Chameleon-company/MOP-Code
Apr 2025 – Sep 2025
4 Months active
Languages Used
Jupyter NotebookPythonGitHTMLN/AJSONJavaScriptTypeScript
Technical Skills
Data AnalysisData VisualizationJupyter NotebookJupyter NotebooksLeaflet.jsMatplotlib
ajaiysentil
PROFILE
Ajaiysentil
Over four months, contributed to Chameleon-company/MOP-Code by building data-driven analytics and visualization tools for urban safety and pedestrian flow. Developed Jupyter Notebooks and web app components that integrated Python, JavaScript, and mapping libraries such as Leaflet.js and Folium to visualize safety indices and pedestrian activity. Enhanced repository hygiene through code cleanup, documentation improvements, and structured file management, supporting scalable collaboration. Implemented secure credential handling by replacing hardcoded API keys with environment variables. Delivered features including AI-driven pedestrian flow prediction, map-based safety scoring, and scenario-driven analysis pipelines, enabling stakeholders to make informed decisions based on clear, actionable data insights.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
26Total
Bugs
3
Commits
26
Features
8
Lines of code
856,207
Activity Months4
Your Network
98 peopleWork History
September 2025
9 Commits • 3 Features
Sep 1, 2025September 2025 focused on delivering core data and UI capabilities in Chameleon-company/MOP-Code. Key outcomes include a scalable Pedestrian Flow Prediction data pipeline with improved data quality and an initial visualization, secure credential handling by removing hardcoded API keys, JSON-driven configuration and UI scaffolding for use cases, enhanced map visualization with refined markers and coordinates, and essential maintenance to stabilize the repository. These efforts accelerate AI experimentation, reduce security risk, and improve the reliability and clarity of data dashboards for stakeholders.
9 Commits • 3 Features
Sep 1, 2025September 2025 focused on delivering core data and UI capabilities in Chameleon-company/MOP-Code. Key outcomes include a scalable Pedestrian Flow Prediction data pipeline with improved data quality and an initial visualization, secure credential handling by removing hardcoded API keys, JSON-driven configuration and UI scaffolding for use cases, enhanced map visualization with refined markers and coordinates, and essential maintenance to stabilize the repository. These efforts accelerate AI experimentation, reduce security risk, and improve the reliability and clarity of data dashboards for stakeholders.
September 2025
August 2025
4 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for Chameleon-company/MOP-Code: Focused on delivering data-driven analytics capabilities for pedestrian safety, securing notebook artifacts, and laying the groundwork for scalable, scenario-based analysis. The work emphasizes business value through early-stage analytics, reliable notebook hygiene, and a reusable data-fetching scaffold for multi-source inputs.
August 2025
4 Commits • 1 Features
Aug 1, 20252025-08 Monthly Summary for Chameleon-company/MOP-Code: Focused on delivering data-driven analytics capabilities for pedestrian safety, securing notebook artifacts, and laying the groundwork for scalable, scenario-based analysis. The work emphasizes business value through early-stage analytics, reliable notebook hygiene, and a reusable data-fetching scaffold for multi-source inputs.
May 2025
10 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Chameleon-company/MOP-Code focused on delivering data-driven safety analytics and strengthening repository hygiene to support scalable collaboration. Delivered two new safety features: Night Time Safety Index map visualization and Urban Safety Analytics & Scoring, alongside comprehensive repository cleanup and organization. These changes enable actionable night-time risk insights, improved location-level safety scoring, and a cleaner, scalable codebase for future work, driving better decisions and maintainability.
10 Commits • 3 Features
May 1, 2025May 2025 monthly summary for Chameleon-company/MOP-Code focused on delivering data-driven safety analytics and strengthening repository hygiene to support scalable collaboration. Delivered two new safety features: Night Time Safety Index map visualization and Urban Safety Analytics & Scoring, alongside comprehensive repository cleanup and organization. These changes enable actionable night-time risk insights, improved location-level safety scoring, and a cleaner, scalable codebase for future work, driving better decisions and maintainability.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025For 2025-04, delivered Night Time Safety Index Notebook Enhancements in Chameleon-company/MOP-Code. Implemented map circle markers for visualization, refined analysis workflow, integrated a lamp wattage histogram, and prepared a production-ready notebook. No major bugs reported this month; minor stability fixes were applied during the NTSI enhancements. The changes improve night-time safety risk visibility and data-driven decision-making for safety planning, enabling faster iterations and clearer stakeholder reporting. Demonstrated Python data analysis, Jupyter notebook proficiency, data visualization (maps and histograms), and robust version control through well-documented commits.
April 2025
3 Commits • 1 Features
Apr 1, 2025For 2025-04, delivered Night Time Safety Index Notebook Enhancements in Chameleon-company/MOP-Code. Implemented map circle markers for visualization, refined analysis workflow, integrated a lamp wattage histogram, and prepared a production-ready notebook. No major bugs reported this month; minor stability fixes were applied during the NTSI enhancements. The changes improve night-time safety risk visibility and data-driven decision-making for safety planning, enabling faster iterations and clearer stakeholder reporting. Demonstrated Python data analysis, Jupyter notebook proficiency, data visualization (maps and histograms), and robust version control through well-documented commits.
Activity
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Quality Metrics
Correctness90.8%
Maintainability90.8%
Architecture88.4%
Performance87.0%
AI Usage24.6%
Skills & Technologies
Programming Languages
GitHTMLJSONJavaScriptJupyter NotebookN/APythonTypeScript
Technical Skills
API IntegrationAPI ManagementCode CleanupCode OrganizationData AnalysisData CleaningData EngineeringData PreprocessingData VisualizationDocumentationEnvironment VariablesFile ManagementFoliumFront-end DevelopmentFrontend Development
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline