Chameleon-company/MOP-Code
Sep 2025 – Oct 2025
2 Months active
Languages Used
CSVJupyter NotebookMarkdownPython
Technical Skills
Data AnalysisData EngineeringData ScienceData VisualizationData WranglingK-Means Clustering
SuhanaT
PROFILE
Suhanat
Suhana Tunio developed foundational data-driven features for the Chameleon-company/MOP-Code repository, focusing on optimizing luggage management for tourists in Melbourne. Over two months, Suhana engineered a comprehensive CSV dataset and implemented data wrangling pipelines using Python and Pandas to analyze footfall patterns and locker proximity. She applied regression analysis and machine learning models with Scikit-learn to predict travel times, supporting city planning and operational decisions. Her work included static data visualizations with Matplotlib, enabling quick insights for stakeholders. The features delivered established a scalable analytics baseline, demonstrating depth in data engineering, system design, and practical application of data science techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
13,682
Activity Months2
Your Network
98 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — In Chameleon-company/MOP-Code, delivered the Smart Luggage Management for Tourist Mobility feature. The work encompassed data wrangling pipelines, visualization of locker proximity coverage, analysis of tourist footfall patterns, static charts visualizations, and regression models to predict travel time. This feature establishes core functionalities and requirements to optimize luggage storage locations, enhancing city planning and improving the tourist experience. No major bugs were reported; stability improvements were addressed in tandem with feature delivery. This work sets a foundation for scalable analytics and future enhancements.
2 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — In Chameleon-company/MOP-Code, delivered the Smart Luggage Management for Tourist Mobility feature. The work encompassed data wrangling pipelines, visualization of locker proximity coverage, analysis of tourist footfall patterns, static charts visualizations, and regression models to predict travel time. This feature establishes core functionalities and requirements to optimize luggage storage locations, enhancing city planning and improving the tourist experience. No major bugs were reported; stability improvements were addressed in tandem with feature delivery. This work sets a foundation for scalable analytics and future enhancements.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Chameleon-company/MOP-Code. Focused on delivering a foundational data asset to enable data-driven luggage management analytics and operational optimization.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Chameleon-company/MOP-Code. Focused on delivering a foundational data asset to enable data-driven luggage management analytics and operational optimization.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance80.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
CSVJupyter NotebookMarkdownPython
Technical Skills
Data AnalysisData EngineeringData ScienceData VisualizationData WranglingK-Means ClusteringMachine LearningMatplotlibNumpyPandasRegression AnalysisScikit-learnSystem DesignUse Case Definition
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