# import libraries
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
from sklearn.cluster import DBSCAN
from sklearn.preprocessing import StandardScaler

# Generate non-linear, real-world like unlabeled data
X, _ = make_moons(
    n_samples=500,
    noise=0.08,
    random_state=42
)

# Feature scaling (important for DBSCAN)
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Apply DBSCAN
dbscan = DBSCAN(eps=0.3, min_samples=5)
clusters = dbscan.fit_predict(X_scaled)

# Visualizing clusters
plt.figure(figsize=(8, 6))
plt.scatter(
    X_scaled[:, 0],
    X_scaled[:, 1],
    c=clusters,
    cmap='rainbow',
    s=60
)

plt.title("DBSCAN Clustering on Unlabeled Data")
plt.xlabel("Feature 1 (scaled)")
plt.ylabel("Feature 2 (scaled)")
plt.show()

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