Chameleon-company/MOP-Code
Apr 2025 – Aug 2025
3 Months active
Languages Used
Jupyter NotebookMarkdownPythonCSSHTMLJSONJavaScriptSQL
Technical Skills
API IntegrationData AnalysisData VisualizationDecision TreesGeospatial AnalysisKMeans Clustering
Saneli Wijesinghe
PROFILE
Saneli Wijesinghe
Sahan Ewijesinghe developed data-driven urban analytics solutions for the Chameleon-company/MOP-Code repository, focusing on smart infrastructure and workforce planning. He built Jupyter notebooks to optimize Melbourne’s street lighting using pedestrian movement data, applying Python, Pandas, and geospatial analysis to map sensors, clean data, and model lighting needs for safety and energy efficiency. Sahan also delivered a workforce planning tool for Victoria, integrating and analyzing government datasets to identify teacher supply gaps. His work emphasized reproducible pipelines, clear documentation, and actionable insights, demonstrating depth in data cleaning, predictive modeling, and technical writing to support urban planning and decision-making processes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
5
Lines of code
255,270
Activity Months3
Your Network
98 people
Work History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for Chameleon-company/MOP-Code highlights the delivery of a new Teacher Workforce Planning notebook for Victoria, establishing an end-to-end data workflow to estimate educational demand vs. supply and surface potential workforce gaps for planning decisions. The work emphasizes business value by informing proactive staffing and policy decisions, while demonstrating solid data-science practices and reproducible workflows.
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for Chameleon-company/MOP-Code highlights the delivery of a new Teacher Workforce Planning notebook for Victoria, establishing an end-to-end data workflow to estimate educational demand vs. supply and surface potential workforce gaps for planning decisions. The work emphasizes business value by informing proactive staffing and policy decisions, while demonstrating solid data-science practices and reproducible workflows.
August 2025
May 2025
7 Commits • 3 Features
May 1, 2025May 2025 monthly summary: Delivered end-to-end Melbourne pedestrian-driven adaptive street lighting optimization for the Chameleon-company/MOP-Code project. Implemented data ingestion from APIs, cleaning and processing of pedestrian movement and sensor data, spatial mapping of sensors to streetlights, and a predictive model for optimal lighting levels based on traffic and location, complemented by a simulator to evaluate energy efficiency and public safety improvements. Finalized Sprint 2 notebook with comprehensive conclusions and stakeholder-ready recommendations; updated configuration and documentation to improve discoverability and reuse. Ensured filename consistency and submission readiness for UC00166. Tech stack leveraged Python, Jupyter notebooks, API integration, data cleaning, spatial analysis, predictive modeling, and JSON metadata configuration; Git-based collaboration and clear documentation produced repeatable artifacts for decision-making.
May 2025
7 Commits • 3 Features
May 1, 2025May 2025 monthly summary: Delivered end-to-end Melbourne pedestrian-driven adaptive street lighting optimization for the Chameleon-company/MOP-Code project. Implemented data ingestion from APIs, cleaning and processing of pedestrian movement and sensor data, spatial mapping of sensors to streetlights, and a predictive model for optimal lighting levels based on traffic and location, complemented by a simulator to evaluate energy efficiency and public safety improvements. Finalized Sprint 2 notebook with comprehensive conclusions and stakeholder-ready recommendations; updated configuration and documentation to improve discoverability and reuse. Ensured filename consistency and submission readiness for UC00166. Tech stack leveraged Python, Jupyter notebooks, API integration, data cleaning, spatial analysis, predictive modeling, and JSON metadata configuration; Git-based collaboration and clear documentation produced repeatable artifacts for decision-making.
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — This monthly summary highlights key features delivered, major bugs fixed, overall impact and accomplishments, and technologies demonstrated for the MOP-Code project. In Melbourne, we delivered the Smart Street Lighting Efficiency Analysis Notebook (EDA) to optimize street lighting using pedestrian movement data. The workflow includes data collection from public APIs, cleaning and preprocessing, spatial mapping of sensors to streetlights, and temporal analysis of foot traffic to identify areas needing lighting adjustments for safety and energy efficiency. Work progressed through Sprint 1 with initial notebook scaffolding, followed by addressing reviewer feedback to improve quality and reproducibility.
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — This monthly summary highlights key features delivered, major bugs fixed, overall impact and accomplishments, and technologies demonstrated for the MOP-Code project. In Melbourne, we delivered the Smart Street Lighting Efficiency Analysis Notebook (EDA) to optimize street lighting using pedestrian movement data. The workflow includes data collection from public APIs, cleaning and preprocessing, spatial mapping of sensors to streetlights, and temporal analysis of foot traffic to identify areas needing lighting adjustments for safety and energy efficiency. Work progressed through Sprint 1 with initial notebook scaffolding, followed by addressing reviewer feedback to improve quality and reproducibility.
April 2025
Activity
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Quality Metrics
Correctness86.4%
Maintainability85.4%
Architecture84.6%
Performance78.2%
AI Usage36.4%
Skills & Technologies
Programming Languages
CSSHTMLJSONJavaScriptJupyter NotebookMarkdownPythonSQL
Technical Skills
API IntegrationData AnalysisData CleaningData ScienceData VisualizationDecision TreesExcel Data HandlingExploratory Data Analysis (EDA)FoliumGeospatial AnalysisJupyter NotebookJupyter NotebooksKMeans ClusteringMachine LearningMatplotlib
Repositories Contributed To
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