Revenue vs Marketing Spend Correlation

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

np.random.seed(401)
BG_COLOR = '#0a0a0f'
TEXT_COLOR = 'white'

# Generate correlated data
n = 80
marketing = np.random.uniform(10, 100, n)
revenue = 2.5 * marketing + np.random.normal(0, 20, n) + 50

df = pd.DataFrame({'Marketing Spend ($K)': marketing, 'Revenue ($K)': revenue})

fig, ax = plt.subplots(figsize=(10, 6), facecolor=BG_COLOR)
ax.set_facecolor(BG_COLOR)

sns.regplot(
    data=df,
    x='Marketing Spend ($K)',
    y='Revenue ($K)',
    scatter_kws={'color': '#27D3F5', 'alpha': 0.7, 's': 60, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#F5276C', 'linewidth': 2.5, 'alpha': 0.9},
    ci=95,
    ax=ax
)

# Style confidence interval
for collection in ax.collections[1:]:
    collection.set_facecolor('#F5276C')
    collection.set_alpha(0.15)

corr = np.corrcoef(marketing, revenue)[0, 1]
ax.text(0.05, 0.95, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
        color='#F5276C', fontweight='bold', va='top',
        bbox=dict(boxstyle='round,pad=0.3', facecolor='#1a1a2e', edgecolor='#333'))

ax.set_xlabel('Marketing Spend ($K)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Revenue ($K)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Revenue vs Marketing Spend', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#888', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#333')

plt.tight_layout()
plt.show()