Conteúdo do Curso
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:
Now let's move to coding!
Swipe to show code editor
- 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.
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.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu 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:
Now let's move to coding!
Swipe to show code editor
- 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.
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.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu 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:
Now let's move to coding!
Swipe to show code editor
- 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.
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.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoObrigado pelo seu 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!
Swipe to show code editor
- 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.
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.
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixoMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo