Course Content

Linear Regression with Python

1. Simple Linear Regression

What Is The Linear Regression Finding The Parameters Building The Linear Regression Using NumPy Building The Linear Regression Using Statsmodels Challenge

2. Multiple Linear Regression

Linear Regression With Two Features Linear Regression With n Features Building Multiple Linear Regression Choosing The Features Challenge

3. Polynomial Regression

Quadratic Regression Polynomial Regression Building The Polynomial Regression Interpolation vs Extrapolation Challenge

4. Choosing The Best Model

Metrics Overfitting R-squared Challenge

Linear Regression with Python

Challenge

For this challenge, you will build the same Polynomial Regression of degree 2 as in the previous challenge. However, you will need to split the set into a training set and a test set to calculate RMSE for both those sets. This is required to judge whether the model overfits/underfits or not.
Here is the reminder of the train_test_split() function you'll want to use.

And also reminder of the mean_squared_error() function needed to calculate RMSE:

Now let's move to coding!

Task

Assign the DataFrame with a single column 'age' of df to the X variable.
Preprocess the X using the PolynomialFeatures class.
Split the dataset using the appropriate function from sklearn.
Build and train a model on the training set.
Predict the targets of both training and test set.
Calculate the RMSE for both training and test set.
Print the summary table.

When you complete the task, you will notice that the test RMSE is even lower than the training RMSE. Usually, models do not show better results on unseen instances. Here, the difference is tiny and caused by chance. Our dataset is relatively small, and while splitting, the test set received a bit better(easier to predict) data points.

Everything was clear?

Section 4. Chapter 4