Annotated Correlation Matrix

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib.colors import LinearSegmentedColormap

np.random.seed(42)

# Create correlated features
n = 100
features = {
    'Revenue': np.random.randn(n),
    'Marketing': np.random.randn(n),
    'Customers': np.random.randn(n),
    'Satisfaction': np.random.randn(n),
    'Retention': np.random.randn(n),
    'Support_Tickets': np.random.randn(n),
    'NPS': np.random.randn(n),
}

# Add correlations
features['Marketing'] = features['Revenue'] * 0.7 + np.random.randn(n) * 0.3
features['Customers'] = features['Revenue'] * 0.8 + np.random.randn(n) * 0.2
features['NPS'] = features['Satisfaction'] * 0.85 + np.random.randn(n) * 0.15
features['Retention'] = features['Satisfaction'] * 0.6 + np.random.randn(n) * 0.4

df = pd.DataFrame(features)
corr = df.corr()

# Custom diverging NEON colormap
neon_div = LinearSegmentedColormap.from_list('neon_div', ['#27D3F5', '#ffffff', '#F5276C'])

fig, ax = plt.subplots(figsize=(9, 6), facecolor='#ffffff')
ax.set_facecolor('#ffffff')

mask = np.triu(np.ones_like(corr, dtype=bool), k=1)
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f', cmap=neon_div,
            center=0, vmin=-1, vmax=1, square=True, linewidths=2, linecolor='#ffffff',
            annot_kws={'size': 10, 'color': '#1f2937', 'fontweight': '500'},
            cbar_kws={'shrink': 0.8, 'label': 'Correlation'}, ax=ax)

ax.set_title('Feature Correlation Matrix', color='#1f2937', fontsize=14, fontweight='bold', pad=15)
ax.tick_params(colors='#374151', labelsize=10)

plt.tight_layout()
plt.show()