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Evaluate 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())

Let's build a scatterplot of 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()

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.

Завдання

Swipe to start coding

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

Рішення

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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Evaluate 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())

Let's build a scatterplot of 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()

But before you start, recall the PolynomialFeatures class.

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

Завдання

Swipe to start coding

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

Рішення

Все було зрозуміло?

Дякуємо за ваш відгук!

Свайпніть щоб показати меню

Evaluate the Model

Рішення

Awesome!

Evaluate the Model

Рішення

Awesome!