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Linear Regression with Python

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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())
copy

Let's build a scatterplot of this data.


              
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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.

Tarea

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.

Solución

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¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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book
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())
copy

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()
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.

Tarea

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.

Solución

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

close

Awesome!

Completion rate improved to 5.26

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