Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.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
What is Linear RegressionFinding the ParametersBuilding the Linear Regression with scikit-learnChallengeCheatsheet
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChallengeCheatsheet
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionPolynomialFeaturesBuilding the Polynomial RegressionInterpolation vs ExtrapolationChallengeCheatsheet
4. Evaluating and Comparing Models
Metrics

bookChallenge

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.

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 4
toggle bottom row

bookChallenge

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.

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 4
toggle bottom row

bookChallenge

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.

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!

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.

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 2. Capítulo 4
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
some-alt