Sveip for å vise menyen

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.

Oppgave

Swipe to start coding

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 2. Kapittel 4

Spør AI

Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår

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.

Oppgave

Swipe to start coding

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 2. Kapittel 4

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor