from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_squared_error, r2_score
import numpy as np

# Sample data
x = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([1.2, 1.9, 3.1, 4.5, 6.8])

# Define polynomial degree
degree = 2

# Create a pipeline to perform polynomial regression
model = Pipeline([
    ("poly_features", PolynomialFeatures(degree=degree, include_bias=False)),
    ("lin_reg", LinearRegression())
])

# Fit model
model.fit(x, y)

# Make predictions
y_pred = model.predict(x)

# Evaluate the model
mse = mean_squared_error(y, y_pred)
r2 = r2_score(y, y_pred)

print("Mean Squared Error:", mse)
print("R^2 Score:", r2)