Mining Model for Prediction
This document provides a comprehensive overview of how to create and use mining models for prediction in SQL Server Analysis Services (SSAS). Predictive mining models are designed to forecast future events or values based on historical data. This involves selecting an appropriate mining algorithm, defining the mining structure, and then training the model.
Understanding Predictive Models
Predictive models help answer questions like:
- Which customers are most likely to churn?
- What is the probability that a transaction will be fraudulent?
- How much revenue can we expect next quarter?
- Which products will a customer likely buy next?
Common algorithms used for prediction include Linear Regression, Logistic Regression, Decision Trees, and Neural Networks.
Creating a Predictive Mining Model
The process typically involves the following steps:
- Define the Mining Structure: This involves specifying the data source, selecting columns (predictable and content), and choosing the appropriate mining algorithm.
- Train the Model: Using the defined structure and historical data, SSAS trains the algorithm to identify patterns and relationships.
- Browse and Inspect the Model: Once trained, you can visualize the model to understand its logic and identify key influencing factors.
- Make Predictions: Use the trained model to predict outcomes for new or existing data.
Example: Predicting Customer Churn
Let's consider predicting customer churn. We might use a Decision Tree algorithm. The predictable column would be a binary indicator of whether a customer has churned or not.
The mining structure would include demographic information, usage patterns, customer service interactions, and contract details as content columns.
DMX for Prediction
Data Mining Extensions (DMX) is a query language used with SSAS. Here's a basic example of how to predict churn probability for a specific customer:
SELECT
[Customer].[CustomerID],
[TargetMail].[PROBABILITY(1)] AS ChurnProbability
FROM
[MyPredictionModel]
PREDICTION JOIN
OPENQUERY(AdventureWorksDW,
'SELECT CustomerID FROM DimCustomer WHERE CustomerID = 12345'
)
AS t ON [MyPredictionModel].[CustomerID] = t.CustomerID
Model Evaluation
After training, it's crucial to evaluate the model's accuracy and effectiveness. SSAS provides tools and metrics for this purpose, such as confusion matrices, accuracy charts, and lift charts.
Key Considerations
- Algorithm Selection: Choose an algorithm that best suits the type of prediction problem.
- Data Quality: High-quality, relevant data is paramount for accurate predictions.
- Feature Engineering: Creating new features from existing data can enhance model performance.
- Model Validation: Regularly validate your models to ensure they remain effective over time.