| Περίληψη: | For this thesis, neural network models have been developed to experiment with classification
on numerical data. The architectures used these years give the opportunity to manage large
volumes of data and export correlations. Every minute, the number of data is constantly
increasing and the target is to make better use of it for various problems. Due to the large
amount of data it is imperative to find data with similar characteristics. In this thesis a
combinational analysis technique based on a "balls in bins" game for the purpose of
classification is implemented. Classification combined with machine learning techniques
enables prediction. This would not be possible without the proper computing tools and
libraries that make the task of the developer much easier.
The work is divided into two parts. The bibliographical section presents the theoretical
documentation of machine learning techniques as well as a general overview of the main
knowledge studied for the writing of this work. The experimental section attempts to
implement a technique, combinational analysis for data processing, and three neural networks
for predicting data categories.
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