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import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score # Load the Diabetes dataset diabetes = load_diabetes() X = diabetes.data[:, np.newaxis, 2] # using only one feature for simplicity y = diabetes.target # Split the dataset 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 y_pred = model.predict(X_test) # RMSE rmse = np.sqrt(mean_squared_error(y_test, y_pred)) print(f"RMSE: {rmse:.2f}") # R-squared score r2 = r2_score(y_test, y_pred) print(f"R-squared: {r2:.2f}") # Visualize the model plt.scatter(X_train, y_train, color='blue', label='Training data') plt.scatter(X_test, y_test, color='green', label='Test data') plt.plot(X_test, y_pred, color='red', linewidth=2, label='Regression line') plt.xlabel('Feature') plt.ylabel('Disease Progression') plt.legend() plt.title('Simple Linear Regression') plt.show()
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