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Created January 28, 2018 14:00
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from sklearn import datasets, linear_model
import matplotlib.pyplot as plt
import numpy as np
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature for training
diabetes_X =[:, np.newaxis, 2]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train =[:-20]
diabetes_y_test =[-20:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets, diabetes_y_train)
# Input data
print('Input Values')
# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
# Predicted Data
print("Predicted Output Values")
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='red', linewidth=1)
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