Predictive modelling is the technical core of predictive analytics: example data with known outcomes is used to build a model that predicts a value or class for new cases. Common methods include regression, decision trees, random forests and neural networks. A model might, for instance, estimate a property price from floor area, location and year of construction. Before deployment it is tested on held-back data to detect overfitting.
Predictive analytics uses historical data, statistics and machine learning to estimate likely future events. It answers questions such as: which customers are about to cancel? How much stock will be needed next month? When is a machine likely to fail? The results are probabilities, not certainties. Their value depends heavily on data quality and on whether the predictions actually feed into decisions and processes.
Linear regression is a statistical method that describes the relationship between a target variable and one or more predictors using a straight line or plane. The coefficients are usually chosen to minimise the sum of squared deviations. The method is easy to understand and interpret: for example, it can estimate how much revenue rises when the advertising budget increases by €1,000. It reaches its limits with non-linear relationships.
Random forest regression predicts numerical values by combining many decision trees. Each tree is trained on a random sample of the data and a random selection of features; the result is the average of all the trees' predictions. This makes the method more robust and less prone to overfitting than a single tree. It is used, for example, for sales forecasts, price estimates or energy demand – often as a solid starting point for tabular data.
Cross-validation is a method for checking how well a model generalises to new, unseen data. In k-fold cross-validation, the data is split into k parts; the model is trained k times on k−1 parts and tested on the remaining part each time, after which the results are averaged. This helps detect overfitting and compare models or settings fairly, especially when little data is available.
