Building Multiple Linear Regression

Building a Multiple Linear Regression is as easy as building a Simple Linear Regression with scikit-learn!
You need to use the same LinearRegression class.

The only difference is that the .coef_ attribute now returns an array of β₁ to βₙ parameters.
Also, X and X_new now hold more than one column.

Looking at data


              12345
            
import pandas as pd

file_link='https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/heights_two_feature.csv'
df = pd.read_csv(file_link)	# Open the file
print(df.head()) # Printing the first 5 rows of df

Here 'Height' is the target, and 'Father', 'Mother' are the features.
So we will assign 'Father' and 'Mother' columns as the X and 'Height' as the y.


              1234567
            
import pandas as pd

file_link='https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/heights_two_feature.csv'
df = pd.read_csv(file_link)	# Open the file
X = df[['Father', 'Mother']] # Assigning X
y = df['Height'] # Assigning y
print(X.head()) # Printing the first 5 rows of features

Now we can use the LinearRegression class the same way as before.
It will automatically find the best parameters for our features (using the Normal Equation underneath).


              1234567891011121314151617
            
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression # Import LinearRegression

file_link = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/heights_two_feature.csv'
df = pd.read_csv(file_link)	# Read the file

X = df[['Father', 'Mother']]	# Assign the feature (with double square brackets)
y = df['Height']	# Assign the target (no need in double square brackets for target)
model = LinearRegression()  # Initialize a model
model.fit(X, y)  # Train a model
# Creating a 2-D array of new instances
X_new = np.array([ [65, 62], [70, 65], [75, 70] ]) 
print("Predictions: ", model.predict(X_new)) # Predict a target for new instances
# Print the parameters (unnecessary if you only want to make the predictions)
print("beta_1 and beta_2: ", model.coef_)
print("beta_0: ", model.intercept_)

Note
Now that our training set has 2 features, we need to provide 2 features for each new instance we want to predict.
That's why np.array([[65, 62],[70, 65],[75, 70]]) was used in the example above.
It predicts y for 3 new instances: [Father:65,Mother:62], [Father:70, Mother:65], [Father:75, Mother:70]

Nice! We made predictions using Multiple Linear Regression.
Using β‎ parameters, we received the following formula:
Person's height = 23.25 + 0.33 * Father's height + 0.32 * Mother's height.

Everything was clear?

Section 2. Chapter 3