springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Dec 2024
1 Month active
Languages Used
PythonSQL
Technical Skills
Data AnalysisExploratory Data AnalysisFeature EngineeringMachine LearningMatplotlibNumPy
bhavajna
PROFILE
Bhavajna
Bhavajna Madivada developed an AI-driven demand forecasting notebook for the FutureCart--AI-Driven-Demand-Prediction repository, focusing on building a robust, end-to-end data science workflow. The solution included data loading, dataset merging, exploratory data analysis, outlier detection and handling, and temporal feature engineering by extracting date-based features. Using Python, Pandas, and Scikit-learn, Bhavajna established a repeatable process that accelerates forecasting model experimentation and supports data-informed business decisions. The work emphasized clarity and reusability, with organized notebook assets to facilitate collaboration. While no bugs were addressed during this period, the depth of the feature delivered reflects a strong foundation for future enhancements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
3,888
Activity Months1
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12 — Delivered an AI-driven demand forecasting notebook in the FutureCart--AI-Driven-Demand-Prediction repo, enabling end-to-end data preparation for forecasting. The feature covers data loading, dataset merging, exploratory data analysis, outlier handling, and temporal feature engineering by extracting meaningful date-based features. No major bugs were fixed this month; the focus was on feature delivery and establishing a repeatable data science workflow that accelerates forecasting iterations and supports data-informed business decisions.
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12 — Delivered an AI-driven demand forecasting notebook in the FutureCart--AI-Driven-Demand-Prediction repo, enabling end-to-end data preparation for forecasting. The feature covers data loading, dataset merging, exploratory data analysis, outlier handling, and temporal feature engineering by extracting meaningful date-based features. No major bugs were fixed this month; the focus was on feature delivery and establishing a repeatable data science workflow that accelerates forecasting iterations and supports data-informed business decisions.
December 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisExploratory Data AnalysisFeature EngineeringMachine LearningMatplotlibNumPyOutlier DetectionPandasScikit-learnSeabornStatsmodels
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