PolynomialRegression.py

#Import required modules

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

#Some random values for input to a model

X_train = [[1,4],[3,5]]
Y_train = [1,2]
X_test = [[1,5]]

#Create a Linear Regressor

lin_regressor = LinearRegression()

#Pass the order of your polynomial here

poly = PolynomialFeatures(degree = 2)

#Convert to be used further to linear regression. 
#If we have two variables a and b, then degree 2 and using fit_transform would give us 1,a, b, a2, ab and b2.

X_transform = poly.fit_transform(X_train)
X_test_ = poly.fit_transform(X_test)

#This finds the coefficient of polynomial regression. This is training part of the algorithm.

lin_regressor.fit(X_transform,Y_train)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

#Predict the value on the value passed
y_preds = lin_regressor.predict(X_test_)

#Printing the predicted value
print(y_preds)
#[1.34317343]