Linear Regression with Scikit-Learn – Python Explained

Linear regression is a fundamental technique in statistics and machine learning, used to model the relationship between a dependent variable and one or more independent variables. In this article, we will explore how to perform linear regression using Scikit-Learn, a popular machine learning library in Python.

Importing the necessary libraries

First, we need to import the necessary libraries for performing linear regression with Scikit-Learn. We will be using NumPy for numerical calculations and matplotlib for data visualization.

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

Loading the dataset

Next, we need to load the dataset that we will be using for linear regression. For the purpose of this demonstration, let’s create a simple dataset with two variables – X as the independent variable and y as the dependent variable.

        
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2, 4, 6, 8, 10])
        
    

Creating and fitting the linear regression model

Now, we can create a linear regression model using Scikit-Learn’s LinearRegression class and fit it to our dataset.

        
model = LinearRegression()
model.fit(X, y)
        
    

Visualizing the results

Finally, we can visualize the results of our linear regression model by plotting the original data points and the regression line.

        
plt.scatter(X, y, color='blue')
plt.plot(X, model.predict(X), color='red')
plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression')
plt.show()