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X_train, X_test, y_train, y_test train_test_split(X_reduced, y, test_size=0.2, random_state=42) est_size=6/2, Slope

print("\nTraining Samples:", X_train.shape[0])

print("Testing Samples:", X_test.shape[0])



lin_model LinearRegression()

lin_model.fit(X_train, y_train)

y_pred_lin lin_model.predict(X_test)

P

Slope

Varie

mse_lin mean_squared_error(y_test, y_pred_lin)

r2_1inr2_score(y_test, y_pred_lin)

print("\nLinear Regression:")

print("MSE: (mse_lin:.4f), R²: (r2_lin:.4f)")




plt.figure(figsize=(6,6))

plt.scatter(y_test, y_pred_lin, alpha=0.7)

plt.xlabel("Actual Values")

plt.ylabel("Predicted Values")

plt.title("Actual vs Predicted (Linear Regression)"

#Reference line

plt.plot([y_test.min(), y_test.max()],

[y_test.min(), y_test.max()),

linestyle='--')

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Slope

Judep

rependend

Variable

Variat

plt.show()]))