Chameleon-company/MOP-Code
Aug 2025 – Sep 2025
2 Months active
Languages Used
PythonMarkdownSQL
Technical Skills
Data CleaningData PreprocessingData VisualizationJupyter NotebookMatplotlibPandas
Litxinh123
PROFILE
Litxinh123
During a two-month period, Linh developed end-to-end traffic analytics and predictive frameworks for the Chameleon-company/MOP-Code repository. Linh engineered a modular dataset and analytics pipeline for event permit data, implementing data loading, cleaning, feature engineering, and visualization using Python, Pandas, and Scikit-learn. The work included numeric feature scaling and preparation for machine learning, supporting scalable analytics and onboarding. Linh then built a predictive framework for event-driven traffic disruptions in Melbourne, enhancing data preprocessing, applying anomaly detection, and training multiple classification models. Comprehensive documentation and deployment-ready artifacts were delivered, enabling integration with traffic management notifications and supporting data-driven decision making.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
2,220
Activity Months2
Your Network
98 people
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 - Performance review-ready summary for Chameleon-company/MOP-Code. Delivered an end-to-end Event-driven Traffic Disruption Predictive Framework for Melbourne, combining robust labeling with weak supervision, anomaly detection, and comprehensive evaluation to forecast event impacts on transport and the economy. Implemented data preprocessing enhancements, trained and evaluated multiple classification models, and prepared a deployment-ready framework that supports traffic management and user notifications. Documentation and final artifacts updated to support review and handoff.
2 Commits • 1 Features
Sep 1, 2025Month: 2025-09 - Performance review-ready summary for Chameleon-company/MOP-Code. Delivered an end-to-end Event-driven Traffic Disruption Predictive Framework for Melbourne, combining robust labeling with weak supervision, anomaly detection, and comprehensive evaluation to forecast event impacts on transport and the economy. Implemented data preprocessing enhancements, trained and evaluated multiple classification models, and prepared a deployment-ready framework that supports traffic management and user notifications. Documentation and final artifacts updated to support review and handoff.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025Performance summary for August 2025: Delivered Traffic Analytics for Event Permits Use Case 2 in the MOP-Code repository. Implemented an end-to-end dataset and analytics pipeline with data loading, cleaning, date parsing, and missing value handling, plus feature engineering (event duration). Added visualizations and numeric feature scaling to enable ML readiness. Established modular dataset structure and Use Case 2 code to support reuse and onboarding. This work lays the groundwork for scalable analytics and data-driven decision making in traffic analytics.
August 2025
1 Commits • 1 Features
Aug 1, 2025Performance summary for August 2025: Delivered Traffic Analytics for Event Permits Use Case 2 in the MOP-Code repository. Implemented an end-to-end dataset and analytics pipeline with data loading, cleaning, date parsing, and missing value handling, plus feature engineering (event duration). Added visualizations and numeric feature scaling to enable ML readiness. Established modular dataset structure and Use Case 2 code to support reuse and onboarding. This work lays the groundwork for scalable analytics and data-driven decision making in traffic analytics.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
MarkdownPythonSQL
Technical Skills
Anomaly DetectionClassification ModelsData AnalysisData CleaningData PreprocessingData VisualizationDocumentationFeature EngineeringJupyter NotebookMachine LearningMatplotlibPandasProject ManagementPythonScikit-learn
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