Pattern : 7 Qs ( SQL+Python )

A healthcare research team wants to predict the quantitative measure of disease progression values in diabetes patients using baseline medical variables. Use the Diabetes dataset available in scikit-learn to build and evaluate a Linear Regression model.

Dataset: Use: from sklearn.datasets import load_diabetes The dataset contains ten baseline variables, including age, sex, body mass index, blood pressure, and blood serum measurements.

Task:

• Write a Python program that performs the following steps:

• Load the Diabetes dataset using load_diabetes().

• Separate the dataset into:

• Feature matrix X

• Target vector y

• Read one integer random_state from standard input.

• Split the dataset into training and testing sets using:

• test_size=0.2

• random_state equal to the input value

• Train a LinearRegression model using the training data.

• Predict disease progression values for the test set.

• Calculate the Mean Squared Error between actual and predicted values.

• Print the Mean Squared Error rounded to exactly six decimal places.

Constraints: 0 <= random_state <= 10000

Note: Do not print any extra text or labels

Input Format: A single integer random_state

Output Format: Print one floating-point value: the Mean Squared Error rounded to six decimal places.

Sample Input: 42

Sample Output: 2900.193628