#imports 
import numpy as np 
from sklearn.linear_model import LinearRegression 
from sklearn.preprocessing import PolynomialFeatures

#creating sample data 
array_x = np.array([5, 10, 15, 20, 25]).reshape((-1, 1))
~ calling reshape makes sure that the array is 2D 
array_y = np.array([5, 3, 15, 23, 6])

#transforming data 
transformed = PolynomialFeatures(degree = 2, include_bias = False)
#degree = 2, so this is a binomial regression 
#then fit it 
transformed.fit(array_x)

#now it has been fitted, so let’s create a new input 
x_mod = transformed.transform(array_x)

#create a model and fit it 
model = LinearRegression().fit(x_mod, y)

#get results 
r_squared = model.score(x_mod, y)
intercept = model.intercept_
coefficients = model.coef_

#predict response 
y_pred = model.predict(x_)