Abstract: This paper introduces an Organization Optimization System (OOS) that integrates machine learning (ML) and Python programming to efficiently host various activities, including campaigns, competitions, and other events involving multiple organizations. The system utilizes data-driven decision-making to streamline resource allocation, enhance participant engagement, and maximize outcomes. This paper presents the architecture, key components, and implementation details of OOS, along with case studies showcasing its effectiveness in optimizing organization-hosted activities.

Organizations worldwide are facing unprecedented complexity, technological advancements, and increasing competition, necessitating a structured approach to enhance efficiency, productivity, and overall performance. The proposed framework amalgamates various organizational aspects, including leadership, culture, processes, technology, and human resources, into a cohesive system for sustained success. The research encompasses both theoretical insights and practical case studies, offering valuable guidelines to leaders and decision-makers seeking to optimize their organizations.

Keywords: Organization Optimization, Leadership, Culture Processes Technology Human Resources Efficiency

| DOI: 10.17148/IARJSET.2023.107101