springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Dec 2024
1 Month active
Languages Used
Python
Technical Skills
ARIMAARIMAXData AnalysisData VisualizationHyperparameter TuningJupyter Notebook
abhignakumar24
PROFILE
Abhignakumar24
Abhigna Kumar developed two core features for the FutureCart--AI-Driven-Demand-Prediction repository, focusing on enhancing demand forecasting capabilities. She implemented and tuned ARIMAX and SARIMAX models in Python, leveraging Pandas and Statsmodels to incorporate seasonal factors and optimize forecast accuracy, as measured by MAE, RMSE, and MSE. Her work included hyperparameter tuning with Optuna to further refine model performance. To support stakeholder engagement, she added demonstration assets and streamlined the repository by removing outdated notebooks. This combination of advanced time series modeling and repository hygiene improved both the technical robustness and presentation readiness of the project within one month.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
15,876
Activity Months1
Your Network
16 peopleWork History
December 2024
4 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for FutureCart AI-Driven-Demand-Prediction: Delivered two main feature areas with measurable business value and improved repository readiness. Implemented and tuned ARIMAX/SARIMAX demand forecasting models incorporating seasonal factors, resulting in improved forecast accuracy evidenced by better MAE, RMSE, and MSE metrics. Added demo assets and performed repository cleanup by removing obsolete notebooks to streamline demonstrations for stakeholders. These efforts enhance inventory planning accuracy and support compelling demos, while showcasing competencies in time-series modeling, hyperparameter tuning, data hygiene, and presentation readiness.
4 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for FutureCart AI-Driven-Demand-Prediction: Delivered two main feature areas with measurable business value and improved repository readiness. Implemented and tuned ARIMAX/SARIMAX demand forecasting models incorporating seasonal factors, resulting in improved forecast accuracy evidenced by better MAE, RMSE, and MSE metrics. Added demo assets and performed repository cleanup by removing obsolete notebooks to streamline demonstrations for stakeholders. These efforts enhance inventory planning accuracy and support compelling demos, while showcasing competencies in time-series modeling, hyperparameter tuning, data hygiene, and presentation readiness.
December 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
ARIMAARIMAXData AnalysisData VisualizationHyperparameter TuningJupyter NotebookMachine LearningMatplotlibNumPyOptunaPandasSARIMASARIMAXScikit-learnSeaborn
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