نبذة مختصرة : The construction industry, particularly in residential housing, faces ongoing challenges in cost management and efficiency optimization. This paper presents a comprehensive data science framework for optimizing house construction processes, focusing on cost reduction and efficiency enhancement. By leveraging advanced analytics, machine learning, and predictive modeling techniques, we propose a novel approach to address key areas of construction management, including material procurement, labor allocation, project scheduling, and quality control. Our methodology integrates diverse data sources, employs sophisticated algorithms for pattern recognition and optimization, and provides actionable insights for decision-makers in the construction industry. This research contributes to the growing field of data-driven construction management and offers practical strategies for improving the economic and operational aspects of house construction projects.
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