clessn/datagotchi_federal_2024
Nov 2024 – Apr 2025
6 Months active
Languages Used
CSSHTMLMarkdownQMDQuartoRSQLShell
Technical Skills
Bayesian ModelingClusteringCode OrganizationData AnalysisData CleaningData Clustering
AlexBouillon
PROFILE
Alexbouillon
Alexandre Bouillon developed advanced analytics and visualization pipelines for the clessn/datagotchi_federal_2024 repository, focusing on clustering, geospatial mapping, and data-driven segmentation. He engineered modular workflows in R and Python, integrating techniques like PCA, K-Means, and HDBSCAN to analyze socio-economic and political data, and built reproducible pipelines for daily predictions and stakeholder reporting. His work included end-to-end data validation, feature engineering, and the creation of export-ready visualizations such as heatmaps and regional maps. By refactoring codebases, expanding test coverage, and improving data quality, Alexandre delivered maintainable, scalable solutions that support marketing, policy analysis, and automated analytics across multiple domains.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
111Total
Bugs
4
Commits
111
Features
29
Lines of code
60,956
Activity Months6
Your Network
11 people
Work History
April 2025
3 Commits • 1 Features
Apr 1, 2025Summary for 2025-04: Delivered the Tattoo Visualization Suite for datagotchi_federal_2024, providing a heatmap and map visuals that quantify tattoo prevalence by electoral riding. The R-based pipeline integrates spatial data with survey responses and computes weighted percentages. Refactored map generation to consume tattoo data, refined plotting logic, updated image save paths, and added branding/logo. Performed cleanup without altering core functionality. These changes establish a reproducible, export-ready visualization pipeline that supports stakeholder reporting and data-driven decisions.
3 Commits • 1 Features
Apr 1, 2025Summary for 2025-04: Delivered the Tattoo Visualization Suite for datagotchi_federal_2024, providing a heatmap and map visuals that quantify tattoo prevalence by electoral riding. The R-based pipeline integrates spatial data with survey responses and computes weighted percentages. Refactored map generation to consume tattoo data, refined plotting logic, updated image save paths, and added branding/logo. Performed cleanup without altering core functionality. These changes establish a reproducible, export-ready visualization pipeline that supports stakeholder reporting and data-driven decisions.
April 2025
March 2025
11 Commits • 2 Features
Mar 1, 2025March 2025 performance for clessn/datagotchi_federal_2024 focused on delivering end-to-end analytics features that enable data-driven marketing and policy insights. Delivered two major features: (1) Social-space analysis and clustering for Canadian socio-economic data, incorporating HDBSCAN-based clustering, Bourdieuan-inspired variable engineering, and a follow-on K-Means analysis with rich visualizations to inform user personas and segmentation. (2) Electoral ridings lifestyle and consumer preferences mapping, establishing a modular geospatial mapping foundation and domain-specific analyses for coffee, alcohol, meat, and pet ownership, with national and regional maps to support marketing, policy decisions, and regional strategy. Improvement work across data preparation, visualization pipelines, and maintainability, including clustering module reorganization and test coverage, sets the stage for scalable, reusable analytics and faster iteration.
March 2025
11 Commits • 2 Features
Mar 1, 2025March 2025 performance for clessn/datagotchi_federal_2024 focused on delivering end-to-end analytics features that enable data-driven marketing and policy insights. Delivered two major features: (1) Social-space analysis and clustering for Canadian socio-economic data, incorporating HDBSCAN-based clustering, Bourdieuan-inspired variable engineering, and a follow-on K-Means analysis with rich visualizations to inform user personas and segmentation. (2) Electoral ridings lifestyle and consumer preferences mapping, establishing a modular geospatial mapping foundation and domain-specific analyses for coffee, alcohol, meat, and pet ownership, with national and regional maps to support marketing, policy decisions, and regional strategy. Improvement work across data preparation, visualization pipelines, and maintainability, including clustering module reorganization and test coverage, sets the stage for scalable, reusable analytics and faster iteration.
February 2025
26 Commits • 8 Features
Feb 1, 2025February 2025 performance summary for clessn/datagotchi_federal_2024. Delivered robust feature refinements, expanded test coverage, and analytics groundwork across modules 05–07. Key outcomes include 05 core enhancements with tests and refactors, 06 core enhancements with migration to R and a v0 graph plus data saving for 06, and initial map/cluster enhancements in the 07 module. Addressed reliability and quality through broader test suites; Positron-related issues in 05 notebooks/tests required partial fixes and ongoing investigation; a 06 vote-intent bug was identified and tracked. Final cleanup for 07 and the groundwork for mapping/imputation features establish a solid platform for data-driven insights and scalable analytics.
26 Commits • 8 Features
Feb 1, 2025February 2025 performance summary for clessn/datagotchi_federal_2024. Delivered robust feature refinements, expanded test coverage, and analytics groundwork across modules 05–07. Key outcomes include 05 core enhancements with tests and refactors, 06 core enhancements with migration to R and a v0 graph plus data saving for 06, and initial map/cluster enhancements in the 07 module. Addressed reliability and quality through broader test suites; Positron-related issues in 05 notebooks/tests required partial fixes and ongoing investigation; a 06 vote-intent bug was identified and tracked. Final cleanup for 07 and the groundwork for mapping/imputation features establish a solid platform for data-driven insights and scalable analytics.
February 2025
January 2025
31 Commits • 8 Features
Jan 1, 2025January 2025 monthly summary for clessn/datagotchi_federal_2024. Focused on delivering core data science pipeline improvements, model alignment, advanced visualizations, and notebook-based workflows that accelerate clustering experiments and decision support. Highlights include batch script maintenance, cluster naming and descriptive statistics, 2022 model integration across components, graph-based visual analytics with realVoteIntent support, and extensive notebook versioning/deployment to streamline experimentation and collaboration.
January 2025
31 Commits • 8 Features
Jan 1, 2025January 2025 monthly summary for clessn/datagotchi_federal_2024. Focused on delivering core data science pipeline improvements, model alignment, advanced visualizations, and notebook-based workflows that accelerate clustering experiments and decision support. Highlights include batch script maintenance, cluster naming and descriptive statistics, 2022 model integration across components, graph-based visual analytics with realVoteIntent support, and extensive notebook versioning/deployment to streamline experimentation and collaboration.
December 2024
17 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for clessn/datagotchi_federal_2024: Key data quality, analytics, and repository hygiene work that enabled QA validation, richer segmentation insights, and improved maintainability. Delivered validated data pipelines, advanced clustering analytics, and cleaner project structure, driving reliable data-driven decisions and scalable development.
17 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for clessn/datagotchi_federal_2024: Key data quality, analytics, and repository hygiene work that enabled QA validation, richer segmentation insights, and improved maintainability. Delivered validated data pipelines, advanced clustering analytics, and cleaner project structure, driving reliable data-driven decisions and scalable development.
December 2024
November 2024
23 Commits • 7 Features
Nov 1, 2024Month 2024-11 — Delivered substantial clustering, modeling, and visualization enhancements for clessn/datagotchi_federal_2024. Key deliverables include PCA and clustering exploration on the Datagotchi pilot data (2021) with elbow and silhouette analyses and descriptive cluster documentation; refactored clustering workflow for better maintainability and up-to-date results; groundwork for variable selection and priors in voting data; multinomial model implementation for clustering results; and the development of a prediction pipeline skeleton (daily prediction generation, data merges, and centroid computation). Enhanced visual analytics in Quarto with final graphs, evolution graphs, and graph v1, culminating in the integration of final clustering results. This work strengthens data-driven insights, improves pipeline reliability, and lays the foundation for automated daily predictions and scalable analytics, delivering clear business value and reproducible workflows.
November 2024
23 Commits • 7 Features
Nov 1, 2024Month 2024-11 — Delivered substantial clustering, modeling, and visualization enhancements for clessn/datagotchi_federal_2024. Key deliverables include PCA and clustering exploration on the Datagotchi pilot data (2021) with elbow and silhouette analyses and descriptive cluster documentation; refactored clustering workflow for better maintainability and up-to-date results; groundwork for variable selection and priors in voting data; multinomial model implementation for clustering results; and the development of a prediction pipeline skeleton (daily prediction generation, data merges, and centroid computation). Enhanced visual analytics in Quarto with final graphs, evolution graphs, and graph v1, culminating in the integration of final clustering results. This work strengthens data-driven insights, improves pipeline reliability, and lays the foundation for automated daily predictions and scalable analytics, delivering clear business value and reproducible workflows.
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Quality Metrics
Correctness83.4%
Maintainability82.8%
Architecture80.4%
Performance72.2%
AI Usage20.4%
Skills & Technologies
Programming Languages
CSSHTMLJSONJupyter NotebookMarkdownPythonQMDQuartoRSQL
Technical Skills
Bayesian ModelingClusteringClustering AnalysisCode OrganizationData AnalysisData CleaningData ClusteringData FilteringData LoadingData MappingData MergingData ModelingData PreparationData PreprocessingData Processing
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