clessn/datagotchi_federal_2024
Nov 2024 – Mar 2025
4 Months active
Languages Used
R
Technical Skills
Data CleaningData TransformationData WranglingFeature EngineeringR ProgrammingData Modeling
NicholasGaudet
PROFILE
Nicholasgaudet
Over four months, NIGAU108@ulaval.ca developed and maintained robust data pipelines for the clessn/datagotchi_federal_2024 repository, focusing on data cleaning, transformation, and machine learning in R. They engineered end-to-end workflows for lifestyle, user preferences, socio-economic status, and demographic datasets, standardizing variable formats and improving data integrity. Their work included building a multinomial logistic regression pipeline for vote-choice prediction, implementing feature engineering, and ensuring analytics readiness through harmonized data structures. By refactoring data sources and enhancing code readability, they enabled more reliable reporting and downstream modeling. The technical approach demonstrated strong skills in R programming, data wrangling, and statistical analysis.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
5
Lines of code
2,984
Activity Months4
Your Network
18 peopleSame Organization
@ulaval.ca7
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for clessn/datagotchi_federal_2024: Delivered end-to-end data cleaning and preparation pipelines for Socio-Economic Status and Demographics in R. Implemented data loading, cleaning, transforming, and structuring across demographics, lifestyle, and SES domains. Refactored SES handling to a new data source and standardized categorical variables into numeric or factor formats. Produced a clean, analysis-ready dataset to accelerate analytics and reporting. Evidence of work includes commits: 3e9ce535521863206c522277b6a86eabfa1a24b0 (Creation Fichier) and 49f17f05010be411ee421b95d477fe80155bd3bb (premiere vague).
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for clessn/datagotchi_federal_2024: Delivered end-to-end data cleaning and preparation pipelines for Socio-Economic Status and Demographics in R. Implemented data loading, cleaning, transforming, and structuring across demographics, lifestyle, and SES domains. Refactored SES handling to a new data source and standardized categorical variables into numeric or factor formats. Produced a clean, analysis-ready dataset to accelerate analytics and reporting. Evidence of work includes commits: 3e9ce535521863206c522277b6a86eabfa1a24b0 (Creation Fichier) and 49f17f05010be411ee421b95d477fe80155bd3bb (premiere vague).
March 2025
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for clessn/datagotchi_federal_2024: Delivered an end-to-end Multinomial Logistic Regression ML pipeline for vote-choice prediction, including data loading, model training, prediction generation, and evaluation via a confusion matrix, with model persistence for reuse. Implemented robust data preparation: defined variables, missing-value handling, and harmonized data structure and naming across training/testing (DfTrain/DfTest, dv_voteChoice) to ensure reliable future training. The work is accompanied by a clear commit trail across bootstrap, variable organization, and modeling enhancements, positioning the project for scalable experimentation and deployment.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for clessn/datagotchi_federal_2024: Delivered an end-to-end Multinomial Logistic Regression ML pipeline for vote-choice prediction, including data loading, model training, prediction generation, and evaluation via a confusion matrix, with model persistence for reuse. Implemented robust data preparation: defined variables, missing-value handling, and harmonized data structure and naming across training/testing (DfTrain/DfTest, dv_voteChoice) to ensure reliable future training. The work is accompanied by a clear commit trail across bootstrap, variable organization, and modeling enhancements, positioning the project for scalable experimentation and deployment.
December 2024
4 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for clessn/datagotchi_federal_2024. Delivered key data-cleaning enhancements for pet ownership data and resolved a formatting issue to improve reliability of the analytics pipeline. This work focused on business value, enabling clearer reporting and more accurate downstream modeling through stronger data categorization, ownership-vs-no-pets differentiation, and standardized category casing. All changes are tracked with focused commits to ensure traceability and reproducibility.
4 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for clessn/datagotchi_federal_2024. Delivered key data-cleaning enhancements for pet ownership data and resolved a formatting issue to improve reliability of the analytics pipeline. This work focused on business value, enabling clearer reporting and more accurate downstream modeling through stronger data categorization, ownership-vs-no-pets differentiation, and standardized category casing. All changes are tracked with focused commits to ensure traceability and reproducibility.
December 2024
November 2024
3 Commits • 2 Features
Nov 1, 2024November 2024: Delivered data cleaning and feature engineering for lifestyle and user preferences datasets in clessn/datagotchi_federal_2024. Refactored to descriptive string labels to improve readability and downstream reporting. Implemented robust population logic for new cleaned columns to ensure consistent capture of user preferences.
November 2024
3 Commits • 2 Features
Nov 1, 2024November 2024: Delivered data cleaning and feature engineering for lifestyle and user preferences datasets in clessn/datagotchi_federal_2024. Refactored to descriptive string labels to improve readability and downstream reporting. Implemented robust population logic for new cleaned columns to ensure consistent capture of user preferences.
Activity
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Quality Metrics
Correctness85.8%
Maintainability85.0%
Architecture75.0%
Performance76.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
R
Technical Skills
Data CleaningData ModelingData PreprocessingData TransformationData WranglingFeature EngineeringMachine LearningR ProgrammingStatistical Analysis
Repositories Contributed To
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