5 b. Linear Regression and Logistic Regression with the Diabetes Dataset Using Python Machine Learning
Aim
In this experiment we use the diabetes dataset from sklearn and then we need to implement the Linear Regression over this:
Procedure
Load sklearn Libraries.
Load Data
Load the diabetes dataset
Split Dataset
Creating Model Linear Regression and Logistic Regression
Make predictions using the testing set
Finding Coefficient And Mean Square Error
Program
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
#To calculate accuracy measures and confusion matrix
from sklearn import metrics
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
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:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
# Create Logistic regression object
Logistic_model = LogisticRegression()
Logistic_model.fit(diabetes_X_train, diabetes_y_train)
# 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))
y_predict = Logistic_model.predict(diabetes_X_train)
#print("Y predict/hat ", y_predict)
y_predict
Output
Coefficients:
[938.23786125]
Mean squared error: 2548.07
Coefficient of determination: 0.47
