BUILDING DATA WAREHOUSING FOR MACHINE LEARNING AND CLIENT SCORING

In modern business, data is a critical resource that can significantly increase the company's competitiveness. One of the key tasks associated with data is the creation of data warehouses that provide effective storage, processing and analysis of information. In particular, structured data warehouses play a critical role in the development of machine learning and customer scoring systems. In this article, we will look at the stages of building data warehouses, integrating them with machine learning systems, and the importance of structured data for customer scoring. At the end of the article, we will present a mini-project on implementing a data warehouse using SQL and PySpark, as well as an example of machine learning for customer scoring. Keywords: data warehouse, SQL and PySpark, customer scoring, data structuring, structured data warehouses.

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Publication Details

Journal
ETM Equipment Technologies Materials
Published
2024-11-08
DOI
https://doi.org/10.36962/etm23052024-10
Primary Topic
Big Data and Business Intelligence
Type
article
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article

BUILDING DATA WAREHOUSING FOR MACHINE LEARNING AND CLIENT SCORING

Jala Jamalova, Irada Seyidova Nazila Ragimova Nazila Ragimova
ETM Equipment Technologies Materials
Big Data and Business Intelligence
article

BUILDING DATA WAREHOUSING FOR MACHINE LEARNING AND CLIENT SCORING

Jala Jamalova, Irada Seyidova Nazila Ragimova Nazila Ragimova
article en

Abstract

In modern business, data is a critical resource that can significantly increase the company's competitiveness. One of the key tasks associated with data is the creation of data warehouses that provide effective storage, processing and analysis of information. In particular, structured data warehouses play a critical role in the development of machine learning and customer scoring systems. In this article, we will look at the stages of building data warehouses, integrating them with machine learning systems, and the importance of structured data for customer scoring. At the end of the article, we will present a mini-project on implementing a data warehouse using SQL and PySpark, as well as an example of machine learning for customer scoring. Keywords: data warehouse, SQL and PySpark, customer scoring, data structuring, structured data warehouses.

ETM Equipment Technologies MaterialsVol. 23(05)
Industry, innovation and infrastructure
Openalex Percentile: Top 69%
Big Data and Business Intelligence
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BUILDING DATA WAREHOUSING FOR MACHINE LEARNING AND CLIENT SCORING — Jala Jamalova, Irada Seyidova Nazila Ragimova Nazila Ragimova · ETM Equipment Technologies Materials (2024) | TGRS Research Map | TGRS