title: ÐÐ¾ÑÑÑÐ¾ÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¿Ð¾ Ð½ÐµÐ´Ð²Ð¸Ð¶Ð¸Ð¼Ð¾ÑÑÐ¸ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Building an automated system for predictive analysis of real estate data using machine learning
Creators: name: ÐÐµÑÐºÐ°Ð´Ð¾ ÐÑÐ´Ð°Ð»Ð¾Ð²Ð°, ÐÐ°Ð½Ð¸Ð»Ð¾
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð½ÑÐ¸Ì Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÐºÐ¾Ð½ÑÐµÐ¸ÌÐ½ÐµÑÑ
Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ
predictive data analysis
containers
machine learning model
data processing methods

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description: This paper proposes a method for building an automatic predictive real estate analysis system with machine learning using a distributed approach that allows the appropriate scaling that can be applied in different situations. This work is devoted to studying the shortcomings of existing predictive analytics systems and giving an example of an automatic predictive analytics system that has advanced experience, uses modern technologies, and allows analysis to be carried out in the most effective way possible. To achieve this goal, advanced methods of data extraction, transformation and purification will be used, followed by the implementation of a machine learning model using Apache Spark. In addition, the "master worker" architecture was used, which provides optimal storage capacity combined with increased fault tolerance due to the integration of the Apache Kafka cluster using the agreed Kraft protocol, as well as portability and Docker containers. This comprehensive approach is designed to offer a reliable and scalable solution to real estate decision-making challenges. The results show that this approach reduces the time and labor cost of these tasks.