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Challenge

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())

Let's build a scatterplot of this data.


              1234567
            
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)

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.

The task is to build a Polynomial Regression of degree 2 using a pipeline, as was shown in a previous chapter. Here is a list of the classes and functions from sklearn that you will need.

Aufgabe

Swipe to start coding

Create a model using the make_pipeline function.
As function arguments, pass the instances of classes that:
- adds polynomial features of a degree n (don't forget to set the include_bias to False).
- performs Linear Regression.
Train the model.
Predict the target for X_new.

Lösung

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 6

Fragen Sie AI

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

Challenge

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())

Let's build a scatterplot of this data.


              1234567
            
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)

The task is to build a Polynomial Regression of degree 2 using a pipeline, as was shown in a previous chapter. Here is a list of the classes and functions from sklearn that you will need.

Aufgabe

Swipe to start coding

Create a model using the make_pipeline function.
As function arguments, pass the instances of classes that:
- adds polynomial features of a degree n (don't forget to set the include_bias to False).
- performs Linear Regression.
Train the model.
Predict the target for X_new.

Lösung

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 6

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden