5 c. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following: Multiple Regression

 5 c. Use the diabetes data set from UCI and Pima Indians Diabetes data set for performing the following: Multiple Regression

Aim

Multiple regression is like linear regression, but with more than one independent value, meaning that we try to predict a value based on two or more variables.

Procedure 

The Pandas module allows us to read csv files and return a DataFrame object.

Then make a list of the independent values and call this variable X.

Put the dependent values in a variable called y.

From the sklearn module we will use the LinearRegression() method to create a linear regression object.

This object has a method called fit() that takes the independent and dependent values as parameters and fills the regression object with data that describes the relationship. 

We have a regression object that are ready to predict age values based on a person  Glucose and BloodPressure

Program

import pandas as pd

from sklearn import linear_model

df = pd.read_csv (r'd:\\diabetes.csv')

print (df)

X = df[['Glucose', 'BloodPressure']]

y = df['Age']

regr = linear_model.LinearRegression()

regr.fit(X, y)

predictedage = regr.predict([[150, 13]])

print(predictedage)

Output

[28.77214401]