Using historical data, statistics, and machine learning to forecast future outcomes or behaviors.
Predictive analytics answers questions like: which customers are likely to cancel next month, how much stock will a store need for the festival season, or which machine is at risk of failure? It builds models on past data and applies them to current data to estimate probabilities and trends.
Common techniques include regression, decision trees, time series forecasting, and neural networks. Applications span retail demand planning, credit scoring, fraud detection, insurance pricing, predictive maintenance in factories, and hospital readmission risk. Predictions are only as good as the data behind them, and models can drift as conditions change. Responsible use requires monitoring accuracy, checking for bias, and explaining how predictions influence decisions that affect people.
The percentage of customers or subscribers who stop using a product or cancel a subscription during a given period.
Data VisualizationPresenting data as charts, graphs, maps, or other visuals so patterns, trends, and outliers are easier to understand.
Inventory Management SystemSoftware that tracks stock levels, orders, sales, and deliveries so a business knows what it has and when to reorder.
Machine LearningA branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case.
Model DriftThe gradual decline in an AI model's accuracy as real-world data or conditions change from those it was trained on.
RegressionA statistical and machine learning method for predicting a continuous numerical value based on the relationship between variables.
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