Predictive analytics is one of the most important areas of data analysis, which allows predicting future events based on historical data. The relevance of predictive analytics in the modern world is due to the rapid development of technology, the growth of data volumes and the growing need for informed management decision-making. The article discusses the main approaches such as regression models, time series, decision trees, clustering methods and neural networks, as well as their advantages and disadvantages.

Keywords: predictive analytics, regression models, time series, decision trees, neural networks, clustering, big data, predictive analytics methods, big data analysis, forecasting

The railway transport industry demonstrates significant achievements in various fields of activity through the introduction of predictive analytics. Predictive analytics systems use data from a variety of sources, such as sensor networks, historical data, weather conditions, etc. The article discusses the key areas of application of predictive analytics in railway transport, as well as the advantages, challenges and prospects for further development of this technology in the railway infrastructure.

Keywords: predictive analytics in railway transport, passenger traffic forecasting, freight optimization, maintenance optimization, inventory and supply management, personnel management, financial planning, big data analysis