cyber-security-resources/ai_research/ML_Fundamentals/linear_regression.py

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

# 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)

# Create a linear regression model
lin_reg = LinearRegression()

# Fit the model to the training data
lin_reg.fit(X_train, y_train)

# Get the slope (coef_) and the intercept (intercept_)
slope = lin_reg.coef_
intercept = lin_reg.intercept_

# Print the slope and intercept
print("Slope (Coefficient): ", slope)
print("Intercept: ", intercept)

# Predict y values for the test set
y_pred = lin_reg.predict(X_test)

# Visualize the training data and the regression line
plt.scatter(X_train, y_train, color='blue', label='Training Data')
plt.scatter(X_test, y_test, color='green', label='Testing Data')
plt.plot(X_test, y_pred, color='red', linewidth=2, label='Regression Line')
plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression Demonstration')
plt.legend()
plt.show()