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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!

Oppgave

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 4

Spør AI

Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår

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)

Oppgave

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.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 4

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor