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

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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).

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import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv') print(df.head())
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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.

Tarefa

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  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.

Solução

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

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Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 2. Capítulo 5

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book
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).

1234
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houseprices.csv') print(df.head())
copy

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.

Tarefa

Swipe to start coding

  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.

Solução

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

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 2. Capítulo 5
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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