Course Content
Linear Regression with Python
Linear Regression with Python
Predict Prices Using Polynomial Regression
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:
python
Now let's move to coding!
Swipe to start coding
- Assign the DataFrame with a single column
'age'ofdfto theXvariable. - Preprocess the
Xusing thePolynomialFeaturesclass. - 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.
Solution
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.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Predict Prices Using Polynomial Regression
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:
python
Now let's move to coding!
Swipe to start coding
- Assign the DataFrame with a single column
'age'ofdfto theXvariable. - Preprocess the
Xusing thePolynomialFeaturesclass. - 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.
Solution
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.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Switch to desktop for real-world practiceContinue from where you are using one of the options below