Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Predict Prices Using Polynomial Regression | Choosing The Best Model
Linear Regression with Python
course content

Course Content

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding Linear Regression Using NumPyBuilding Linear Regression Using StatsmodelsPredict House Prices
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChoosing the FeaturesPredict Prices Using Two Features
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionBuilding Polynomial RegressionInterpolation vs ExtrapolationEvaluate the Model
4. Choosing The Best Model
MetricsOverfittingR-squaredPredict Prices Using Polynomial Regression

bookPredict 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:

Now let's move to coding!

Task
test

Swipe to show code editor

  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.

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!

Section 4. Chapter 4
toggle bottom row

bookPredict 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:

Now let's move to coding!

Task
test

Swipe to show code editor

  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.

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!

Section 4. Chapter 4
toggle bottom row

bookPredict 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:

Now let's move to coding!

Task
test

Swipe to show code editor

  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.

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!

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
test

Swipe to show code editor

  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.

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
Section 4. Chapter 4
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt