Applications of predictive models in Big Data

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Clustering models, on the other hand, are often used for customer segmentation and shopping basket analysis. These models classify customers based on their income or purchasing habits.

Classification analyses on their side identify the variables leading to similar results, making it possible to predict future customer purchases based on their past benin phone data purchases and the profiles of similar customers. The areas of application are numerous, for example, a classification algorithm will make it possible to define whether an email is spam or not.

Finally, Machine Learning is a sophisticated computer system that allows machine learning algorithms to automatically improve themselves based on the data they process.

Most companies opt for a judicious combination of several methods and algorithms, in order to obtain more precise and accurate analyses. The constant evolution and improvement of these methods paves the way for prescriptive analyses, thus guiding the company's strategies based on the results generated by predictive analyses.


The applications of predictive analytics in the field of Big Data are plentiful, offering the possibility of identifying trends, detecting risks, and guiding decision-making. Both the private and public sectors use them to achieve specific objectives. For example, in the field of public health, these analyses make it possible to monitor the evolution of an epidemic and identify patients at risk based on their typology. These models help improve patient care while reducing costs. In the field of manufacturing, predictive analyses optimize each step of the production process, reducing costs and accelerating lead times.