Predictive analytics technology, what is it ?

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Predictive analytics technology (predictive analytics) is a set of mathematical, statistical, and algorithmic methods that analyze historical data to estimate the probability of future events.

3 pillars of the technology:
Data collection and preparation: All available information from the past and present is extracted (e.g., logistics, finance, user actions).

Analytical methods: Mathematical statistics, machine learning (ML), and game theory are applied.

Forecast generation: The system produces a result in the form of probability estimates of future events or numerical values with an accuracy of 80–85%.

Main methods:
Regression analysis: Finding relationships between variables to predict numerical values (e.g., future revenue).

Decision trees: A classification algorithm for splitting data based on specific rules (e.g., probability of loan approval).

Neural networks: Mimic the human brain to identify complex hidden patterns in huge datasets (Big Data).

Where it is applied:
Business and retail: Demand forecasting, inventory optimization, and customer churn prevention.

Industry: Predictive maintenance — predicting equipment failures before they occur, based on sensors.

Finance and banking: Credit risk assessment and detection of fraudulent transactions.

Marketing: Personalization of offers and conversion probability estimation.

Popular implementation tools for building predictive models use both ready-made SaaS platforms and development environments:
Visual programming tools: Loginom or RapidMiner.

Libraries for deep analysis: Open-source Python packages such as Scikit-learn, TensorFlow, or PyTorch.

Cloud services: Solutions from Amazon Web Services (AWS) or [Microsoft Azure]