**Plotting polynomial regression models**

Today, we will attempt to plot polynomial regression models of differrent degrees and compare the regression lines and the R-squared values We will be using the Inactivity vs Diabetets data to perform this analysis as it has the maximum number of data points available.

The Polynomial regression is performed using the sklearn package that provides the inbuilt function *PolynomialFeatures() * that allows us to model the conersion matrix with the polynomial degree as parameter

Then we create the regression model using the *LinearRegression()* function and fit the model to our data. Once the model is created, it is a fairly straight forward process of using the model to predict the values and use this prediction to calculate the r-quared values for each degree model

Outputs:

As expected, the r-squared values shows slight progressive improvement with each successive degree, but at the same the its is clear from the plot that the model progresively tends to be overfitted making it less effective in predictions