springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Dec 2024
1 Month active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
ARIMACode CleanupData AnalysisData DeletionData PreprocessingData Science
Jagadeeswari14
PROFILE
Jagadeeswari14
Jagadeeswari Arava developed end-to-end AI-driven demand forecasting capabilities for the FutureCart--AI-Driven-Demand-Prediction repository, focusing on building reproducible data pipelines and robust modeling workflows. She implemented data loading, exploratory analysis, preprocessing, and time-series feature engineering using Python, Pandas, and Jupyter Notebooks. Her work included developing and evaluating forecasting models such as SARIMAX, ARIMA, Random Forest, and multivariate regression to address retail demand prediction challenges. Jagadeeswari also improved repository hygiene by removing outdated notebooks and directories, reducing technical debt. She produced clear documentation and demo assets, supporting cross-functional collaboration and preparing the project for future deployment and stakeholder engagement.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
11Total
Bugs
1
Commits
11
Features
2
Lines of code
56,433
Activity Months1
Your Network
16 peopleWork History
December 2024
11 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for the FutureCart--AI-Driven-Demand-Prediction project: Implemented end-to-end AI-driven demand forecasting capabilities and repository hygiene improvements. Key features include data loading, exploratory data analysis (EDA), preprocessing, time-series feature engineering, and model implementations with evaluation across SARIMAX, ARIMA, Random Forest, and multivariate regression. Delivered notebooks and modeling workflows enabling reproducible experimentation and stakeholder-ready results. Produced presentation slides and demo video assets for project demonstrations and communications with stakeholders. Major maintenance included cleaning up legacy AI demand prediction notebooks/directories to streamline the repository and reduce technical debt. Documentation and demos support cross-functional collaboration and future deployment readiness.
11 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for the FutureCart--AI-Driven-Demand-Prediction project: Implemented end-to-end AI-driven demand forecasting capabilities and repository hygiene improvements. Key features include data loading, exploratory data analysis (EDA), preprocessing, time-series feature engineering, and model implementations with evaluation across SARIMAX, ARIMA, Random Forest, and multivariate regression. Delivered notebooks and modeling workflows enabling reproducible experimentation and stakeholder-ready results. Produced presentation slides and demo video assets for project demonstrations and communications with stakeholders. Major maintenance included cleaning up legacy AI demand prediction notebooks/directories to streamline the repository and reduce technical debt. Documentation and demos support cross-functional collaboration and future deployment readiness.
December 2024
Activity
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Quality Metrics
Correctness91.0%
Maintainability91.0%
Architecture91.0%
Performance85.4%
AI Usage23.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
ARIMACode CleanupData AnalysisData DeletionData PreprocessingData ScienceData VisualizationFeature EngineeringForecastingGridSearchCVJupyter NotebookJupyter NotebooksLinear RegressionMachine LearningMatplotlib
Repositories Contributed To
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