House Size vs Sale Price

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

np.random.seed(404)
BG_COLOR = '#0a0a0f'
TEXT_COLOR = 'white'

n = 75
sqft = np.random.uniform(800, 4000, n)
price = 50 + 0.15 * sqft + np.random.normal(0, 50, n)

df = pd.DataFrame({'Square Feet': sqft, 'Price ($K)': price})

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

sns.regplot(
    data=df,
    x='Square Feet',
    y='Price ($K)',
    scatter_kws={'color': '#F5D327', 'alpha': 0.7, 's': 55, 'edgecolor': 'white', 'linewidths': 0.5},
    line_kws={'color': '#F5276C', 'linewidth': 2.5},
    ci=95,
    ax=ax
)

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

corr = np.corrcoef(sqft, price)[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'))

slope = np.polyfit(sqft, price, 1)[0]
ax.text(0.05, 0.85, '$%.0f/sqft' % (slope * 1000), transform=ax.transAxes, fontsize=11,
        color='#F5D327', va='top')

ax.set_xlabel('Square Feet', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_ylabel('Price ($K)', fontsize=12, color=TEXT_COLOR, fontweight='500')
ax.set_title('House Size vs Sale Price', 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()