# Data Mining Techniques

There are several key techniques used to mine data.

## 1. Classification (Predictive)
- **Goal**: Assign items to predefined categories (classes).
- **Supervised Learning**: We know the categories beforehand.
- **Example**: Is this email **Spam** or **Not Spam**?

## 2. Regression (Predictive)
- **Goal**: Predict a continuous **number**.
- **Example**: Predicting the **price** of a house based on its size and location.

## 3. Clustering (Descriptive)
- **Goal**: Group similar items together.
- **Unsupervised Learning**: We don't know the groups beforehand.
- **Example**: Grouping customers into segments (e.g., "High Spenders", "Budget Shoppers").

## 4. Association Rules (Descriptive)
- **Goal**: Find relationships between items.
- **Market Basket Analysis**: "People who buy Bread often also buy Butter."
- **Key Terms**:
  - **Support**: How often items appear together.
  - **Confidence**: How likely item B is purchased if item A is purchased.

## 5. Outlier Detection
- **Goal**: Find unusual data points that don't fit the pattern.
- **Example**: Detecting credit card fraud (a huge transaction in a usually quiet account).