springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Dec 2024
1 Month active
Languages Used
Jupyter NotebookMarkdownPython
Technical Skills
Code CleanupData ScienceData VisualizationDocumentationForecastingGrid Search
Bhavajna-Madivada
PROFILE
Bhavajna-madivada
Bhavajna Madivada contributed to the FutureCart--AI-Driven-Demand-Prediction repository by developing a grid-search based hyperparameter tuning workflow for multivariate Ridge regression models, improving demand forecast accuracy and reliability. She used Python, Pandas, and Scikit-learn to implement model evaluation with RMSE and integrated Matplotlib visualizations for forecast results. Bhavajna also prepared demo assets and presentation materials to support stakeholder engagement, organizing resources for efficient access. To enhance project maintainability, she expanded documentation scaffolding with updated READMEs and milestones, and performed code cleanup by removing outdated notebooks. Her work demonstrated depth in both technical implementation and project organization within a short timeframe.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
11Total
Bugs
1
Commits
11
Features
3
Lines of code
16,673
Activity Months1
Your Network
18 peopleSame Organization
@srmap.edu.in2
Work History
December 2024
11 Commits • 3 Features
Dec 1, 2024December 2024 was focused on delivering measurable business value for the FutureCart AI-Driven-Demand-Prediction project via model tuning, asset enablement, and repository hygiene. Key deliverables include a grid-search based Ridge regression hyperparameter tuning workflow for a multivariate forecast, the preparation of presentation and demo materials for stakeholder demonstrations, and ongoing documentation scaffolding with README updates. Additionally, outdated documentation and notebooks were cleaned up to reduce confusion and maintain a clean baseline for future work. These efforts collectively improved forecast reliability, enabled compelling demos for stakeholders, and strengthened maintainability and onboarding for the team.
11 Commits • 3 Features
Dec 1, 2024December 2024 was focused on delivering measurable business value for the FutureCart AI-Driven-Demand-Prediction project via model tuning, asset enablement, and repository hygiene. Key deliverables include a grid-search based Ridge regression hyperparameter tuning workflow for a multivariate forecast, the preparation of presentation and demo materials for stakeholder demonstrations, and ongoing documentation scaffolding with README updates. Additionally, outdated documentation and notebooks were cleaned up to reduce confusion and maintain a clean baseline for future work. These efforts collectively improved forecast reliability, enabled compelling demos for stakeholders, and strengthened maintainability and onboarding for the team.
December 2024
Activity
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Quality Metrics
Correctness98.2%
Maintainability98.2%
Architecture98.2%
Performance98.2%
AI Usage23.6%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPython
Technical Skills
Code CleanupData ScienceData VisualizationDocumentationForecastingGrid SearchHyperparameter TuningMachine LearningMatplotlibPandasProject PlanningPythonRidge RegressionScikit-learnTechnical Writing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline