Linear Regression Plot

Followers vs Engagement Rate

Social media analytics and influence metrics

Output
Followers vs Engagement Rate
Python
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

np.random.seed(413)
BG_COLOR = '#ffffff'
TEXT_COLOR = '#1f2937'

n = 70
followers = np.random.uniform(1, 500, n)
engagement = 8 - 0.01 * followers + np.random.normal(0, 1.2, n)
engagement = np.clip(engagement, 0.5, 12)

df = pd.DataFrame({'Followers (K)': followers, 'Engagement Rate (%)': engagement})

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

sns.regplot(
    data=df,
    x='Followers (K)',
    y='Engagement Rate (%)',
    scatter_kws={'color': '#F5276C', 'alpha': 0.6, '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.axhline(3.0, color='#22c55e', linestyle='--', alpha=0.5, linewidth=1)
ax.text(480, 3.2, 'Good', color='#22c55e', fontsize=9, ha='right')

corr = np.corrcoef(followers, engagement)[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='#f8fafc', edgecolor='#e5e7eb'))

ax.set_xlabel('Followers (K)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Engagement Rate (%)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Social Media Followers vs Engagement', fontsize=14, color=TEXT_COLOR, fontweight='bold', pad=15)

ax.tick_params(colors='#374151', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#e5e7eb')

plt.tight_layout()
plt.show()
Library

Matplotlib

Category

Pairwise Data

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