Predictive Analytics Using Oracle Data Mining — Lesson 2: Feature Preparation, Classification, Regression, and Algorithm Selection

Learning Objectives

• Explain the purpose and operating context of transformations, derived features, categorical encoding, and sampling.
• Apply classification algorithms, probabilities, costs, and ranking and regression algorithms, numeric forecasts, residuals, and error patterns in a controlled environment.
• Validate outcomes and troubleshoot risks involving algorithm assumptions, settings, tuning, and reproducible experiments.

Detailed Topics

• transformations, derived features, categorical encoding, and sampling
• classification algorithms, probabilities, costs, and ranking
• regression algorithms, numeric forecasts, residuals, and error patterns
• algorithm assumptions, settings, tuning, and reproducible experiments

Instructor Demonstration

Demonstrate transformations, derived features, categorical encoding, and sampling for a retention campaign supported by an Oracle predictive model with measurable lift and controlled scoring; capture the exact configuration, code, command, transaction, query, dashboard, model, schedule, or report evidence that proves the expected result.

Hands-On Exercise

Complete a controlled Predictive Analytics Using Oracle Data Mining exercise covering classification algorithms, probabilities, costs, and ranking and regression algorithms, numeric forecasts, residuals, and error patterns; introduce an exception involving algorithm assumptions, settings, tuning, and reproducible experiments, diagnose it from evidence, apply the smallest reversible correction, and rerun acceptance checks.

1. Confirm scope, prerequisites, identities, dependencies, data, risks, and expected outcome.
2. Perform the course-specific configuration, development, administration, analysis, or business-process workflow.
3. Capture commands, settings, records, logs, screenshots, reports, metrics, or test results.
4. Compare the observed result with acceptance criteria and document corrective or preventive action.

Scenario Analysis

• Identify the technical or business decision and the stakeholders affected by it.
• Compare security, performance, reliability, compliance, cost, usability, and support trade-offs.
• Define rollback, recovery, communication, evidence, ownership, and escalation requirements.

Troubleshooting Guidance

Diagnose issues involving transformations, derived features, categorical encoding, and sampling, classification algorithms, probabilities, costs, and ranking, regression algorithms, numeric forecasts, residuals, and error patterns, algorithm assumptions, settings, tuning, and reproducible experiments without making an unverified production change. Separate symptoms from causes, gather time-correlated evidence, test a reversible hypothesis, validate the correction, and record recurrence controls.

Validation Checklist

• The requirement, environment, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by product-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, support actions, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Feature Preparation, Classification, Regression, and Algorithm Selection in a realistic Predictive Analytics Using Oracle Data Mining scenario and defend the result with documented evidence.