Experience Years vs Salary

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

np.random.seed(406)
BG_COLOR = '#0a0a0f'
TEXT_COLOR = 'white'

n = 85
experience = np.random.uniform(0, 25, n)
salary = 45 + 5.5 * experience + np.random.normal(0, 15, n)

df = pd.DataFrame({'Experience (years)': experience, 'Salary ($K)': salary})

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

sns.regplot(
    data=df,
    x='Experience (years)',
    y='Salary ($K)',
    scatter_kws={'color': '#6CF527', 'alpha': 0.7, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#5314E6', 'linewidth': 2.5},
    ci=95,
    ax=ax
)

for collection in ax.collections[1:]:
    collection.set_facecolor('#5314E6')
    collection.set_alpha(0.15)

corr = np.corrcoef(experience, salary)[0, 1]
ax.text(0.05, 0.95, 'r = %.3f' % corr, transform=ax.transAxes, fontsize=12,
        color='#5314E6', fontweight='bold', va='top',
        bbox=dict(boxstyle='round,pad=0.3', facecolor='#1a1a2e', edgecolor='#333'))

slope = np.polyfit(experience, salary, 1)[0]
ax.text(0.05, 0.85, '+$%.1fK/year' % slope, transform=ax.transAxes, fontsize=11,
        color='#6CF527', va='top')

ax.set_xlabel('Years of Experience', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Annual Salary ($K)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('Experience vs Salary', 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()