Amelia Low developed a data-driven guest segmentation pipeline for the tadamaen/DSA3101-Group-Project-Group-3 repository, focusing on theme park visitor analysis. She implemented end-to-end data cleaning, feature engineering, and dimensionality reduction using Python, Pandas, and scikit-learn, followed by K-means clustering to identify actionable visitor segments. Amelia produced reproducible datasets, scripts, and Jupyter notebooks to support visualization and business decision-making. She also improved repository hygiene by refactoring file structures, standardizing dataset references, and expanding documentation for onboarding and deployment. Her work established a robust, maintainable foundation for data science workflows, enabling targeted marketing and guest experience optimization for the project.
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.
Overview of all repositories you've contributed to across your timeline