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Challenge

Let's build a real-world example regression model. We have a file, houses_simple.csv, that holds information about housing prices with its area as a feature.


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import pandas as pd

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

Let's assign variables and visualize our dataset!


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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_simple.csv')
X = df[['square_feet']]
y = df['price']

plt.scatter(X, y, alpha=0.5)

In the example with a person's height, it was much easier to imagine a line fitting the data well.
But now our data has much more variance since the target highly depends on many other things like age, location, interior, etc.
Anyway, the task is to build the line that best fits the data we have; it will at least show the trend. The LinearRegression class should be used for that. Soon we will learn how to add more features to improve the predictions!

Tarea

Swipe to start coding

Import the LinearRegression class from sklearn.linear_model.
Assign the 'square_feet' column to X.
Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
Initialize the LinearRegression model.
Train the model.
Predict the target for the X_new array.

Solución

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 1. Capítulo 4

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Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla

Challenge

Let's build a real-world example regression model. We have a file, houses_simple.csv, that holds information about housing prices with its area as a 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_simple.csv')
print(df.head())

Let's assign variables and visualize our dataset!


              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_simple.csv')
X = df[['square_feet']]
y = df['price']

plt.scatter(X, y, alpha=0.5)

Tarea

Swipe to start coding

Import the LinearRegression class from sklearn.linear_model.
Assign the 'square_feet' column to X.
Make sure you assign pandas DataFrame with a single column instead of pandas Series (refer to hint if needed).
Initialize the LinearRegression model.
Train the model.
Predict the target for the X_new array.

Solución

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 1. Capítulo 4

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones