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Challenge | Multiple Linear Regression
Linear Regression for ML
course content

Conteúdo do Curso

Linear Regression for ML

Linear Regression for ML

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Evaluating and Comparing Models

Challenge

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

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

Tarefa

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

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Tudo estava claro?

Seção 2. Capítulo 4
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Challenge

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

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

Tarefa

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 4
toggle bottom row

Challenge

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

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

Tarefa

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

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

  1. Assign the 'age' and 'square_feet' columns of df to X.
  2. Build and train the model using the LinearRegression class.
  3. Predict the target for X_new.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 2. Capítulo 4
Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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