Chameleon-company/MOP-Code
Aug 2025 – Sep 2025
2 Months active
Languages Used
JSONJupyter NotebookPythonSQLCSSHTMLJavaScriptShell
Technical Skills
API IntegrationCode CleanupData AnalysisData CleaningData VisualizationExcel
shivanisatav
PROFILE
Shivanisatav
Shivani developed and published a comprehensive analytics feature for the Chameleon-company/MOP-Code repository, focusing on the impact of student populations on local business revenue in Melbourne. She engineered an end-to-end workflow using Python, Pandas, and Jupyter Notebooks, handling data ingestion from multiple Excel sources, cleaning and merging datasets, and performing correlation analysis with visualizations by suburb and year. Shivani improved repository organization by removing outdated files, restructuring directories, and standardizing documentation. Her work enabled reproducible, actionable insights for business strategy and urban planning, while her attention to code hygiene and documentation supported maintainable analytics workflows and streamlined future project collaboration.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
20Total
Bugs
1
Commits
20
Features
3
Lines of code
114,238
Activity Months2
Your Network
98 people
Work History
September 2025
12 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Chameleon-company/MOP-Code: Delivered a publication-ready Melbourne UC00185 Student Population Impact Analysis and integrated Sprint 2 updates. Executed comprehensive repository governance to support publishing readiness and ongoing maintenance.
12 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for Chameleon-company/MOP-Code: Delivered a publication-ready Melbourne UC00185 Student Population Impact Analysis and integrated Sprint 2 updates. Executed comprehensive repository governance to support publishing readiness and ongoing maintenance.
September 2025
August 2025
8 Commits • 2 Features
Aug 1, 2025August 2025 performance summary for Chameleon-company/MOP-Code: focused on delivering a data-driven analytics feature, improving codebase hygiene, and enhancing project organization to drive business value and reproducible results. Key features delivered include an end-to-end Student Population Impact on Local Business Revenue Analysis for Melbourne and broader regions, with data loading/cleaning from multiple Excel sources, data merging, correlation analysis, and visualization by suburb/year; updated UC00185 notebook and documentation to reflect sprint 1 work and mentor feedback. Major bugs fixed include removing duplicate/outdated notebooks from sprint 1 related to the analysis, reducing confusion and keeping the codebase clean. Overall impact: provides actionable, reproducible insights for local business strategy and supports faster, reliable analytics workflows; improved developer productivity through better organization and documentation. Technologies/skills demonstrated: data ingestion from Excel, data cleaning, dataset merging, statistical correlation analysis, data visualization, notebook-based analytics, documentation practices, and git-based project organization.
August 2025
8 Commits • 2 Features
Aug 1, 2025August 2025 performance summary for Chameleon-company/MOP-Code: focused on delivering a data-driven analytics feature, improving codebase hygiene, and enhancing project organization to drive business value and reproducible results. Key features delivered include an end-to-end Student Population Impact on Local Business Revenue Analysis for Melbourne and broader regions, with data loading/cleaning from multiple Excel sources, data merging, correlation analysis, and visualization by suburb/year; updated UC00185 notebook and documentation to reflect sprint 1 work and mentor feedback. Major bugs fixed include removing duplicate/outdated notebooks from sprint 1 related to the analysis, reducing confusion and keeping the codebase clean. Overall impact: provides actionable, reproducible insights for local business strategy and supports faster, reliable analytics workflows; improved developer productivity through better organization and documentation. Technologies/skills demonstrated: data ingestion from Excel, data cleaning, dataset merging, statistical correlation analysis, data visualization, notebook-based analytics, documentation practices, and git-based project organization.
Activity
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Quality Metrics
Correctness93.0%
Maintainability93.0%
Architecture93.0%
Performance91.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
CSSHTMLJSONJavaScriptJupyter NotebookPythonSQLShell
Technical Skills
API IntegrationAnalysisCode CleanupContent EditingData AnalysisData CleaningData VisualizationDocumentationExcelExploratory Data AnalysisFile ManagementFoliumGeocodingJupyter NotebookJupyter Notebooks
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline