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Lära Predict Prices Using Two Features | Multiple Linear Regression
Linear Regression with Python
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Kursinnehåll

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Regression
4. Choosing The Best Model

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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).

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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())
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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.

Uppgift

Swipe to start coding

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

Lösning

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

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 5
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book
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())
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.

Uppgift

Swipe to start coding

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

Lösning

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

Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Var allt tydligt?

Hur kan vi förbättra det?

Tack för dina kommentarer!

Avsnitt 2. Kapitel 5
Switch to desktopByt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan
Vi beklagar att något gick fel. Vad hände?
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