NewsAppsUMD/maryland_voter_data
Apr 2025 – May 2025
2 Months active
Languages Used
CSVGroq APIPython
Technical Skills
AI IntegrationAI Prompt EngineeringAPI IntegrationBatch ProcessingData AnalysisData Cleaning
rushabhkamdar44
PROFILE
Rushabhkamdar44
Rohan Kamdar developed a Groq-based Voter Data Insight Engine for the NewsAppsUMD/maryland_voter_data repository, delivering an end-to-end AI-driven pipeline for processing and summarizing Maryland voter data. He implemented batch processing, retry logic, and prompt engineering using Python and the Groq API to ensure reliability and scalability for large datasets. His work included preparing and cleaning 2024 analytics data, refining prompts for qualitative, county-level insights, and enabling journalistic-style summaries with diverse age representation. Through incremental commits and version control, Rohan ensured traceability and reproducibility, demonstrating depth in data analysis, AI integration, and robust scripting for election-focused analytics.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
3
Lines of code
20,165
Activity Months2
Your Network
33 peopleShared Repositories
3
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary for NewsAppsUMD/maryland_voter_data: Key feature delivered was Groq API Voter Data Prompt Refinement enabling comprehensive statewide summaries and county quick hits; outputs now require qualitative reporting, diverse age representation, and standout facts from each county to support journalistic-style briefs. No major bugs reported; minor prompt tweaks applied to improve consistency. Business value: improved accuracy, faster production of media-ready briefs, and better county-level coverage. Technologies/skills demonstrated: prompt engineering, Groq prompt design, data storytelling, version control.
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary for NewsAppsUMD/maryland_voter_data: Key feature delivered was Groq API Voter Data Prompt Refinement enabling comprehensive statewide summaries and county quick hits; outputs now require qualitative reporting, diverse age representation, and standout facts from each county to support journalistic-style briefs. No major bugs reported; minor prompt tweaks applied to improve consistency. Business value: improved accuracy, faster production of media-ready briefs, and better county-level coverage. Technologies/skills demonstrated: prompt engineering, Groq prompt design, data storytelling, version control.
May 2025
April 2025
11 Commits • 2 Features
Apr 1, 2025Monthly summary for 2025-04: Implemented a production-ready Groq-based Voter Data Insight Engine within NewsAppsUMD/maryland_voter_data, plus data prep for 2024 analytics. Key deliverables include an end-to-end Groq AI-driven data insight pipeline (Groq client, chat completion flow, refined prompts) and Python scripts that process and summarize voter data with batch processing and retry logic for reliability and scalability. County-level detail enhancements, randomized fact generation, and 2024-specific analyses were added to deliver actionable insights at scale. Completed 2024 analytics data prep by creating 2024_agg_md.csv and stripping non-essential fields to sharpen analytics for the 2024 election. The month also included substantial improvements to the Groq prompts framework and bot integration, supported by batch testing and iterative prompt refinements. All work was organized in incremental commits to improve traceability and collaboration.
April 2025
11 Commits • 2 Features
Apr 1, 2025Monthly summary for 2025-04: Implemented a production-ready Groq-based Voter Data Insight Engine within NewsAppsUMD/maryland_voter_data, plus data prep for 2024 analytics. Key deliverables include an end-to-end Groq AI-driven data insight pipeline (Groq client, chat completion flow, refined prompts) and Python scripts that process and summarize voter data with batch processing and retry logic for reliability and scalability. County-level detail enhancements, randomized fact generation, and 2024-specific analyses were added to deliver actionable insights at scale. Completed 2024 analytics data prep by creating 2024_agg_md.csv and stripping non-essential fields to sharpen analytics for the 2024 election. The month also included substantial improvements to the Groq prompts framework and bot integration, supported by batch testing and iterative prompt refinements. All work was organized in incremental commits to improve traceability and collaboration.
Activity
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Quality Metrics
Correctness81.6%
Maintainability81.6%
Architecture73.8%
Performance67.6%
AI Usage66.2%
Skills & Technologies
Programming Languages
CSVGroq APIPython
Technical Skills
AI IntegrationAI Prompt EngineeringAPI IntegrationBatch ProcessingData AnalysisData CleaningData ExtractionData FormattingData ProcessingError HandlingGroq APIPandasPrompt EngineeringPythonPython Development
Repositories Contributed To
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