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Linear Regression with Python

bookChallenge: Evaluating the Model

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              
1234

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())
copy

Next, we'll create a scatterplot for this data:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)
plt.show()
copy

Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.

But before you start, recall the PolynomialFeatures class.

The fit_transform(X) method requires X to be a 2-D array (or a DataFrame).
Using X = df[['column_name']] will get your X suited for fit_transform().
And if you have a 1-D array, use .reshape(-1, 1) to make a 2-D array with the same contents.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Task

Swipe to start coding

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Reshape X_new to be a 2-D array.
  5. Preprocess X_new the same way as X.
  6. Print the model's parameters.

Solution

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SectionΒ 3. ChapterΒ 5
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bookChallenge: Evaluating the Model

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              
1234

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())
copy

Next, we'll create a scatterplot for this data:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)
plt.show()
copy

Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.

But before you start, recall the PolynomialFeatures class.

The fit_transform(X) method requires X to be a 2-D array (or a DataFrame).
Using X = df[['column_name']] will get your X suited for fit_transform().
And if you have a 1-D array, use .reshape(-1, 1) to make a 2-D array with the same contents.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Task

Swipe to start coding

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Reshape X_new to be a 2-D array.
  5. Preprocess X_new the same way as X.
  6. Print the model's parameters.

Solution

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!

SectionΒ 3. ChapterΒ 5
single

single

close

Awesome!

Completion rate improved to 5.26

bookChallenge: Evaluating the Model

Swipe to show menu

In this challenge, you are given the good old housing dataset, but this time only with the 'age' feature.


              
1234

            
import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
print(df.head())
copy

Next, we'll create a scatterplot for this data:


              
12345678

            
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/houses_poly.csv')
X = df['age']
y = df['price']
plt.scatter(X, y, alpha=0.4)
plt.show()
copy

Fitting a straight line to this data may not be a great choice. The price gets higher for either brand-new or really old houses. Fitting a parabola looks like a better choice. And that's what you will do in this challenge.

But before you start, recall the PolynomialFeatures class.

The fit_transform(X) method requires X to be a 2-D array (or a DataFrame).
Using X = df[['column_name']] will get your X suited for fit_transform().
And if you have a 1-D array, use .reshape(-1, 1) to make a 2-D array with the same contents.

The task is to build a Polynomial Regression of degree 2 using PolynomialFeatures and OLS.

Task

Swipe to start coding

  1. Assign the X variable to a DataFrame containing column 'age'.
  2. Create an X_tilde matrix using the PolynomialFeatures class.
  3. Build and train a Polynomial Regression model.
  4. Reshape X_new to be a 2-D array.
  5. Preprocess X_new the same way as X.
  6. Print the model's parameters.

Solution

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!

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