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
Developed an AI-driven demand forecasting notebook for the FutureCart--AI-Driven-Demand-Prediction repository, focusing on establishing a robust, repeatable data science workflow. The work encompassed end-to-end data preparation, including loading and merging datasets, performing exploratory data analysis, detecting and handling outliers, and engineering temporal features from date columns to enhance forecasting accuracy. Leveraged Python and SQL alongside libraries such as Pandas, Scikit-learn, and Matplotlib to support data exploration and model experimentation. The approach prioritized modularity and collaboration by organizing notebook assets for reuse, enabling faster iteration cycles and supporting data-informed business decisions without addressing major bug fixes during this period.
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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