This paper analyzes the performance of solving the classification problem using various open-source artificial intelligence and machine learning libraries in the field of marketing and customer relationship management; based on the results of experiments, the best library is selected for the purpose of introducing artificial intelligence into domestic CRM systems based on numerical performance indicators.

Keywords: artificial intelligence, machine learning, big data, classification, marketing, customer relationship management, import substitution, open source

Is devoted to the actual problem of constructing classifiers objects given by a point in a multidimensional space of feature values. The principle of linear normal classification of objects in multi-dimensional space of attributes can be used to build a classifier in the case of many complex structures, in general, are inseparable one hyperplane. In such cases, proposed to use a set of hierarchically related normal separating hyperplanes, which is called the normal hierarchical classifier.

Keywords: recognition, classification, feature space, the geometric method

  Is devoted to the actual problem of constructing classifiers objects given by a point in a multidimensional space of feature values. A version of the geometric separation of sets by hyperplanes normal to the center-distance data sets. This approach to separating planes reduces the computational operations performed. This author separability criterion allows a normal quite effective in terms of computational complexity the exact solution of the normal separation, which requires only a linear search of points separated sets. Proposed in the article the approach to classification of sets in the multidimensional space of values ​​of their attributes can be used as a starting point for building effective in terms of computational complexity classification not only for normally separable sets, but also for more complex variations thereof. This is the most significant practical importance of materials submitted by the authors.

Keywords: recognition, classification, feature space, the geometric method