labels = X.columns
n = len(labels)
coeff = np.transpose(pca.components_)
pc1 = pca.components_[:, 0]
pc2 = pca.components_[:, 1]

plt.figure(figsize=(8, 8))

for i in range(n):
    plt.arrow(x=0, y=0, dx=coeff[i, 0], dy=coeff[i, 1], color="#000000", width=0.003, head_width=0.03)
    plt.text(x=coeff[i, 0] * 1.15, y=coeff[i, 1] * 1.15, s=labels[i], size=13, color="#000000", ha="center", va="center")

plt.axis("square")
plt.title(f"Wine Quality Dataset PCA Biplot", loc="left", fontdict={"weight": "bold"}, y=1.06)
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")

plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.xticks(np.arange(-1, 1.1, 0.2))
plt.yticks(np.arange(-1, 1.1, 0.2))

plt.axhline(y=0, color="black", linestyle="--")
plt.axvline(x=0, color="black", linestyle="--")
circle = plt.Circle((0, 0), 0.99, color="gray", fill=False)
plt.gca().add_artist(circle)

plt.grid()
plt.show()