Predictive Analysis

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What It Is:

Predictive analytics combines statistical techniques, ML models, and big data pipelines to anticipate future trends. Unlike descriptive analytics (what happened), predictive analytics asks: what is likely to happen next?

Technical Stack:
       •        Data preparation: ETL pipelines (Apache Spark, Kafka).
       •        Modeling: Time-series forecasting (ARIMA, Prophet, LSTM networks), regression analysis, ensemble methods.
       •        Validation: Cross-validation, AUC-ROC, RMSE.
       •        Deployment: Dashboards (Power BI, Tableau), cloud ML endpoints (AWS SageMaker, GCP Vertex).

Applications:
       •        Finance: Credit risk scoring, fraud detection.
       •        Retail: Inventory planning, demand spikes.
       •        Healthcare: Disease progression models, patient readmission forecasts.
       •        Manufacturing: Predictive maintenance using IoT sensors.

Think of predictive analysis as equipping your business with a weather forecast—not for the skies, but for markets, risks, and opportunities.

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