Developed a data-driven guest segmentation pipeline for the tadamaen/DSA3101-Group-Project-Group-3 repository, enabling targeted analysis of theme park visitors. Leveraged Python, Pandas, and Scikit-learn to implement end-to-end workflows including data cleaning, feature engineering, PCA-based dimensionality reduction, and K-means clustering. Produced reproducible datasets, scripts, and Jupyter notebooks to support visualization and business decision-making. Enhanced repository hygiene through systematic file management, removal of obsolete assets, and improved naming conventions. Expanded documentation and deployment guidance, including Docker setup and onboarding instructions, to streamline collaboration and accelerate experimentation. The work established a robust foundation for data science modeling and reproducibility.
April 2025 monthly summary for tadamaen/DSA3101-Group-Project-Group-3. Delivered data provisioning and documentation improvements for guest segmentation, along with targeted code hygiene fixes. Established a reproducible data foundation, enhanced deployment/readme guidance, and standardized dataset references to support modeling and onboarding.
April 2025 monthly summary for tadamaen/DSA3101-Group-Project-Group-3. Delivered data provisioning and documentation improvements for guest segmentation, along with targeted code hygiene fixes. Established a reproducible data foundation, enhanced deployment/readme guidance, and standardized dataset references to support modeling and onboarding.
March 2025 monthly summary: Delivered a data-driven guest segmentation pipeline for the DSA3101 Group Project, enabling targeted insights into theme park visitors. Implemented end-to-end pipeline with data cleaning, feature engineering, PCA-based dimensionality reduction, and K-means clustering. Produced reproducible analysis assets including datasets, scripts, and notebooks to support segment visualization and decision-making. Performed repository cleanup and maintenance to improve hygiene, organization, and collaboration, including removal of obsolete notebooks and directories and renaming assets for clarity. Overall, this work adds business value by enabling more precise marketing, queue management, and guest experience optimizations, while strengthening the team's data science workflow and version-controlled reproducibility.
March 2025 monthly summary: Delivered a data-driven guest segmentation pipeline for the DSA3101 Group Project, enabling targeted insights into theme park visitors. Implemented end-to-end pipeline with data cleaning, feature engineering, PCA-based dimensionality reduction, and K-means clustering. Produced reproducible analysis assets including datasets, scripts, and notebooks to support segment visualization and decision-making. Performed repository cleanup and maintenance to improve hygiene, organization, and collaboration, including removal of obsolete notebooks and directories and renaming assets for clarity. Overall, this work adds business value by enabling more precise marketing, queue management, and guest experience optimizations, while strengthening the team's data science workflow and version-controlled reproducibility.
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