Saving multiple different polynomial regression objects in python

I am trying to generate polynomial regressions of different degrees and save the model objects.

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error as MSE

df = pd.read_csv("kc_house_train_data(1).csv")

X = df['sqft_living'].values
Y = df['price'].values

lm = LinearRegression()

def Poly(X, degree):
    poly = PolynomialFeatures(degree = degree)
    poly_X = poly.fit_transform(X.reshape(-1,1))
    
    return poly_X

models = []

for i in range(15):
    poly_X = Poly(X, i+1)
    model = lm.fit(poly_X, Y)
    model.append(models)

Where lm is LinearRegression() from sklearn.

I end up getting a list of models, but all the models are the 15 degree polynomial. Not sure what I’m doing wrong.

edit: output of print(df[['sqft_living', 'price']].head(10).tostring()):

       sqft_living      price
0             1180   221900.0
1             2570   538000.0
2              770   180000.0
3             1960   604000.0
4             1680   510000.0
5             5420  1225000.0
6             1715   257500.0
7             1060   291850.0
8             1780   229500.0
9             1890   323000.0
10            3560   662500.0

>Solution :

You have to recreate your model at each iteration. Try:

models = []

for i in range(15):
    poly_X = Poly(X, i+1)
    lm = LinearRegression()
    model = lm.fit(poly_X, Y)
    models.append(model)

>>> models[0].n_features_in_
2

>>> models[1].n_features_in_
3

>>> models[2].n_features_in_
4