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unit 1/02_Data_Mining_Process.md

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+# The Data Mining Process
+
+How do we actually do data mining? It follows a standard process (often similar to CRISP-DM).
+
+## Steps in the Process
+1. **Define the Goal**: What do you want to achieve? (e.g., Increase sales, detect fraud).
+2. **Gather Data**: Collect data from databases, logs, etc.
+3. **Cleanse Data**: Fix errors, remove duplicates, and handle missing values.
+4. **Interrogate Data**: Explore the data (charts, graphs) to find initial patterns.
+5. **Build a Model**: Use algorithms (like decision trees or regression) to find the solution.
+6. **Validate Results**: Check if the model is accurate.
+7. **Implement**: Use the insights in the real world.
+
+## Data Mining Functionalities
+Tasks are generally divided into two types:
+
+### 1. Descriptive Mining
+- Describes what is in the data.
+- Finds patterns and relationships.
+- *Examples*: Clustering, Association Rules.
+
+### 2. Predictive Mining
+- Predicts future or unknown values.
+- *Examples*: Classification, Regression, Prediction.