springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Dec 2024
1 Month active
Languages Used
MarkdownPython
Technical Skills
ARIMAARIMA ModelingARIMAXData AnalysisData ScienceData Visualization
Aravindhan S
PROFILE
Aravindhan S
Developed an AI-driven demand forecasting system for FutureCart, focusing on e-commerce inventory optimization and pricing decisions. Leveraged Python and Jupyter Notebook to implement ARIMA and SARIMA models, integrating Optuna for hyperparameter tuning and robust evaluation. The project, maintained in the springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction repository, included end-to-end notebooks with scenario analyses, forecast dashboards, and comprehensive visualizations to support stakeholder decision-making. Enhanced documentation and repository hygiene ensured reproducibility and collaboration, while obsolete code was removed to stabilize the codebase. This work established a data-driven foundation for proactive demand sensing and laid the groundwork for future production integration and workflow automation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
1
Lines of code
13,581
Activity Months1
Your Network
17 peopleSame Organization
@sonatech.ac.in1
Work History
December 2024
13 Commits • 1 Features
Dec 1, 2024December 2024 highlights: Delivered an AI-driven demand forecasting system for FutureCart leveraging ARIMA/SARIMA models with Optuna-based hyperparameter tuning, robust evaluation, and visualization. Includes end-to-end notebooks, project deliverables, and stakeholder-ready outputs for e-commerce demand forecasting. Documentation and repository hygiene improved to support reproducibility and collaboration. This work provides a data-driven foundation for inventory optimization and pricing decisions, enabling proactive demand sensing and reduced stockouts.
13 Commits • 1 Features
Dec 1, 2024December 2024 highlights: Delivered an AI-driven demand forecasting system for FutureCart leveraging ARIMA/SARIMA models with Optuna-based hyperparameter tuning, robust evaluation, and visualization. Includes end-to-end notebooks, project deliverables, and stakeholder-ready outputs for e-commerce demand forecasting. Documentation and repository hygiene improved to support reproducibility and collaboration. This work provides a data-driven foundation for inventory optimization and pricing decisions, enabling proactive demand sensing and reduced stockouts.
December 2024
Activity
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Quality Metrics
Correctness72.4%
Maintainability72.4%
Architecture72.4%
Performance72.4%
AI Usage26.2%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
ARIMAARIMA ModelingARIMAXData AnalysisData ScienceData VisualizationDocumentationHyperparameter TuningJupyter NotebookMachine LearningOptunaProject ManagementSARIMATime Series Analysis
Repositories Contributed To
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