It was specially designed for you to test your knowledge on linear regression techniques. If you are one of those who missed out on this skill test, here are the questions and solutions. You missed on the real time test, but can read this article to find out how many could have answered correctly.

This article requires the knowledge of Linear Regression. If you haven't heard of it, then please check out an article on Linear Regression before you proceed here. Till now we assumed that the relationship between independent variable X and dependent Y can be represented with a straight line. But what if when we can't represent the relationship in a straight line because the data might not be linearly separable? In such kind of scenario we can look for polynomial regression.

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.