🎯 02 — K-Means

K-Means groups data into k clusters by minimizing distance to cluster centers.

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Example

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("model", KMeans(n_clusters=3, random_state=42, n_init="auto"))
])

clusters = pipe.fit_predict(X)
df["cluster"] = clusters

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Choosing K

Use inertia/elbow method:

inertias = []

for k in range(1, 10):
    model = KMeans(n_clusters=k, random_state=42, n_init="auto")
    model.fit(StandardScaler().fit_transform(X))
    inertias.append(model.inertia_)

Plot k vs inertia and look for the elbow.

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Cluster Profiling

df.groupby("cluster")[["spend", "visits", "tenure"]].mean()

This is where clustering becomes useful.

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Next

➡️ 03-hierarchical-clustering