We will take the second column, Level as our independent variable in the feature matrix, X and the Salary column as the dependent variable in the dependent variable vector, y. So we are required to use a Polynomial Regression for this case. Here, you can observe that our data has a tendency of growing non-linearly. If we plot the above data in a graph, it would look like this. Suppose we have a dataset(Salary_data.csv) that contains the salaries of employees of different positions based on their Level. Now we will jump into a dataset and implement the above idea. Implementing Polynomial Regression in Python And based on the non-linear characteristics of your data, you should use polynomial regression. For example, if you want to discover how diseases spread, how a pandemic or epidemic spread over a continent, and so on. There are many cases where you will find great uses of polynomial regression. The Polynomial model has fitted the dataset well with a higher accuracy rate than that of a simple linear model.

Now if you look at the polynomial regression, you will clearly see the difference. Plotted with a polynomial regression model Hence a simple linear model could not find the most optimal line that can fit the data well and has a very poor accuracy level. Here you can see the data has a tendency to grow in a non-linear fashion. Let's say you have a dataset and fit both Simple and Polynomial Regression to that data. The main difference is that in Multiple Linear Regression, there are several variables of the same degree but here the single variable has different powers. The equation for Polynomial Regression looks very similar to that of Multiple Linear Regression.y=b0+b1*x1+b2*(x1)2+.+bn*(x1)n That is, if your dataset holds the characteristic of being curved when plotted in the graph, then you should go with a polynomial regression model instead of SimpleВ or Multiple Linear regressionВ models. Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial of x. Let's dive into that! What is Polynomial Regression? After discussing the concepts, we will build a predictive model with the algorithm in Python. In this tutorial, we are going to understand a different type of regression algorithm, the Polynomial Regression. And you need to use the right kind of algorithm to make the best predictive model out of your data. It is not like that the models are bad, rather it is due to the underlying property of your data. If you have worked with linear regression models such as simple linear regression or multiple linear regression, you might have observed some drawbacks of those models.

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