Create linear_regression.py

ai_research/ML_Fundamentals/linear_regression.py

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+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()