Linear Regression Tutorial

Linear regression is one of the most fundamental and widely used machine learning algorithms. It is used to predict a continuous target variable based on one or more input variables.

Key Concepts

• Independent Variable: The variable that you use to predict the value of the dependent variable.
• Dependent Variable: The variable that you want to predict.

Types of Linear Regression

• Simple Linear Regression: Used when there is only one independent variable.
• Multiple Linear Regression: Used when there are more than one independent variables.

How to Implement Linear Regression

To implement linear regression, you can use libraries like scikit-learn in Python.

from sklearn.linear_model import LinearRegression

# Create a linear regression object
regr = LinearRegression()

# Train the model using the training sets
regr.fit(X_train, y_train)

# Make predictions using the testing set
y_pred = regr.predict(X_test)

Further Reading

For more detailed information on linear regression, you can check out our Advanced Linear Regression Tutorial.

Visualizing Linear Regression

To visualize linear regression, you can use libraries like matplotlib in Python.

import matplotlib.pyplot as plt

# Plot outputs
plt.scatter(X_test, y_test,  color='black')
plt.plot(X_test, y_pred, color='blue', linewidth=3)

plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')

plt.show()

Visualizing Linear Regression