racousin/data_science_practice_2024
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSVJupyter NotebookPython
Technical Skills
Cross-validationData EngineeringData PreprocessingData ScienceFeature EngineeringHyperparameter Tuning
Pollsiesdifi
PROFILE
Pollsiesdifi
Developed two end-to-end data science pipelines in the racousin/data_science_practice_2024 repository, focusing on machine learning model training and time series forecasting. Leveraged Python, Pandas, and Scikit-learn to implement robust data preprocessing, feature engineering, and cross-validation workflows. Applied Bayesian hyperparameter tuning and model evaluation techniques, generating reproducible submission outputs for assessment and competition scenarios. Addressed data inconsistencies and missing values to ensure pipeline reliability and scalability. Documented all changes for transparency and reproducibility, supporting onboarding and skill development. Delivered production-like artifacts, including automated CSV submissions, and incorporated cross-language support to facilitate broader usage and submission tracking within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
2,502
Activity Months1
Your Network
15 peopleShared Repositories
15
Work History
November 2024
3 Commits • 2 Features
Nov 1, 2024Month 2024-11 performance summary: Implemented two end-to-end data science exercise pipelines in racousin/data_science_practice_2024, delivering reproducible learning artifacts and production-like submission outputs. These efforts enhance onboarding, skill development, and assessment readiness, while reinforcing data handling standards and model evaluation discipline.
3 Commits • 2 Features
Nov 1, 2024Month 2024-11 performance summary: Implemented two end-to-end data science exercise pipelines in racousin/data_science_practice_2024, delivering reproducible learning artifacts and production-like submission outputs. These efforts enhance onboarding, skill development, and assessment readiness, while reinforcing data handling standards and model evaluation discipline.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability86.6%
Architecture86.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPython
Technical Skills
Cross-validationData EngineeringData PreprocessingData ScienceFeature EngineeringHyperparameter TuningLightGBMMachine LearningMatplotlibPandasPythonScikit-learnSeabornTime Series AnalysisXGBoost
Repositories Contributed To
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