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Predict Prices Using Two Features | Multiple Linear Regression
Linear Regression with Python
course content

Course Content

Linear Regression with Python

Linear Regression with Python

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

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.

Task

  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.

Task

  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.

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

Everything was clear?

Section 2. Chapter 5
toggle bottom row

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.

Task

  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.

Task

  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.

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

Everything was clear?

Section 2. Chapter 5
toggle bottom row

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.

Task

  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.

Task

  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.

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

Everything was clear?

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.

Task

  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.

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

Section 2. Chapter 5
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