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Applied predictive analytics
How historical data feeds projections and business decisions.
Predictive analytics uses historical data to estimate the future — churn, demand, default, LTV. It is not a crystal ball: it is probability with clean data and a reviewed model.
Basic pipeline
- Collection — consistent data without selection bias.
- Preparation — cleaning, features, train/test split.
- Model — regression, trees, or ML depending on complexity.
- Validation — clear metric (MAE, AUC, etc.).
- Deploy — integration into product or operational dashboard.
- Monitoring — data drift and model degradation.
When it is worth it
Sufficient data volume and a repeatable decision with measurable error cost. For small samples, start with simple rules and linear regression.
Por trás do sistema
Dados de mercado alimentam simuladores e produtos de decisão que construo para clientes.
Versão em português (original): /analise-preditiva