Scorri per mostrare il menu

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)

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!

Compito

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.

Soluzione

Cambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 4

Chieda ad AI

Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione

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)

Compito

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.

Soluzione

Cambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 1. Capitolo 4

Cambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti