Linear Regression with the Diabetes Dataset Using Python Machine Learning

In this we use the diabetes dataset from sklearn and then we need to implement the Linear Regression over this:

Load sklearn Libraries:

#import libraries
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score

Load Data

# Load the diabetes dataset
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)

Split Dataset

# Use only one feature
diabetes_X = 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 = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]

Creating Model

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

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

Make Prediction

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

Finding Coefficient And Mean Square Error

# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print('Mean squared error: %.2f'
      % mean_squared_error(diabetes_y_test, diabetes_y_pred))
# The coefficient of determination: 1 is perfect prediction
print('Coefficient of determination: %.2f'
      % r2_score(diabetes_y_test, diabetes_y_pred)) 

Output

Coefficients: [938.23786125] Mean squared error: 2548.07 Coefficient of determination: 0.47

Plot the Result

#Scatter Plot
plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()

Output

Need any help in machine learning project, machine learning assignment, machine learning homework or any coursework then our expert ready to help you. You need to send your requirement details at:

realcode4you@gmail.com

Get quality code with our researchers and professionals which work large number of projects which is related to Deep Learning, Big Data, NLP, OpenCV, Image Processing and more other advance level concepts.