springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Jan 2025
2 Months active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
AIARIMAData AnalysisData ScienceData VisualizationDocumentation
CHRIS_JOY
PROFILE
Chris_joy
Chris Joy contributed to the FutureCart--AI-Driven-Demand-Prediction repository by building and refining a foundation for AI-driven demand forecasting. Over two months, Chris established reproducible Jupyter Notebooks and managed assets to streamline experimentation and onboarding. Using Python, ARIMA, and Optuna, Chris focused on time series analysis and hyperparameter tuning to support robust model prototyping. The work included removing obsolete and duplicate files, updating documentation, and improving repository hygiene, which enhanced maintainability and accelerated iteration cycles. By consolidating assets and clarifying project scope, Chris enabled more reliable demand planning insights and ensured the repository remained current and accessible for future development.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
25Total
Bugs
2
Commits
25
Features
5
Lines of code
28,316
Activity Months2
Your Network
16 peopleWork History
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly performance summary for the repository springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction. Focused on asset management for the AI-driven demand forecasting workflow. Delivered a new Jupyter Notebook to advance experimentation and predictions, and removed an obsolete notebook to ensure asset currency and reduce confusion. These changes improve reproducibility, accelerate experimentation cycles, and strengthen the reliability of insights used for demand planning.
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly performance summary for the repository springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction. Focused on asset management for the AI-driven demand forecasting workflow. Delivered a new Jupyter Notebook to advance experimentation and predictions, and removed an obsolete notebook to ensure asset currency and reduce confusion. These changes improve reproducibility, accelerate experimentation cycles, and strengthen the reliability of insights used for demand planning.
January 2025
December 2024
23 Commits • 4 Features
Dec 1, 2024December 2024 monthly summary for the FutureCart AI-Driven-Demand-Prediction project. Focused on establishing a stable asset and notebook foundation, improving repository hygiene, and documenting the project for faster onboarding and reliable experimentation.
December 2024
23 Commits • 4 Features
Dec 1, 2024December 2024 monthly summary for the FutureCart AI-Driven-Demand-Prediction project. Focused on establishing a stable asset and notebook foundation, improving repository hygiene, and documenting the project for faster onboarding and reliable experimentation.
Activity
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Quality Metrics
Correctness89.6%
Maintainability89.6%
Architecture89.6%
Performance89.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
AIARIMAData AnalysisData ScienceData VisualizationDocumentationHyperparameter TuningMachine LearningOptunaPythonRepository ManagementTechnical WritingTime Series AnalysisTime Series Forecasting
Repositories Contributed To
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