The Data Mining Pipeline
Successful analytics doesn’t start with algorithms - it starts with clarity. Use this lifecycle as a repeatable blueprint. Expect to loop back as you learn.
 |
| Data pipeline |
1) Application Domain
Goal: Understand context, success criteria, and stakeholders.
Do: Define the decision to support, metrics that matter, constraints (privacy, latency), and where relevant data lives.
2) Data Selection
Goal: Pull only what’s needed for the task.
Do: Identify the target, choose candidate features, avoid data leakage, and set up time-aware train/validation/test splits.
3) Data Preprocessing (Cleaning)
Goal: Make data trustworthy. Often 60-80% of the work.
Do: Remove duplicates, handle missing values, fix inconsistent categories/units, and investigate outliers (cap, transform, or justify exclusion).
4) Data Transformation (and Reduction)
Goal: Turn raw fields into signals and manage complexity.
Do: Engineer features (lags, ratios, interactions), encode categoricals, normalise/standardise where needed, and reduce dimensionality/numerosity (PCA, aggregation, binning).
5) Data Mining
Goal: Apply suitable algorithms and tools.
Do: Start with baselines, run cross-validation, tune hyperparameters, and select models that meet business constraints (accuracy, interpretability, latency).
6) Interpretation/Evaluation
Goal: Translate results into decisions.
Do: Use task-appropriate metrics, perform error analysis across segments, calibrate thresholds, and communicate findings with clear visuals and plain language.
7) Revisit, Revise
Goal: Improve by iterating.
Do: Loop back when you find leakage, weak features, misaligned metrics, or drift. Update data, features, models, and documentation as needed.
Comments
Post a Comment