exp_var = [val * 100 for val in pca.explained_variance_ratio_]
plot_y = [sum(exp_var[:i+1]) for i in range(len(exp_var))]
plot_x = range(1, len(plot_y) + 1)

plt.plot(plot_x, plot_y, marker="o", color="#9B1D20")
for x, y in zip(plot_x, plot_y):
    plt.text(x, y + 1.5, f"{y:.1f}%", ha="center", va="bottom")

plt.xlabel("Principal Component")
plt.ylabel("Cumulative Percentage of Explained Variance")
plt.title("Cumulative Variance Explained per Principal Component", loc="left", fontdict={"weight": "bold"}, y=1.06)

plt.yticks(range(0, 101, 5))
plt.grid(axis="y")
plt.xticks(plot_x)

plt.show()