addition of unit 1 3 4 5

unit 1/03_Data_Mining_Techniques.md

@@ -0,0 +1,28 @@
+# 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).