Data mining and predictive models: pattern discovery

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Do you intend to confirm or discover? Do you know the difference between a verification and a finding? Which one benefits your business the most??

Data mining and predictive models are the foundation of business knowledge. Its objective is to look for patterns in large volumes of data that add value to the organization and its strategy.. However, What aspects should we pay attention to??

Nowadays, Data mining uses artificial intelligence and machine learning, what enhances their reach and the impact that models that result from training algorithms with data and more data can have. That is why we always start from a correct administration of the data, to take us to the next level.

Data mining techniques and predictive models

There are two large groups of data mining techniques and Predictive models: supervised and unsupervised, Classification that caters to three factors:

  1. Application maturity.
  2. Combined use of historical and current data.
  3. Prediction potential.

Knowledge discovery techniques, that are unsupervised, They are only used for description and generate valuable information through analysis, display, Grouping or study of dependencies. Besides, Supervised techniques enable us to go further.


When using predictive and data mining models based on a training and test system, It is feasible to detect deviations, segment, Create sequential patterns, Association and grouping rules. To do this, Just start two actions:

  1. Train the model.
  2. Try the model.

Besides, There are three aspects of predictive modeling that should always be considered:

  • The data sample: These are the data that are collected by their representativeness to describe the problem to be solved and that present recognized relationships between inputs and outputs.
  • Learning the model: An algorithm is created to apply to this data, with the particularity that the model created must be able to be used in the future again and again.
  • Predictions: They consist of applying the model you have already learned with new data, for which the result is not previously known.


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Despite this, Although the application of this technique of data mining and predictive modeling may seem simple, Keep in mind that there are some potential disadvantages, like the following:

  • Any errors in the training and testing stage will multiply later.
  • It may happen that the initial data classification provided by the analyst is not sufficiently representative of the entire population to be studied., which would lead to deviations.
  • The model may not be able to detect the different types of data that deviate from the initial training set.
  • Sometimes, The assumption that groups within the data do not overlap and can be easily separated is not correct.

The discovery of patterns, Predictive modeling, Anticipate what's to come, Going competitive and finding a needle in a haystack are just some of the benefits of working with data mining.

Thus, Companies can be increasingly effective and efficient with respect to the business decisions that are made. Decidedly, without forgetting the starting point: Efficient data management.

Is your company ready??

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