Linear Regression Example

This is a simple example of Linear Regression using Python and scikit-learn.

Python Source Code:

# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Generate synthetic data
np.random.seed(42)
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# Plot the results
plt.scatter(X_test, y_test, color='black')
plt.plot(X_test, y_pred, color='blue', linewidth=3)
plt.title('Linear Regression Example')
plt.xlabel('X')
plt.ylabel('y')
plt.show()

Explanation:

• Import Libraries: Import necessary Python libraries, including NumPy for numerical operations, Matplotlib for plotting, and scikit-learn for machine learning.
• Generate Synthetic Data: Create synthetic data with a linear relationship and some random noise.
• Split Data: Split the data into training and testing sets using the train_test_split function.
• Train Model: Create and train a Linear Regression model using scikit-learn's LinearRegression.
• Make Predictions: Use the trained model to make predictions on the test set.
• Evaluate Model: Calculate Mean Squared Error to evaluate the performance of the model.
• Plot Results: Plot the actual vs. predicted values for visualization.