ksgeist/Merrimack_DSE6630
May 2025 – Jul 2025
3 Months active
Languages Used
BibTeXHTMLMarkdownRTextR Markdown
Technical Skills
ClassificationData AnalysisData CleaningData ManipulationData MergingData Modeling
BugajskiSharp
PROFILE
Bugajskisharp
Krisha Bugajski developed robust analytics and data infrastructure for the ksgeist/Merrimack_DSE6630 repository, focusing on hospital readmission and gene expression analysis. Over three months, Krisha engineered scalable onboarding scaffolds, hardened data pipelines, and delivered machine learning models for classification and risk stratification, using R and Python alongside libraries like scikit-learn and Tidyverse. Her work included spatial data visualization, standardized data loading, and comprehensive technical documentation to support reproducibility and stakeholder communication. By integrating bioinformatics workflows and refining model reporting, Krisha enabled faster, more reliable analyses and actionable insights, demonstrating depth in data engineering, statistical modeling, and reproducible research practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
26Total
Bugs
0
Commits
26
Features
7
Lines of code
22,995
Activity Months3
Your Network
12 peopleWork History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) — Merrimack DSE6630 feature delivery and analysis refinement.
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) — Merrimack DSE6630 feature delivery and analysis refinement.
July 2025
June 2025
9 Commits • 3 Features
Jun 1, 2025June 2025 — Merrimack_DSE6630 monthly summary. Focused on delivering robust analytics, standardized data pipelines for ML demos, and enriched spatial visuals to support decision-making. Key features delivered: - Pneumonia readmission analytics and modeling enhancements: refactored the Random Forest model, clarified justifications in the reporting, and improved results reporting to enable clearer risk stratification and actionability. - Demo_2 dataset provisioning and loading standardization: added readyTrain/readyTest datasets, standardized data loading paths, and updated file paths with data aggregation explanations to accelerate ML demos and reduce onboarding time. - Project 2 spatial data analysis and visualization: completed spatial data analysis, map visualizations, and mortality trend visuals, including metadata, shapefile components, projections, and QA annotations to improve interpretability and governance of results. Major bugs fixed: - No separate bug fixes logged this month; stability improvements were embedded within feature work (model refactor, data-path hardening, and QA clarifications) to reduce support tickets and ensure reproducibility. Overall impact and accomplishments: - Business value: clearer risk insights for pneumonia readmission, faster and more reliable ML demos through standardized datasets, and actionable spatial visuals to support public health decisions. - Technical achievements: improved model reliability and interpretability, robust data-loading pipelines, and comprehensive QA/metadata coverage for reproducible analyses. Technologies/skills demonstrated: - Python, scikit-learn (Random Forest), data engineering and pipeline hardening, geospatial analysis (metadata, shapefiles, projections), data visualization, QA annotations, and documentation for reproducibility.
June 2025
9 Commits • 3 Features
Jun 1, 2025June 2025 — Merrimack_DSE6630 monthly summary. Focused on delivering robust analytics, standardized data pipelines for ML demos, and enriched spatial visuals to support decision-making. Key features delivered: - Pneumonia readmission analytics and modeling enhancements: refactored the Random Forest model, clarified justifications in the reporting, and improved results reporting to enable clearer risk stratification and actionability. - Demo_2 dataset provisioning and loading standardization: added readyTrain/readyTest datasets, standardized data loading paths, and updated file paths with data aggregation explanations to accelerate ML demos and reduce onboarding time. - Project 2 spatial data analysis and visualization: completed spatial data analysis, map visualizations, and mortality trend visuals, including metadata, shapefile components, projections, and QA annotations to improve interpretability and governance of results. Major bugs fixed: - No separate bug fixes logged this month; stability improvements were embedded within feature work (model refactor, data-path hardening, and QA clarifications) to reduce support tickets and ensure reproducibility. Overall impact and accomplishments: - Business value: clearer risk insights for pneumonia readmission, faster and more reliable ML demos through standardized datasets, and actionable spatial visuals to support public health decisions. - Technical achievements: improved model reliability and interpretability, robust data-loading pipelines, and comprehensive QA/metadata coverage for reproducible analyses. Technologies/skills demonstrated: - Python, scikit-learn (Random Forest), data engineering and pipeline hardening, geospatial analysis (metadata, shapefiles, projections), data visualization, QA annotations, and documentation for reproducibility.
May 2025
16 Commits • 3 Features
May 1, 2025May 2025 — Merrimack_DSE6630: Delivered foundational Team Alpha infrastructure, data pipeline hardening, and a classification model, delivering business value through scalable onboarding, reliable data prep, and actionable insights. The work spanned onboarding scaffolding, data pipeline reliability improvements, and a model-driven reporting flow, enabling faster, data-backed decision making for hospital readmissions analytics.
16 Commits • 3 Features
May 1, 2025May 2025 — Merrimack_DSE6630: Delivered foundational Team Alpha infrastructure, data pipeline hardening, and a classification model, delivering business value through scalable onboarding, reliable data prep, and actionable insights. The work spanned onboarding scaffolding, data pipeline reliability improvements, and a model-driven reporting flow, enabling faster, data-backed decision making for hospital readmissions analytics.
May 2025
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Quality Metrics
Correctness85.8%
Maintainability85.8%
Architecture81.6%
Performance77.4%
AI Usage29.6%
Skills & Technologies
Programming Languages
BibTeXHTMLMarkdownRR MarkdownText
Technical Skills
BioinformaticsClassificationData AnalysisData CleaningData LoadingData ManipulationData MergingData ModelingData PreparationData PreprocessingData TransformationData VisualizationData WranglingDocumentationElastic Net Regression
Repositories Contributed To
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