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Project
Machine Learning Projects
Applied ML models for defect prediction, demand forecasting, and operational classification — integrated into BI workflows.
Pythonscikit-learnPandasJupyterPower BI
Project Overview
A series of applied machine learning prototypes and production models used to support operational decisions in quality and supply chain contexts.
Business Context
Operational teams relied largely on lagging indicators and intuition, with limited use of predictive analytics in day-to-day decisions.
Solution
Developed classification and regression models, validated with cross-validation and business-driven metrics, then exposed results in Power BI for adoption.
Technologies
Pythonscikit-learnPandasJupyterPower BI
Screenshots Gallery
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Video Demonstration
Video walkthrough will be added here.
Key Learnings
- Model adoption depends on UX as much as accuracy.
- Feature engineering with domain experts beats brute-force modeling.
- Connecting ML output to existing BI tools accelerates trust.
Business Value
- Improved support for proactive decision-making.
- Reusable data pipelines for future ML use cases.
- Closer collaboration between data and operations teams.
Other Projects
Power BI Executive Dashboards
Enterprise BI models and executive storyboards built to give leadership a single source of truth across operations, sales, and quality.
Process Documentation Platform
Structured documentation platform linking SOPs to owners, KPIs, and audit evidence — replacing fragmented SharePoint and Excel files.
Power Apps & Power Automate Workflows
Low-code applications and automated flows replacing manual approval, intake, and SLA-tracking processes across departments.
Supply Chain Analytics
End-to-end supply chain analytics covering inventory health, OTIF performance, and segmentation across distribution nodes.