🟪 1-Minute Summary
Clustering is unsupervised learning that groups similar data points together without predefined labels. Common algorithms: K-Means (fast, needs K), Hierarchical (dendrogram, no K needed), DBSCAN (density-based, finds arbitrary shapes). Use cases: customer segmentation, anomaly detection, data exploration. Unlike supervised learning, there’s no “correct” answer - evaluate with silhouette score, elbow method, domain knowledge.
🟦 Core Notes (Must-Know)
What is Clustering?
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Supervised vs Unsupervised Learning
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Common Clustering Algorithms
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Use Cases
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Evaluation Metrics
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“When would you use clustering vs classification?”
- [Answer: Clustering when no labels, classification when you have labels]
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“How do you evaluate a clustering model?”
- [Answer: Silhouette score, elbow method, business validation]
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“Name 3 clustering algorithms and when to use each”
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🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Not scaling features before clustering
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Mistake 2: Using accuracy for clustering
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🟩 Mini Example (Quick Application)
Scenario
[Customer segmentation example]
Solution
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
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