Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Aprenda Challenge | Building and Training Model
Explore the Linear Regression Using Python

Deslize para mostrar o menu

book
Challenge

Let’s combine our knowledge!

Tarefa

Swipe to start coding

In this task, you build, train and fit your model and make predictions based on it. This time you will make predictions about total_phenols, based on flavanoids. It means that your target now is total_phenols.

Your plan:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [Line #32] Print the predicted amount of flavanoids.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 4

Pergunte à IA

expand
ChatGPT

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

book
Challenge

Let’s combine our knowledge!

Tarefa

Swipe to start coding

In this task, you build, train and fit your model and make predictions based on it. This time you will make predictions about total_phenols, based on flavanoids. It means that your target now is total_phenols.

Your plan:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [Line #32] Print the predicted amount of flavanoids.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 4
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Sentimos muito que algo saiu errado. O que aconteceu?
some-alt