Developed an end-to-end data pipeline and predictive modeling workflow for the NotInvalidUsername/DSA3101_Group8_Project1 repository, supporting Disney Park operations planning. Integrated diverse datasets—including incidents, weather, and attendance—using Python and Pandas to enable robust regression analysis and demand forecasting. Selected Random Forest as the optimal model after evaluating multiple approaches, and deployed a Streamlit-based UI for demand prediction. Enhanced repository maintainability through code organization, dependency management, and removal of obsolete files, while updating documentation to clarify business impact and usage. Focused on data quality, feature engineering, and reproducibility, the work enabled data-driven staffing and resource allocation decisions for park management.
April 2025 monthly summary for NotInvalidUsername/DSA3101_Group8_Project1. Delivered data onboarding for theme park incidents and attendance, launched an evidence-based demand prediction UI, and improved repository hygiene and packaging. Focused on business value, data quality, and maintainability with a streamlined setup for data pipelines and analytics.
April 2025 monthly summary for NotInvalidUsername/DSA3101_Group8_Project1. Delivered data onboarding for theme park incidents and attendance, launched an evidence-based demand prediction UI, and improved repository hygiene and packaging. Focused on business value, data quality, and maintainability with a streamlined setup for data pipelines and analytics.
March 2025 highlights for NotInvalidUsername/DSA3101_Group8_Project1. Delivered an end-to-end Disney Park Demand data pipeline and predictive modeling workflow to support operations planning. The pipeline ingests and fuses multiple datasets (park incidents, weather, disasters, public holidays, seasons, events, and competitor attendance) and trains/evaluates regression models, with Random Forest selected as the best-performing model for attraction demand scores. Business impact and usage have been documented to inform staffing, capacity planning, and event-driven resource allocation. Additionally, performed repo hygiene by removing unused placeholder HTML and obsolete notebooks, ensuring the focus remains on the predictive model workflow and keeping documentation aligned with deployment-ready artifacts. Updated README/docs to reflect business value and usage scenarios for park operations planning.
March 2025 highlights for NotInvalidUsername/DSA3101_Group8_Project1. Delivered an end-to-end Disney Park Demand data pipeline and predictive modeling workflow to support operations planning. The pipeline ingests and fuses multiple datasets (park incidents, weather, disasters, public holidays, seasons, events, and competitor attendance) and trains/evaluates regression models, with Random Forest selected as the best-performing model for attraction demand scores. Business impact and usage have been documented to inform staffing, capacity planning, and event-driven resource allocation. Additionally, performed repo hygiene by removing unused placeholder HTML and obsolete notebooks, ensuring the focus remains on the predictive model workflow and keeping documentation aligned with deployment-ready artifacts. Updated README/docs to reflect business value and usage scenarios for park operations planning.
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