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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, 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)) 


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)


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