Contenido del Curso

Linear Regression with Python

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building Linear Regression Using NumPy Building Linear Regression Using Statsmodels Predict House Prices

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Choosing the Features Predict Prices Using Two Features

3. Polynomial Regression

Quadratic Regression Polynomial Regression Building Polynomial Regression Interpolation vs Extrapolation Evaluate the Model

4. Choosing The Best Model

Metrics Overfitting R-squared Predict Prices Using Polynomial Regression

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())

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.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 5

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())

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.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 5

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())

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.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo 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())

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.

Tarea

Assign the 'age' and 'square_feet' columns of df to X.
Preprocess the X for the OLS's class constructor.
Build and train the model using the OLS class.
Preprocess the X_new array the same as X.
Predict the target for X_new.
Print the model's summary table.

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

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 2. Capítulo 5

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones