Chameleon-company/MOP-Code
Apr 2025 – May 2025
2 Months active
Languages Used
CSSHTMLPython
Technical Skills
Data AnalysisData VisualizationJupyter NotebookMatplotlibPandasSeaborn
nikhilnaga1
PROFILE
Nikhilnaga1
Nikhil contributed to the Chameleon-company/MOP-Code repository by developing a comprehensive Melbourne housing market analysis notebook, enabling end-to-end data loading, preprocessing, and visualization for multi-year datasets. Using Python, Pandas, and Seaborn, he implemented cross-type trend analysis to compare price trajectories and geographic disparities among houses, townhouses, and apartments, surfacing actionable insights for market analytics. In addition, he improved data governance by restructuring analysis files into a versioned, publish-ready artifact system, streamlining downstream automation and review processes. Nikhil’s work demonstrated depth in data analysis and lifecycle management, with a focus on reproducibility, documentation, and maintainable Jupyter Notebook workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
950
Activity Months2
Your Network
98 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Chameleon-company/MOP-Code focusing on data lifecycle and publish-ready artifact organization.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for Chameleon-company/MOP-Code focusing on data lifecycle and publish-ready artifact organization.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 — Feature delivered in Chameleon-company/MOP-Code: Melbourne Housing Market Analysis Notebook Update. Updated use-case notebook to load and preprocess data (2000-2016), generate visualizations, and compare price trends and geographic disparities between houses, townhouses, and apartments. Highlighted divergence with houses outperforming apartments. Business value: provides data-driven insights for market analytics and strategic decision-making; supports product analytics and go-to-market planning. No major bugs fixed this month; maintenance tasks focused on data integrity and documentation. Technologies demonstrated: Python data processing, exploratory data analysis, visualization, and notebook-based analytics.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 — Feature delivered in Chameleon-company/MOP-Code: Melbourne Housing Market Analysis Notebook Update. Updated use-case notebook to load and preprocess data (2000-2016), generate visualizations, and compare price trends and geographic disparities between houses, townhouses, and apartments. Highlighted divergence with houses outperforming apartments. Business value: provides data-driven insights for market analytics and strategic decision-making; supports product analytics and go-to-market planning. No major bugs fixed this month; maintenance tasks focused on data integrity and documentation. Technologies demonstrated: Python data processing, exploratory data analysis, visualization, and notebook-based analytics.
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSHTMLPython
Technical Skills
Data AnalysisData VisualizationJupyter NotebookMatplotlibPandasSeaborn
Repositories Contributed To
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