racousin/data_science_practice_2024
Nov 2024 – Nov 2024
1 Month active
Languages Used
PythonSQL
Technical Skills
API IntegrationBeautifulSoupData AnalysisData CleaningData CollectionData Merging
NathanDufournaud
PROFILE
Nathandufournaud
Nathan Dufournaud developed two end-to-end data science features in the racousin/data_science_practice_2024 repository, focusing on forecasting and data exploration workflows. He built a quantity sold prediction pipeline that collects, preprocesses, and merges data from multiple sources, applies feature engineering, and trains a linear regression model to generate submission files based on product and store attributes. Additionally, he created a Jupyter Notebook for data collection and exploratory analysis, enabling faster understanding of numerical features through visualizations. His work demonstrated practical use of Python, pandas, and scikit-learn, emphasizing reproducibility, clear commit traceability, and notebook-based workflows for reliable results.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
7,491
Activity Months1
Your Network
15 people
Work History
November 2024
3 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11: Delivered two end-to-end data science features in racousin/data_science_practice_2024 that directly support forecasting and data exploration workflows. Overall impact: enabled more reliable quantity_sold forecasting, faster data understanding for module 3, and improved reproducibility through notebook-based workflows and explicit commit traceability. Technologies/skills demonstrated: Python, pandas, Jupyter notebooks, data collection pipelines, feature engineering, linear regression baseline modeling, and version-controlled development.
3 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11: Delivered two end-to-end data science features in racousin/data_science_practice_2024 that directly support forecasting and data exploration workflows. Overall impact: enabled more reliable quantity_sold forecasting, faster data understanding for module 3, and improved reproducibility through notebook-based workflows and explicit commit traceability. Technologies/skills demonstrated: Python, pandas, Jupyter notebooks, data collection pipelines, feature engineering, linear regression baseline modeling, and version-controlled development.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
API IntegrationBeautifulSoupData AnalysisData CleaningData CollectionData MergingData PreprocessingData VisualizationFeature EngineeringJupyter NotebookLinear RegressionMachine LearningMatplotlibModel TrainingPandas
Repositories Contributed To
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