UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS

Аннотация

INTRODUCTION analysis of large volumes of unstructured data, In the context of rapid technological development, uncovering hidden patterns and optimizing both retailers and telecom operators face business processes without the need for pre- challenges in managing physical sales networks, as labeled data. This approach is especially relevant maintaining a network of physical stores is one of in the telecommunications sector, where the primary cost drivers. The efficiency of consumer data and behavior play a key role in managing these stores is influenced by modern decision-making, making this topic highly relevant. technological solutions. Traditional methods of The use of unsupervised machine learning and data analysis and demand forecasting are vector models significantly enhances operational becoming less effective due to the increasing efficiency, leading to cost reduction and improved volume and complexity of data. In this context, the customer experience. These technologies open use of unsupervised machine learning and vector new possibilities for analysis and forecasting, models appears particularly promising. contributing to more accurate and faster decision- Unsupervised machine learning enables the making. https://www.theamericanjournals.com/index.php/tajet 23 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06 ISSUE10 The aim of this work is to explore the potential accurately labeled data for feeding into the model application of unsupervised machine learning and is often difficult. Moreover, the cost of data can be vector models for designing and optimizing retail high, and its use may not always yield the expected channels for telecommunications services. results. Currently, alternative methods are being developed that have not yet gained widespread use 1. Application of Unsupervised Machine but may become important in the future. Learning in Consumer Data Analysis One method worth noting is label-free learning. In In the process of studying and processing data this technique, the model receives raw data within the framework of machine learning, without predefined labels or patterns. The especially when dealing with models that operate algorithm independently analyzes this data, on information, it is crucial to use accurate and generating new patterns and labels. One of the reliable data. Training models implies that advantages of this approach is that there is no need algorithms must receive the necessary data to to provide labeled data. The system autonomously effectively perform tasks using artificial identifies the rules necessary for analysis and intelligence. While this approach is a popular generates its own patterns. The process of method in machine learning, certain challenges unsupervised learning involves several key stages, arise with its use. One of the most significant which are illustrated in Figure 1. challenges is the issue of data labeling, as finding The study of data structure Extracting the necessary a lgorithms and the formation kn owledge that can be used in T he decision-making process of templates the future for analysis Fig.1. The stages of unsupervised learning [1]. Unsupervised learning algorithms can be classified into two main types based on the methods used for data processing, as shown in Figure 2. https://www.theamericanjournals.com/index.php/tajet 24 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06 ISSUE10 U ncontrolled neural networks M ethods of unsupervised learning These networks are trained on unlabeled data, which allows Clustering.This method is aimed at th em to solve regression and fin ding patterns in the data and classification problems in the grouping them. absence of supervision. Dimensionality reduction simplifies data analysis by reducing the number of variables and identifying key features Fig.2. Classification of unsupervised learning algorithms [1]. tasks, such as determining spam, sentiment Overall, unsupervised learning methods provide analysis of texts, or weather forecasting. extensive opportunities for data analysis and Unsupervised learning, in turn, is useful for tasks processing, making them essential tools in the related to anomaly detection, improving development of artificial intelligence [1]. The main recommendation systems, predicting customer distinction between supervised and unsupervised behavior, and analyzing medical images [3]. learning lies in the use of labeled data. Supervised learning relies on pre-known labeled input and 2. Vector Models in Demand Forecasting and output data, allowing the algorithm to learn by Distribution Channel Optimization adjusting its predictions until the correct result is To obtain accurate data on future customer churn, achieved. This requires human involvement and a deep understanding of classification tasks, which accurate data labeling. An example of this would be are part of data mining, is required