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main.py
Ln 1, Col 1Spaces: 4
Python 3.13 (Pyodide)Hit Run to see output here.
What You Get
Classification & regression
Clustering
Built-in datasets
Model evaluation metrics
Scikit-Learn Examples
K-Means Clustering
from sklearn.cluster import KMeans
import numpy as np
np.random.seed(42)
X = np.vstack([np.random.randn(50, 2) + [i, j] for i, j in [(0,0), (5,5), (5,0)]])
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
labels = kmeans.fit_predict(X)
print(f"Cluster centers:\n{kmeans.cluster_centers_}")
print(f"Inertia: {kmeans.inertia_:.2f}")