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Label analysis

Analyze cluster labels.

Segment profiles

Clusters are only valuable once interpreted. The means of each segment in original units are computed and the segments are named:

for k in range(3):
    uye = X[km.labels_ == k]
    print(k, len(uye), uye.mean(axis=0).round(0))
# e.g. low spending + few visits  -> "occasional"
#      high spending + few visits -> "big basket"
#      medium spending + many visits -> "loyal frequent"

Means in original units are reported rather than scaled centres; the marketing team talks TL, not z-scores.

Tying segment names to code

Because cluster numbers may change on every run, names aren't hard-coded to numbers; they are derived from the centres' properties. For example, the cluster with the highest mean visits is picked as "loyal":

ort = np.array([X[km.labels_ == k].mean(axis=0) for k in range(3)])
sadik = ort[:, 1].argmax()           # highest visits
buyuk = ort[:, 2].argmax()           # biggest basket
print(sadik, buyuk)

This approach prevents names from drifting to the wrong cluster when the model is retrained.

Practice Extract the profiles

Collect each segment's means in original units in the array ort and print it with np.round(ort, 0). Then print the numbers of the segments with the most visits and the biggest basket on one line.

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
km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(Xs)

# 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
km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(Xs)

ort = np.array([X[km.labels_ == k].mean(axis=0) for k in range(3)])
print(np.round(ort, 0))
print(ort[:, 1].argmax(), ort[:, 2].argmax())

Expected output

[[ 303.    2.  181.]
 [1505.    4.  422.]
 [ 787.   12.   65.]]
2 1

Questions and answers

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

1 What is the problem with reporting the centres in scaled data?

  • The values can't be read in TL or visits Correct answer
  • They are wrong
  • They can't be computed
  • There is no problem

Explanation: z-scores describe relative position; business language needs the original units.

2 Find the segment with the highest mean visits.

Question 
sadik = ort[:, 1].____()
Answer 
sadik = ort[:, 1].argmax()

Explanation: argmax gives the cluster number, not the value.

3 Match the profiles with the names.

Term Meaning
Low spend, few visits Occasional
High spend, few visits Big basket
Medium spend, many visits Loyal frequent
ort[:, 1].argmax() Tying the name

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