linear_reg_pima.py

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 = diabetes.data[:, 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 = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# Input data
print('Input Values')
print(diabetes_X_test)

# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)

# Predicted Data
print("Predicted Output Values")
print(diabetes_y_pred)

# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='red', linewidth=1)

plt.show()