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Linear Regression with Python

bookChallenge: Predicting 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:

rmse = mean_squared_error(y_true, y_predicted, squared=False)
Task

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  1. Assign the DataFrame with a single column 'age' of df to the X variable.
  2. Preprocess the X using the PolynomialFeatures class.
  3. Split the dataset using the appropriate function from sklearn.
  4. Build and train a model on the training set.
  5. Predict the targets of both training and test set.
  6. Calculate the RMSE for both training and test set.
  7. 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.

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SectionΒ 4. ChapterΒ 4
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bookChallenge: Predicting 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:

rmse = mean_squared_error(y_true, y_predicted, squared=False)
Task

Swipe to start coding

  1. Assign the DataFrame with a single column 'age' of df to the X variable.
  2. Preprocess the X using the PolynomialFeatures class.
  3. Split the dataset using the appropriate function from sklearn.
  4. Build and train a model on the training set.
  5. Predict the targets of both training and test set.
  6. Calculate the RMSE for both training and test set.
  7. 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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 4. ChapterΒ 4
single

single

close

Awesome!

Completion rate improved to 5.26

bookChallenge: Predicting Prices Using Polynomial Regression

Swipe to show menu

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:

rmse = mean_squared_error(y_true, y_predicted, squared=False)
Task

Swipe to start coding

  1. Assign the DataFrame with a single column 'age' of df to the X variable.
  2. Preprocess the X using the PolynomialFeatures class.
  3. Split the dataset using the appropriate function from sklearn.
  4. Build and train a model on the training set.
  5. Predict the targets of both training and test set.
  6. Calculate the RMSE for both training and test set.
  7. 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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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