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Predict Prices Using Two Features | Multiple Linear Regression
Linear Regression with Python

Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age and square_feet).

Your task is to build a Multiple Linear Regression model using the OLS class. Also, you will print the summary table to look at the p-values of each feature.

Tarea

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Preprocess the X for the OLS's class constructor.
  3. Build and train the model using the OLS class.
  4. Preprocess the X_new array the same as X.
  5. Predict the target for X_new.
  6. Print the model's summary table.

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

¿Todo estuvo claro?

Sección 2. Capítulo 5
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Contenido del Curso

Linear Regression with Python

Predict Prices Using Two Features

For this challenge, the same housing dataset will be used. However, now it has two features: age and area of the house (columns age and square_feet).

Your task is to build a Multiple Linear Regression model using the OLS class. Also, you will print the summary table to look at the p-values of each feature.

Tarea

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Preprocess the X for the OLS's class constructor.
  3. Build and train the model using the OLS class.
  4. Preprocess the X_new array the same as X.
  5. Predict the target for X_new.
  6. Print the model's summary table.

If you did everything right, you got the p-values close to zero. That means all our features are significant for the model.

¿Todo estuvo claro?

Sección 2. Capítulo 5
toggle bottom row
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