Linear regression is a fundamental concept in machine learning. This tutorial will guide you through implementing linear regression using Scikit-Learn, a popular machine learning library in Python.

Overview

  • Linear Regression: A supervised learning algorithm that models the relationship between a scalar dependent variable and one or more explanatory variables.
  • Scikit-Learn: A free software machine learning library for the Python programming language.

Installation

Before you start, make sure you have Scikit-Learn installed. You can install it using pip:

pip install scikit-learn

Step-by-Step Guide

1. Import Libraries

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

2. Generate Sample Data

X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2, 4, 5, 4, 5])

3. Create Linear Regression Model

model = LinearRegression()

4. Fit the Model

model.fit(X, y)

5. Make Predictions

X_new = np.array([6]).reshape(-1, 1)
y_pred = model.predict(X_new)
print("Predicted value:", y_pred)

6. Visualize the Results

plt.scatter(X, y, color='red')
plt.plot(X, model.predict(X), color='blue')
plt.show()

Further Reading

For more information on linear regression and Scikit-Learn, check out the following resources:

Conclusion

This tutorial provided a basic overview of linear regression using Scikit-Learn. By following these steps, you should now be able to implement linear regression in your own projects.

Linear Regression Graph