racousin/data_science_practice_2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
CSVHTMLPythonShellTextJupyter Notebook
Technical Skills
API IntegrationBeautifulSoupData AggregationData AnalysisData CollectionData Preprocessing
Glttr
PROFILE
Glttr
During two months on the racousin/data_science_practice_2025 repository, Gleteurtre developed and refined end-to-end data science pipelines for practical exercises spanning data collection, preprocessing, feature engineering, and model evaluation. He implemented reproducible workflows for tasks such as house price prediction and electricity demand forecasting, leveraging Python, pandas, and scikit-learn to ensure data quality and maintainability. His work included building modular utilities, integrating external data sources, and establishing cross-validated regression models for time series analysis. By resolving merge conflicts and improving repository hygiene, Gleteurtre enhanced onboarding speed and project reliability, demonstrating depth in both technical execution and workflow design.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
7
Lines of code
55,760
Activity Months2
Your Network
22 people
Work History
October 2025
4 Commits • 2 Features
Oct 1, 2025Month: 2025-10 — Performance review-ready monthly summary for repository racousin/data_science_practice_2025. Delivered two end-to-end features and fixed a critical merge issue to advance practical data science workflow. Key features delivered: - Module 5 Exercise 1: End-to-end data science pipeline (data download, preprocessing, feature engineering, model evaluation) and submission generation, with data updates and corrections. - Module 6 Exercise 1: Time-series regression model development for end-of-day return, including data collection, exploratory data analysis, and cross-validated model evaluation. Major bugs fixed: - Resolved a merge conflict and integrated the Module 6 Exercise 1 changes (commit a626955a) to ensure a clean, consistent implementation pathway. - Incorporated data updates and corrections for Module 5 Exercise 1 to improve dataset quality and reproducibility. Overall impact and accomplishments: - Established a robust end-to-end forecasting pipeline, enabling accurate end-of-day return predictions and electricity demand forecasting data workflows. - Improved data quality, reproducibility, and readiness for submission, reducing cycle time for practice assessments. - Demonstrated a clear, business-value-oriented workflow across data collection, feature engineering, model evaluation, and deployment preparation. Technologies/skills demonstrated: - Python data science stack (pandas, numpy, scikit-learn), time-series modelling, cross-validation, feature engineering, and end-to-end pipeline construction. - Git-based collaboration and change management, including conflict resolution and integrated commits. - Data quality management and submission-file generation for practice-based forecasting tasks.
4 Commits • 2 Features
Oct 1, 2025Month: 2025-10 — Performance review-ready monthly summary for repository racousin/data_science_practice_2025. Delivered two end-to-end features and fixed a critical merge issue to advance practical data science workflow. Key features delivered: - Module 5 Exercise 1: End-to-end data science pipeline (data download, preprocessing, feature engineering, model evaluation) and submission generation, with data updates and corrections. - Module 6 Exercise 1: Time-series regression model development for end-of-day return, including data collection, exploratory data analysis, and cross-validated model evaluation. Major bugs fixed: - Resolved a merge conflict and integrated the Module 6 Exercise 1 changes (commit a626955a) to ensure a clean, consistent implementation pathway. - Incorporated data updates and corrections for Module 5 Exercise 1 to improve dataset quality and reproducibility. Overall impact and accomplishments: - Established a robust end-to-end forecasting pipeline, enabling accurate end-of-day return predictions and electricity demand forecasting data workflows. - Improved data quality, reproducibility, and readiness for submission, reducing cycle time for practice assessments. - Demonstrated a clear, business-value-oriented workflow across data collection, feature engineering, model evaluation, and deployment preparation. Technologies/skills demonstrated: - Python data science stack (pandas, numpy, scikit-learn), time-series modelling, cross-validation, feature engineering, and end-to-end pipeline construction. - Git-based collaboration and change management, including conflict resolution and integrated commits. - Data quality management and submission-file generation for practice-based forecasting tasks.
October 2025
September 2025
8 Commits • 5 Features
Sep 1, 2025September 2025 performance summary for racousin/data_science_practice_2025: Delivered core data lifecycle enhancements, repository hygiene improvements, and foundational ML exercise pipelines across modules 1, 3, and 4. Established reusable tooling and data handling patterns to accelerate future exercise development and ensure reproducible experiments. This work improves data reliability, onboarding speed, and overall project maintainability.
September 2025
8 Commits • 5 Features
Sep 1, 2025September 2025 performance summary for racousin/data_science_practice_2025: Delivered core data lifecycle enhancements, repository hygiene improvements, and foundational ML exercise pipelines across modules 1, 3, and 4. Established reusable tooling and data handling patterns to accelerate future exercise development and ensure reproducible experiments. This work improves data reliability, onboarding speed, and overall project maintainability.
Activity
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Quality Metrics
Correctness81.8%
Maintainability81.8%
Architecture81.8%
Performance78.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVHTMLJupyter NotebookPythonShellText
Technical Skills
API IntegrationBeautifulSoupCross-ValidationData AggregationData AnalysisData CollectionData PreprocessingData ScienceData VisualizationFeature EngineeringFile ManagementLightGBMMachine LearningMatplotlibModel Evaluation
Repositories Contributed To
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