¶ Classification and Regression: Building Predictive Models
In today’s data-driven business environment, predictive analytics plays a pivotal role in forecasting outcomes and making informed decisions. SAP Analytics Cloud (SAC) provides powerful machine learning capabilities that enable organizations to build classification and regression models—two fundamental types of predictive models used for different business problems.
This article explains the concepts of classification and regression, how to build these models in SAC, and their applications in the SAP ecosystem.
¶ Understanding Classification and Regression
Classification is a type of supervised machine learning where the goal is to predict a categorical outcome or class label based on input variables. Examples include:
- Predicting whether a customer will churn (Yes/No).
- Classifying products into categories.
- Determining if a transaction is fraudulent or not.
The output of a classification model is a discrete label or category.
Regression involves predicting a continuous numerical value based on one or more predictors. It’s used when the target variable is quantitative, such as:
- Forecasting sales revenue.
- Estimating delivery times.
- Predicting customer lifetime value.
The output of regression models is a continuous number.
SAC’s Smart Predict service simplifies the creation of classification and regression models without requiring deep data science expertise.
- Import relevant datasets into SAC, combining SAP and non-SAP data sources if needed.
- Cleanse and enrich data to ensure quality.
- Select features (input variables) relevant to the prediction.
- Choose Classification when the target variable is categorical.
- Choose Regression when the target variable is numeric.
SAC automatically suggests suitable algorithms and model parameters.
- Train the model on historical data.
- SAC performs automatic feature engineering and model tuning.
- The platform provides model accuracy metrics, such as accuracy, precision, recall for classification, and R-squared for regression.
- Review model performance reports.
- Perform cross-validation to ensure robustness.
- Adjust features or retrain if needed.
- Integrate the predictive model into SAC stories and dashboards.
- Use model predictions for scenario planning, alerts, or decision support.
- Continuously monitor and retrain models with new data.
¶ Use Cases of Classification and Regression in SAP Analytics Cloud
- Customer Churn Prediction: Identify customers at risk of leaving and proactively engage them.
- Lead Scoring: Classify sales leads as high or low priority.
- Fraud Detection: Flag suspicious transactions in finance or procurement.
- Sales Forecasting: Predict future sales volume for accurate inventory planning.
- Demand Planning: Estimate product demand to optimize supply chains.
- Cost Estimation: Predict project costs based on historical data.
- User-Friendly: No need for coding skills; intuitive interface guides model building.
- Integrated Platform: Combines data preparation, modeling, and visualization in one place.
- Automated Machine Learning: Smart Predict handles complex tasks like feature selection and tuning.
- Real-Time Insights: Embed predictions directly into business dashboards.
- Collaboration: Share models and insights across teams to drive unified decision-making.
- Start with Clean, Relevant Data: Quality input is critical for reliable models.
- Define Clear Business Questions: Align models with specific decision-making needs.
- Monitor Model Performance: Retrain models regularly as data and conditions change.
- Combine with What-If Analysis: Use predictions in scenario planning for deeper insights.
- Leverage SAP Ecosystem: Integrate predictive outputs with other SAP modules for end-to-end process improvement.
Classification and regression are foundational techniques in predictive analytics that enable businesses to forecast categorical outcomes and continuous variables respectively. SAP Analytics Cloud’s Smart Predict simplifies the creation and deployment of these models, empowering organizations to unlock actionable insights and drive smarter decisions.
By incorporating predictive modeling into your analytics strategy, you can better anticipate risks, identify opportunities, and optimize operations across your SAP landscape.