Apprendre Challenge: Predicting House Prices

You will now build a real-world example regression model. You 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())

The next step is to assign variables and visualize the 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)
plt.show()

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!

Tâche

Swipe to start coding

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.

Solution

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Merci pour vos commentaires !

Section 1. Chapitre 5

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