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unit 4/01_Classification_Basics.md

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+# 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.