Polynomial regression is a type of regression that models the relationship between the independent variable x and the dependent variable y as an nth degree polynomial. In this tutorial, we will use Scikit-Learn, a popular machine learning library in Python, to perform polynomial regression.
To get started with polynomial regression in Scikit-Learn, you will need to have Python and Scikit-Learn installed on your machine. If you haven’t already installed them, you can do so by running the following commands in your terminal:
pip install numpy
pip install scipy
pip install scikit-learnOnce you have installed the necessary libraries, you can start coding. Below is an example of how to perform polynomial regression with Scikit-Learn:
<!DOCTYPE html>
<html>
<head>
<title>Polynomial Regression with Scikit-Learn</title>
</head>
<body>
<h1>Polynomial Regression Tutorial</h1>
<h2>Step 1: Import the necessary libraries</h2>
<code>
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
</code>
<h2>Step 2: Create some sample data</h2>
<code>
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2, 4, 6, 8, 10])
</code>
<h2>Step 3: Fit the polynomial regression model</h2>
<code>
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)
model = LinearRegression().fit(X_poly, y)
</code>
<h2>Step 4: Make predictions with the model</h2>
<code>
y_pred = model.predict(X_poly)
</code>
<h2>Step 5: Visualize the results</h2>
<code>
plt.scatter(X, y, color='blue')
plt.plot(X, y_pred, color='red')
plt.xlabel('X')
plt.ylabel('y')
plt.title('Polynomial Regression')
plt.show()
</code>
<h2>Step 6: Calculate the model performance</h2>
<code>
from sklearn.metrics import r2_score
r2 = r2_score(y, y_pred)
print(f'R-squared: {r2}')
</code>
</body>
</html>In this example, we first imported the necessary libraries, created some sample data, fit a polynomial regression model to the data, made predictions with the model, visualized the results, and calculated the model performance using the R-squared score.
You can modify the degree of the polynomial by changing the degree parameter in the PolynomialFeatures class. You can also use higher-order polynomials for more complex relationships between the variables.
I hope this tutorial was helpful in understanding how to perform polynomial regression with Scikit-Learn. Feel free to experiment with different datasets and parameters to further enhance your understanding of polynomial regression.
