Predict House Prices

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 show the trend. The OLS class should be used for that. Soon we will learn how to add more features, it will make the prediction better!

Tarea

Swipe to show code editor

Assign the 'price' column of df to y.
Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
Initialize the OLS object and train it.
Preprocess X_new array the same way as X.
Predict the target for X_new_tilde matrix.

_{Once you've completed this task, click the button below the code to check your solution.}

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 5

Predict House Prices

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 show code editor

Assign the 'price' column of df to y.
Create the X_tilde matrix using the add_constant() function from statsmodels(imported as sm).
Initialize the OLS object and train it.
Preprocess X_new array the same way as X.
Predict the target for X_new_tilde matrix.

_{Once you've completed this task, click the button below the code to check your solution.}

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 5

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