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Training KMeans

Train a KMeans model.

Choosing k and training

For k we'll compare values 2-6 with the silhouette score. A sensible number is also sought from the business side: 2 segments are too coarse for marketing, 8 segments unmanageable.

for k in range(2, 7):
    km = KMeans(n_clusters=k, n_init=10, random_state=0).fit(Xs)
    print(k, round(silhouette_score(Xs, km.labels_), 3))

When the technical criterion and the business need conflict, both must be weighed; the highest silhouette means nothing if the segments can't be used.

Stability check

An ARI comparison across different seeds confirms the chosen k is stable. The segment sizes are also checked: a segment with a single customer, or with 90% of customers, is unusable.

km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(Xs)
print(np.bincount(km.labels_))   # segment sizes

Balanced, stable segments make campaigns reach meaningful groups.

Practice Choose k

Train KMeans (n_init=10, random_state=0) on the scaled data for k = 2-5. On each line print k, the silhouette score with round(..., 3) and the size of the smallest segment (np.bincount(km.labels_).min()). Weigh the silhouette and the segment size together.

Starter code

model.py
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
Xs = StandardScaler().fit_transform(X)
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# buraya yaz

Sample solution

model.py
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
Xs = StandardScaler().fit_transform(X)
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

for k in range(2, 6):
    km = KMeans(n_clusters=k, n_init=10, random_state=0).fit(Xs)
    print(k, round(silhouette_score(Xs, km.labels_), 3), np.bincount(km.labels_).min())

Expected output

2 0.501 141
3 0.699 128
4 0.711 12
5 0.659 12

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 If the silhouette is almost equal for k=3 and k=4, which is preferred?

  • Both are rejected
  • The more interpretable for the business, usually fewer clusters Correct answer
  • Always the larger
  • A random choice

Explanation: Fewer segments are easier to explain and act on.

2 Count the segment sizes.

Question 
print(np.____(km.labels_))
Answer 
print(np.bincount(km.labels_))

Explanation: bincount gives how many times each cluster number appears.

3 Match the checks.

Term Meaning
silhouette Separation quality
ARI across seeds Stability
bincount Segment sizes
business need Interpretability

All lessons in Intermediate

1 scikit-learn basics

  1. What is scikit-learn?
  2. Estimator API
  3. The fit method
  4. The predict method
  5. The score method
  6. Summary - scikit-learn

2 Pipeline

  1. What is a Pipeline?
  2. Steps
  3. fit and predict
  4. Advantages
  5. ColumnTransformer
  6. Summary - Pipeline

3 Cross-validation

  1. What is cross-validation?
  2. K-fold
  3. cross_val_score
  4. Stratified
  5. Advantages
  6. Summary - Cross-validation

4 Grid search

  1. What is grid search?
  2. GridSearchCV
  3. param_grid
  4. best_params_
  5. best_score_
  6. Summary - Grid search

5 SVM

  1. What is SVM?
  2. Margin
  3. Kernel
  4. C parameter
  5. Classification
  6. Summary - SVM

6 Project: SVM

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

7 Random forest

  1. What is random forest?
  2. Ensemble
  3. n_estimators
  4. Bagging
  5. Feature importance
  6. Summary - Random forest

8 Boosting

  1. What is boosting?
  2. AdaBoost
  3. Gradient boosting
  4. Learning rate
  5. Advantages
  6. Summary - Boosting

9 Clustering

  1. What is clustering?
  2. KMeans
  3. Centroid
  4. Elbow method
  5. Labels
  6. Summary - Clustering

10 KMeans in depth

  1. KMeans algorithm
  2. Initialization
  3. n_init
  4. inertia_
  5. random_state
  6. Summary - KMeans

11 Hierarchical clustering

  1. What is hierarchical clustering?
  2. Dendrogram
  3. Agglomerative
  4. Linkage
  5. Advantages
  6. Summary - Hierarchical clustering

12 Project: Clustering

  1. Preparing data
  2. Training KMeans
  3. Label analysis
  4. Printing results

13 PCA

  1. What is PCA?
  2. Variance
  3. n_components
  4. fit_transform
  5. explained_variance_
  6. Summary - PCA

14 Dimensionality reduction

  1. What is dimensionality reduction?
  2. Why reduce?
  3. PCA vs t-SNE
  4. Visualization
  5. Advantages
  6. Summary - Dimensionality reduction

15 Feature selection

  1. What is feature selection?
  2. Variance threshold
  3. SelectKBest
  4. Recursive elimination
  5. Feature importance
  6. Summary - Feature selection

16 Imbalanced data

  1. What is imbalanced data?
  2. SMOTE
  3. Class weight
  4. Oversampling
  5. Metric selection
  6. Summary - Imbalanced data

17 Project: Features

  1. Feature extraction
  2. Feature selection
  3. Training model
  4. Evaluation

18 Ensemble methods

  1. What is ensemble?
  2. Voting
  3. Bagging
  4. Boosting
  5. Stacking
  6. Summary - Ensemble

19 Regularization

  1. What is regularization?
  2. L1 (Lasso)
  3. L2 (Ridge)
  4. alpha
  5. ElasticNet
  6. Summary - Regularization

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4