Visualizing Linear Regression with Matplotlib in AI

Description: This example demonstrates how to perform linear regression on a dataset and visualize the results using matplotlib. The example fits a linear model to data and shows the relationship between the independent variable and the dependent variable.

1: Install Required Libraries

First, ensure you have numpy, scikit-learn, and matplotlib installed. You can install these using pip:

pip install numpy scikit-learn matplotlib

2: Create the Python Script

Create a Python script (e.g., linear_regression_ai.py) with the following code:

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

# Generate some synthetic data for training
# Here we create a simple linear relationship y = 3x + 2 with some noise
np.random.seed(42)
X = 2 * np.random.rand(100, 1)
y = 3 * X + 2 + np.random.randn(100, 1)

# Create a linear regression model and fit it to the data
model = LinearRegression()
model.fit(X, y)

# Predict values using the model
X_new = np.array([[0], [2]])
y_predict = model.predict(X_new)

# Plot the original data and the regression line
plt.scatter(X, y, color='blue', label='Data points')
plt.plot(X_new, y_predict, color='red', label='Regression line')
plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression in AI')
plt.legend()
plt.show()

Explanation

1: Import Libraries:

• numpy for numerical operations.
• matplotlib.pyplot for plotting.
• sklearn.linear_model.LinearRegression for performing linear regression.

2: Generate Synthetic Data:

• np.random.seed(42): Sets the seed for reproducibility.
• X = 2 * np.random.rand(100, 1): Creates 100 random data points for the independent variable X.
• y = 3 * X + 2 + np.random.randn(100, 1): Creates the dependent variable y with a linear relationship to X plus some noise.

3: Fit the Model:

• model = LinearRegression(): Creates a linear regression model.
• model.fit(X, y): Fits the model to the data.

4: Make Predictions:

• X_new = np.array([[0], [2]]): Defines new data points for prediction.
• y_predict = model.predict(X_new): Predicts y values for the new data points.

5: Plot the Data and Regression Line:

• plt.scatter(X, y, color='blue', label='Data points'): Plots the original data points.
• plt.plot(X_new, y_predict, color='red', label='Regression line'): Plots the regression line.
• plt.xlabel('X'), plt.ylabel('y'), plt.title('Linear Regression in AI'), plt.legend(): Adds labels, title, and legend to the plot.
• plt.show(): Displays the plot.

Next >