# 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.