5 b. Linear Regression and Logistic Regression with the Diabetes Dataset Using Python Machine Learning

 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