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


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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.


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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).


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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.

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