Engine Size vs Fuel Economy

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

np.random.seed(409)
BG_COLOR = '#0a0a0f'
TEXT_COLOR = 'white'

n = 80
engine = np.random.uniform(1.0, 6.0, n)
mpg = 45 - 5 * engine + np.random.normal(0, 4, n)

df = pd.DataFrame({'Engine Size (L)': engine, 'MPG': mpg})

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

sns.regplot(
    data=df,
    x='Engine Size (L)',
    y='MPG',
    scatter_kws={'color': '#F54927', 'alpha': 0.7, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#6CF527', 'linewidth': 2.5},
    ci=95,
    ax=ax
)

for collection in ax.collections[1:]:
    collection.set_facecolor('#6CF527')
    collection.set_alpha(0.15)

corr = np.corrcoef(engine, mpg)[0, 1]
ax.text(0.95, 0.95, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
        color='#6CF527', fontweight='bold', va='top', ha='right',
        bbox=dict(boxstyle='round,pad=0.3', facecolor='#1a1a2e', edgecolor='#333'))

ax.set_xlabel('Engine Size (Liters)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Fuel Economy (MPG)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Engine Size vs Fuel Economy', 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()