Building Multiple Linear Regression
The LinearRegression class allows you to build Multiple Linear Regression the same way as Simple Linear Regression. It automatically handles multiple features (columns) in the input matrix.
Preparing the Data
We have the same dataset from the simple linear regression example, but it now has the mother's height as the second feature. We'll load it and look at its X variable:
12345678import 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 # Assign the variables X = df[['Father', 'Mother']] y = df['Height'] print(X.head())
By default, the parameter fit_intercept=True is set, so the model calculates the bias term () automatically!
We can pass the X DataFrame (which is already 2D) directly to the model.
Finding the Parameters
Great! Now we can build the model, find the parameters, and make predictions.
12345678910111213141516171819202122from sklearn.linear_model import LinearRegression import numpy as np # Initialize the model model = LinearRegression() # Train the model (No need to add constant!) model.fit(X, y) # Get the parameters beta_0 = model.intercept_ beta_1 = model.coef_[0] # Coefficient for 'Father' beta_2 = model.coef_[1] # Coefficient for 'Mother' print('beta_0 is: ', beta_0) print('beta_1 is: ', beta_1) print('beta_2 is: ', beta_2) # Predict new values # Feature values of new instances: [[Father, Mother], [Father, Mother], ...] X_new = np.array([[65, 62], [70, 65], [75, 70]]) y_pred = model.predict(X_new) # Predict the target print('Predictions:', y_pred)
Now that our training set has 2 features, we must provide 2 features for each new instance we want to predict. That's why np.array([[65, 62],[70, 65],[75, 70]]) was used.
Also, note that model.coef_ returns an array of coefficients corresponding to the order of columns in X. Since X was ['Father', 'Mother'], coef_[0] is the slope for Father, and coef_[1] is for Mother.
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Completion rate improved to 3.33Building Multiple Linear Regression
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The LinearRegression class allows you to build Multiple Linear Regression the same way as Simple Linear Regression. It automatically handles multiple features (columns) in the input matrix.
Preparing the Data
We have the same dataset from the simple linear regression example, but it now has the mother's height as the second feature. We'll load it and look at its X variable:
12345678import 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 # Assign the variables X = df[['Father', 'Mother']] y = df['Height'] print(X.head())
By default, the parameter fit_intercept=True is set, so the model calculates the bias term () automatically!
We can pass the X DataFrame (which is already 2D) directly to the model.
Finding the Parameters
Great! Now we can build the model, find the parameters, and make predictions.
12345678910111213141516171819202122from sklearn.linear_model import LinearRegression import numpy as np # Initialize the model model = LinearRegression() # Train the model (No need to add constant!) model.fit(X, y) # Get the parameters beta_0 = model.intercept_ beta_1 = model.coef_[0] # Coefficient for 'Father' beta_2 = model.coef_[1] # Coefficient for 'Mother' print('beta_0 is: ', beta_0) print('beta_1 is: ', beta_1) print('beta_2 is: ', beta_2) # Predict new values # Feature values of new instances: [[Father, Mother], [Father, Mother], ...] X_new = np.array([[65, 62], [70, 65], [75, 70]]) y_pred = model.predict(X_new) # Predict the target print('Predictions:', y_pred)
Now that our training set has 2 features, we must provide 2 features for each new instance we want to predict. That's why np.array([[65, 62],[70, 65],[75, 70]]) was used.
Also, note that model.coef_ returns an array of coefficients corresponding to the order of columns in X. Since X was ['Father', 'Mother'], coef_[0] is the slope for Father, and coef_[1] is for Mother.
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