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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.

Taak

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.

Oplossing

Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 3. Hoofdstuk 6

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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)

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.

Taak

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.

Oplossing

Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 3. Hoofdstuk 6

Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties