total_explained_variance = sum(pca.explained_variance_ratio_[:3]) * 100
colors = ["#1C3041", "#9B1D20", "#0B6E4F", "#895884", "#F07605", "#F5E400"]

pca_3d_df = pd.DataFrame(pca_res[:, :3], columns=["PC1", "PC2", "PC3"])
pca_3d_df["y"] = data["quality"]

fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(projection="3d")

for i, target in enumerate(sorted(pca_3d_df["y"].unique())):
    subset = pca_3d_df[pca_3d_df["y"] == target]
    ax.scatter(xs=subset["PC1"], ys=subset["PC2"], zs=subset["PC3"], s=70, alpha=0.7, c=colors[i], edgecolors="#000000", label=target)

ax.set_xlabel("Principal Component 1")
ax.set_ylabel("Principal Component 2")
ax.set_zlabel("Principal Component 3")
ax.set_title(f"Wine Quality Dataset PCA ({total_explained_variance:.2f}% Explained Variance)", loc="left", fontdict={"weight": "bold"})

ax.legend(title="Wine quality", loc="lower left")
plt.show()