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())

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.

Завдання

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

Завдання

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

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 2. Розділ 4

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())

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.

Завдання

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

Завдання

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

Все було зрозуміло?

Секція 2. Розділ 4

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())

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.

Завдання

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

Завдання

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

Все було зрозуміло?

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())

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.

Завдання

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

Секція 2. Розділ 4