Website Speed vs Conversion Rate

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

np.random.seed(407)
BG_COLOR = '#0a0a0f'
TEXT_COLOR = 'white'

n = 65
load_time = np.random.uniform(0.5, 8, n)
conversion = 8 - 0.9 * load_time + np.random.normal(0, 0.8, n)
conversion = np.clip(conversion, 0, 10)

df = pd.DataFrame({'Load Time (s)': load_time, 'Conversion Rate (%)': conversion})

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

sns.regplot(
    data=df,
    x='Load Time (s)',
    y='Conversion Rate (%)',
    scatter_kws={'color': '#F5276C', 'alpha': 0.7, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#27D3F5', 'linewidth': 2.5},
    ci=95,
    ax=ax
)

for collection in ax.collections[1:]:
    collection.set_facecolor('#27D3F5')
    collection.set_alpha(0.15)

ax.axvline(2.5, color='#22c55e', linestyle='--', alpha=0.5, linewidth=1)
ax.text(2.6, 1, 'Good', color='#22c55e', fontsize=9, rotation=90)

corr = np.corrcoef(load_time, conversion)[0, 1]
ax.text(0.95, 0.95, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
        color='#27D3F5', fontweight='bold', va='top', ha='right',
        bbox=dict(boxstyle='round,pad=0.3', facecolor='#1a1a2e', edgecolor='#333'))

ax.set_xlabel('Page Load Time (seconds)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Conversion Rate (%)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Website Speed vs Conversion', 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()