The article proposes a methodology for implementing self-learning feedback models in Customer Relationship Management (CRM) systems. It examines the key issues of existing CRM systems, including insufficient adaptability to changes in customer behavior and limited capabilities for automatic data analysis.
Nowadays, neural networks are among the most popular analysis tools. They are effective in solving classification, pattern recognition, and clustering problems. This paper provides a detailed description and analysis of the operational principles of two neural networks, namely a Siamese network and a multilayer perceptron. A model for using these neural networks in time series forecasting is proposed. As an example, a web application was created in which the described neural networks were used to analyze the correlation between pairs of financial assets and assess t
The constant growth of data volumes requires the development of effective methods for managing, processing, and storing information. Additionally, it is advisable to apply multimodal approaches for knowledge aggregation to extract additional knowledge. Usually, the problem of efficient processing of multimodal data is associated with high-quality data preprocessing. One of the most critical preprocessing steps is synchronizing multimodal data streams to analyze complex interactions in different data types.
A computer model for analyzing, evaluating, and forecasting bank credit risks has been developed. Utilizing a Bayesian network (BN) and established parameter estimation methods, this model was implemented in the Python programming language. It predicts the probability that a borrower may fail to meet financial obligations, such as repaying a loan.
This article discusses the practical aspects of applying logistic regression for binary data classification. Logistic regression determines the probability of an object belonging to one of two classes. This probability is calculated with the help of a sigmoid function, the argument of which is a linear convolution of the feature vector of the object with the weighting coefficients obtained during the minimization of the logarithmic loss function. Predicted class labels are determined by comparing the calculated probability with a given threshold value.
In the context of critical challenges related to global warming and the necessity of reducing carbon footprint, the electric car sector is experiencing significant growth. This progress inevitably leads to the need for expansion and modernization of the charging station infrastructure. This article conducts a detailed analysis of how big data processing technologies can contribute to the optimization of this infrastructure’s use, the efficiency of charging stations, and the development of personalized services for electric vehicle users.
This article reviews the theoretical aspects of logistic regression for binary data classification, including data preparation processes, training, testing, and model evaluation metrics.
Requirements for input data sets are formulated, methods of coding categorical data are described, methods of scaling input features are defined and substantiated.
In this article, the mathematical justification of logistic regression as an effective and simple to implement method of machine learning is performed.
A review of literary sources was conducted in the direction of statistical processing, analysis and classification of data using the logistic regression method, which confirmed the popularity of this method in various subject areas.
In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data.
In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features.