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
Over two months, Tadamaen developed and refined an end-to-end analytics pipeline for the tadamaen/DSA3101-Group-Project-Group-3 repository, focusing on theme park attendance forecasting. They integrated weather, temperature, precipitation, humidity, and holiday data, consolidating disparate sources into a reproducible modeling workflow. Using Python, Pandas, and Scikit-learn, Tadamaen conducted exploratory data analysis, built and compared Linear Regression and Random Forest models, and enhanced data visualizations. Their work included code refactoring, documentation improvements, and the creation of plotting scaffolding, resulting in actionable forecasts and clearer project documentation. The depth of work established a robust foundation for future analytics and operational planning.
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
73 people
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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