springboardmentor436z/FutureCart--AI-Driven-Demand-Prediction
Dec 2024 – Dec 2024
1 Month active
Languages Used
Jupyter NotebookPDFPython
Technical Skills
Data AnalysisData CleaningData VisualizationFeature EngineeringInternship ProjectMatplotlib
Bharathk099
PROFILE
Bharathk099
Developed core analytics and product readiness features for the FutureCart--AI-Driven-Demand-Prediction repository, focusing on e-commerce demand forecasting. Delivered an end-to-end Jupyter notebook that handled data loading, merging, cleaning, and temporal feature engineering, enabling robust time series analysis. Leveraged Python, Pandas, and Seaborn for data exploration and visualization, addressing missing values and outliers to support accurate forecasting. Contributed to application development and integration, implementing project-wide changes that streamlined forecasting workflows. Maintained repository hygiene by managing documentation, removing obsolete assets, and packaging deliverables such as demo videos and PDFs. Prioritized feature delivery and onboarding efficiency throughout the internship project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
3
Lines of code
36,693
Activity Months1
Your Network
16 peopleWork History
December 2024
9 Commits • 3 Features
Dec 1, 2024December 2024: Delivered core analytics and product readiness for FutureCart, including an end-to-end demand forecasting notebook, application development and integration, and documentation/asset housekeeping. No critical bugs logged; focus on feature delivery and repository hygiene to enable faster onboarding and data-driven decisions. Technologies demonstrated included Python, Jupyter notebooks, Pandas, data visualization, feature engineering, and documentation tooling.
9 Commits • 3 Features
Dec 1, 2024December 2024: Delivered core analytics and product readiness for FutureCart, including an end-to-end demand forecasting notebook, application development and integration, and documentation/asset housekeeping. No critical bugs logged; focus on feature delivery and repository hygiene to enable faster onboarding and data-driven decisions. Technologies demonstrated included Python, Jupyter notebooks, Pandas, data visualization, feature engineering, and documentation tooling.
December 2024
Activity
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Quality Metrics
Correctness82.2%
Maintainability82.2%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPDFPython
Technical Skills
Data AnalysisData CleaningData VisualizationFeature EngineeringInternship ProjectMatplotlibPandasSeabornStatsmodelsTime Series Analysis
Repositories Contributed To
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