For this Question: Creating polynomial features is a simple and common way of feature engineering that adds complexity to numeric input data by combining features.
Polynomial features are often created when we want to include the notion that there exists a nonlinear relationship between the features and the target.
Sklearn provides a PolynomialFeatures class to create polynomial features from scratch. The degree parameter determines the maximum degree of the polynomial. For example, when degree is set to two and X=x1, x2, the features created will be 1, x1, x2, x1², x1x2 and x2². The interaction_only parameter let the function know we only want the interaction features, i. e. 1, x1, x2 and x1x2.

so given an even integer as an input, make a numpy array containing that many elements, i. e., if the input is n then the array would be from 0 to n-1, both inclusive,
Now reshape this array to have 2 columns, the number of rows can be accommodated automatically given n, so after reshaping the array having 2 columns and number of rows such that the data fits, 
use PolynomialFeatures from sklearn. preprocessing to create an array which has all polynomial features till degree 2, what that means is as we have X=x1, x2 owing to the 2 columns, we want the resulting array to be like X_new = 1, x1, x2, x1², x1x2 and x2².
print the final array X_new.6[[ 1. 0. 1. 0. 0. 1. ]
 [ 1. 2. 3. 4. 6. 9. ]
 [ 1. 4. 5. 16. 20. 25. ]]-, I have written this Solution Code: import numpy as np 
from sklearn.preprocessing import PolynomialFeatures
n = int(input())
X = np.arange(n).reshape(-1, 2)
poly = PolynomialFeatures(2)
print(poly.fit_transform(X)), In this Programming Language: Python, Now tell me if this Code is compilable or not?