# Classification Basics

## What is Classification?
**Classification** is the process of predicting the **class label** of a data item.
- **Goal**: To assign a category to a new item based on past data.
- **Example**:
  - Input: A bank loan application.
  - Output Class: "Safe" or "Risky".

## Classification vs Prediction
- **Classification**: Predicts a **category** (Discrete value).
  - *Example*: Yes/No, Red/Blue/Green.
- **Prediction (Regression)**: Predicts a **number** (Continuous value).
  - *Example*: Predicting the price of a house ($500k, $505k...).

## The Process
1. **Training Phase (Learning)**:
   - The algorithm learns from a "Training Set" where the correct answers (labels) are known.
   - It builds a **Model** (e.g., a Decision Tree).
2. **Testing Phase (Classification)**:
   - The model is tested on new, unseen data ("Test Set").
   - We check the **Accuracy**: Percentage of correct predictions.