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Linear Regression with Python
course content

Course Content

Linear Regression with Python

Linear Regression with Python

1. Simple Linear Regression
What is Linear RegressionFinding the ParametersBuilding Linear Regression Using NumPyBuilding Linear Regression Using StatsmodelsPredict House Prices
2. Multiple Linear Regression
Linear Regression with Two FeaturesLinear Regression with n FeaturesBuilding Multiple Linear RegressionChoosing the FeaturesPredict Prices Using Two Features
3. Polynomial Regression
Quadratic RegressionPolynomial RegressionBuilding Polynomial RegressionInterpolation vs ExtrapolationEvaluate the Model
4. Choosing The Best Model
MetricsOverfittingR-squaredPredict Prices Using Polynomial Regression

bookPredict 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.

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

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Thanks for your feedback!

Section 2. Chapter 5
toggle bottom row

bookPredict 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.

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5
toggle bottom row

bookPredict 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.

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your 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.

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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