# Complex demo: Seaborn pairplot with regression, annotations, and customization for AI data
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

def advanced_pairplot(data):
    df = pd.DataFrame(data)
    g = sns.pairplot(df, hue="Category", diag_kind="kde", corner=True, plot_kws={"alpha": 0.5}, diag_kws={"shade": True})
    g.fig.suptitle("AI Feature Pairplot", y=1.02)
    g.add_legend()
    plt.annotate("High Correlation", xy=(0.5, 0.5), xytext=(0.6, 0.6), arrowprops=dict(facecolor='black', shrink=0.05))
    plt.show()

# Generate complex AI-like data
np.random.seed(42)
x = np.random.randn(100)
y = 2 * x + np.random.randn(100)
z = x + y + np.random.randn(100)
category = np.random.choice(['A', 'B'], 100)
data = {"X": x, "Y": y, "Z": z, "Category": category}
advanced_pairplot(data)