Over two months, Andrew Chan developed an end-to-end data pipeline and predictive modeling workflow for the NotInvalidUsername/DSA3101_Group8_Project1 repository, targeting Disney Park demand forecasting. He integrated diverse datasets—including incidents, weather, and attendance—using Python and Pandas, and engineered features for regression analysis. Andrew trained and evaluated multiple models, selecting Random Forest for its performance in predicting attraction demand scores. He also built a Streamlit-based UI for demand prediction, enhancing user experience with robust asset handling. Throughout, he maintained high code quality by cleaning up obsolete files, improving documentation, and streamlining dependency management, ensuring the project’s maintainability and operational readiness.
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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