Data mining and predictive models: discovering patterns

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Discover everything you need to know about data mining techniques and potential problems with predictive models.
Do you want to confirm or discover? Do you know the difference between a verification and a discovery? Which one benefits your business more?

Data mining and predictive models are the foundation of business knowledge. Their purpose is to find patterns in large volumes of data that add value to the organization and its strategy. So, what aspects should we take into account?


Nowadays, data mining uses artificial intelligence and homeowner database learning, which increases its reach and the impact that the models resulting from training algorithms with data and more data can have. That is why we always start with correct data management, so that they can take us to the next level.







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Data mining techniques and predictive models
There are two large groups of data mining techniques and predictive models: supervised and unsupervised, a classification that takes into account three factors:

Application maturity.

Combined use of current and historical data.

Predictive potential.

Knowledge discovery techniques, which are unsupervised, are only used for description and generate valuable information through analysis, visualization, grouping or dependency study. On the other hand, supervised techniques allow going further.

When data mining and predictive models are used based on a training and testing system, it is possible to detect deviations, segment, create sequential patterns, association rules and clustering. To do this, it is enough to implement two actions:

Train the model.

Test the model.


On the other hand, there are three aspects of predictive modeling that must always be taken into account:

Data sample: is the data collected for its representativeness to describe the problem to be solved and that presents known relationships between inputs and outputs.
Model learning: an algorithm is created to be applied to this data, with the particularity that the created model must be able to be used again and again in the future.
Predictions: consist of applying the model that has already been learned to new data, for which the result is not previously known.