tadamaen/DSA3101-Group-Project-Group-3
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVPythonJupyter NotebookMarkdown
Technical Skills
Data AnalysisData EngineeringData VisualizationExploratory Data AnalysisFile ManagementMachine Learning
brenda-su
PROFILE
Brenda-su
Developed and enhanced an end-to-end analytics pipeline for the tadamaen/DSA3101-Group-Project-Group-3 repository, focusing on theme park attendance forecasting by integrating weather, temperature, precipitation, humidity, and holiday data. Leveraged Python, Pandas, and Scikit-learn to conduct exploratory data analysis, build predictive models, and compare Linear Regression with Random Forest approaches. Improved code hygiene by refactoring scripts, removing deprecated files, and updating documentation for clarity and reproducibility. Enhanced guest segmentation analysis and established robust plotting scaffolding in Jupyter Notebooks, supporting business decisions around staffing and operations. Delivered reproducible, well-documented workflows and visualizations to facilitate future project extensions.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
20Total
Bugs
1
Commits
20
Features
4
Lines of code
5,247
Activity Months2
Your Network
81 peopleShared Repositories
11
Work History
April 2025
11 Commits • 2 Features
Apr 1, 2025April 2025 monthly performance for tadamaen/DSA3101-Group-Project-Group-3 focused on delivering analytics enhancements, robust plotting scaffolding, and documentation quality improvements to boost clarity, reproducibility, and business value.
11 Commits • 2 Features
Apr 1, 2025April 2025 monthly performance for tadamaen/DSA3101-Group-Project-Group-3 focused on delivering analytics enhancements, robust plotting scaffolding, and documentation quality improvements to boost clarity, reproducibility, and business value.
April 2025
March 2025
9 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for tadamaen/DSA3101-Group-Project-Group-3. Delivered end-to-end analytics pipeline for theme park attendance, integrating weather, temperature, precipitation, humidity, and holiday data to inform forecasting. Implemented data migration to Subgroup A Python Codes, consolidated datasets, and stabilized the modeling workflow. Conducted EDA, correlation analysis, and predictive modeling, comparing Linear Regression and Random Forest, with standardized features and refined visualizations. Improved code hygiene by removing deprecated files and updating Colab scripts (Round 3), increasing reproducibility and maintainability. Result: actionable forecasts to support staffing and operations decisions, with a foundation for future extensions.
March 2025
9 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for tadamaen/DSA3101-Group-Project-Group-3. Delivered end-to-end analytics pipeline for theme park attendance, integrating weather, temperature, precipitation, humidity, and holiday data to inform forecasting. Implemented data migration to Subgroup A Python Codes, consolidated datasets, and stabilized the modeling workflow. Conducted EDA, correlation analysis, and predictive modeling, comparing Linear Regression and Random Forest, with standardized features and refined visualizations. Improved code hygiene by removing deprecated files and updating Colab scripts (Round 3), increasing reproducibility and maintainability. Result: actionable forecasts to support staffing and operations decisions, with a foundation for future extensions.
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture93.0%
Performance92.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookMarkdownPython
Technical Skills
Code RefactoringData AnalysisData EngineeringData VisualizationDocumentationExploratory Data AnalysisFile ManagementMachine LearningMatplotlibNumPyPandasPythonPython ScriptingScikit-learnSeaborn
Repositories Contributed To
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