[Python] How to interpret the Coefficient of nonlinear regression in scikit learn?

I am using the following code to do a nonlinear regression, and I am getting an intercept & 20 coefficients. How to interpret those coefficients to be able to construct a formula Y = intercept + coeff*feat_a + .....?

data = pd.read_csv("data.csv")

# Step 2: Preprocess the data
X = data[['feat_a', 'feat_b', 'feat_c', 'feat_d', 'feat_e' ]] # Independent variables including blue (b)
y = data['Y']

#splitting Train and Test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101)

# Dependent variable

# Step 3: Fit a polynomial regression model
degree = 2 # Adjust the degree of the polynomial as needed
poly_features = PolynomialFeatures(degree=degree, include_bias=False)
X_poly = poly_features.fit_transform(X_train)

model = LinearRegression()
model.fit(X_poly, y_train)

# Step 4: Evaluate the model (optional)

# Step 5: Extract the formula and parameters
intercept = model.intercept_
coefficients = model.coef_

print("coeff size{}".format(len(coefficients)))
print(coefficients)

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