Printing results
- Machine Learning course
- Intermediate
- Lesson 68/112
- 7 min
Print clustering results.
The results report
The report includes, for each segment, a name, its size and a profile in original units. An example showing which segment new customers fall into makes the use of the system concrete:
yeni = np.array([[1400, 3, 1400 / 3]])
segment = km.predict(sc.transform(yeni))[0]
print(f"Yeni müşteri: {adlar[segment]}")The new customer must be transformed with the same scaler used in training.
Limitations
An honest report also states limitations: the segments rely only on the three features used; there is no seasonal behaviour, age or channel information. KMeans assumes round clusters; the real customer distribution may differ. Segments drift over time; the model should be retrained periodically.
# monitoring suggestion: record segment sizes every month
# a big change = behaviour drift -> reclusterClustering is an exploration tool; its results need validating with the first campaigns.
Practice Write the report
Derive the segment names from the profiles: the segment with the most visits is "sadık", the one with the biggest basket "büyük sepetçi", the remaining one "ara sıra". Print the line f"{ad}: {boyut} müşteri" for each segment in segment-number order. Then print the segment name of the customer [[1400, 3, 1400 / 3]].
Starter code
import numpy as np
rng = np.random.default_rng(5)
n = 400
grup = rng.integers(0, 3, n)
harcama = np.array([300, 1500, 800])[grup] + rng.normal(0, 120, n)
ziyaret = np.array([2, 4, 12])[grup] + rng.normal(0, 1.2, n)
sepet = harcama / np.maximum(ziyaret, 1)
X = np.column_stack([harcama, ziyaret, sepet])
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
sc = StandardScaler().fit(X)
km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(sc.transform(X))
# buraya yazSample solution
import numpy as np
rng = np.random.default_rng(5)
n = 400
grup = rng.integers(0, 3, n)
harcama = np.array([300, 1500, 800])[grup] + rng.normal(0, 120, n)
ziyaret = np.array([2, 4, 12])[grup] + rng.normal(0, 1.2, n)
sepet = harcama / np.maximum(ziyaret, 1)
X = np.column_stack([harcama, ziyaret, sepet])
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
sc = StandardScaler().fit(X)
km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(sc.transform(X))
ort = np.array([X[km.labels_ == k].mean(axis=0) for k in range(3)])
adlar = ["ara sıra"] * 3
adlar[ort[:, 1].argmax()] = "sadık"
adlar[ort[:, 2].argmax()] = "büyük sepetçi"
for k, boyut in enumerate(np.bincount(km.labels_)):
print(f"{adlar[k]}: {boyut} müşteri")
print(adlar[km.predict(sc.transform([[1400, 3, 1400 / 3]]))[0]])Expected output
ara sıra: 139 müşteri büyük sepetçi: 133 müşteri sadık: 128 müşteri büyük sepetçi
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 What happens if you give a new customer to <code>km.predict</code> without scaling?
- It works correctly
- It errors
- A new cluster is opened
- It is assigned to the wrong segment Correct answer
Explanation: The centres are in the scaled space; raw values are a very distant point in that space.
2 Transform the new customer with the training scaler.
segment = km.predict(sc.____(yeni))[0]segment = km.predict(sc.transform(yeni))[0]Explanation: Only transform is used on new data.
3 Match the report parts.
| Term | Meaning |
|---|---|
segment name |
Derived from the profile |
dimension |
bincount |
new customer |
sc.transform + predict |
limitation |
Missing features, drift |