mirror of
https://github.com/The-Art-of-Hacking/h4cker
synced 2024-11-22 19:03:04 +00:00
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
|
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()
|