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Impara Challenge | Building and Training Model
Explore the Linear Regression Using Python
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Contenuti del Corso

Explore the Linear Regression Using Python

Explore the Linear Regression Using Python

1. What is the Linear Regression?
2. Correlation
3. Building and Training Model
4. Metrics to Evaluate the Model
5. Multivariate Linear Regression

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Challenge

Let’s combine our knowledge!

Compito

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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.

Soluzione

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Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 4
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book
Challenge

Let’s combine our knowledge!

Compito

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.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 3. Capitolo 4
Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Siamo spiacenti che qualcosa sia andato storto. Cosa è successo?
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