✅ Everything about Unsupervised Learning 🤖📈

It's a machine learning method where the model works with unlabeled data.

No output labels are given — the algorithm tries to find patterns, structure, or groupings on its own.

Use Case:
Suppose you have customer data (age, purchase history, location), but no info on customer types.
Unsupervised learning will group similar customers — without you telling it who is who.

Key Tasks in Unsupervised Learning:

1. Clustering
→ Group similar data points
→ Example: Customer segmentation
→ Algorithm: K-Means, Hierarchical Clustering

2. Dimensionality Reduction
→ Reduce features while preserving patterns
→ Helps in visualization & speeding up training
→ Algorithm: PCA (Principal Component Analysis), t-SNE

Example Dataset (Unlabeled):

| Age | Spending Score |
| --- | -------------- |
| 22  | 90             |
| 45  | 20             |
| 25  | 85             |
| 48  | 25             |

The model may group rows 1 & 3 as one cluster (young, high spenders) and rows 2 & 4 as another.

Python Code (K-Means):

  
from sklearn.cluster import KMeans  

X = [[22, 90], [45, 20], [25, 85], [48, 25]]  
model = KMeans(n_clusters=2)  
model.fit(X)  
print(model.labels_)  # Output: [0 1 0 1] → Two clusters  

Summary:

⦁ No labels, only input features
⦁ Model discovers structure or patterns
⦁ Great for grouping, compression, and insights

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