Linear Regression Example in sklearn

Linear regression is a linear approach to modeling the relationship between a scalar dependent variable and one or more explanatory variables.

In this example, we will use the popular Python library sklearn to perform linear regression on a sample dataset.

Sample Dataset

Let’s start by creating a sample dataset. We will use the following Python code to generate some random data:

import numpy as np
import matplotlib.pyplot as plt

# Generate random data
np.random.seed(0)
X = np.random.rand(100, 1)
y = 2 + 3 * X + np.random.randn(100, 1)
    

Linear Regression

Next, we will use the sklearn library to perform linear regression on our sample dataset:

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Create a linear regression model
model = LinearRegression()

# Fit the model to the data
model.fit(X, y)

# Make predictions
y_pred = model.predict(X)

# Calculate mean squared error
mse = mean_squared_error(y, y_pred)
print('Mean Squared Error:', mse)
    

Visualizing the Results

Finally, we can visualize the results of our linear regression model using a scatter plot:

# Plot the data
plt.scatter(X, y, color='blue')
# Plot the regression line
plt.plot(X, y_pred, color='red', linewidth=3)
plt.show()
    

Conclusion

In this example, we demonstrated how to perform linear regression using the sklearn library in Python. We created a sample dataset, trained a linear regression model, and visualized the results. Linear regression is a powerful tool for modeling and predicting relationships between variables, and sklearn makes it easy to use in your Python projects.