DS-AI-VEH-Portfolio

Author - Sumedh Bhalerao
DS-AI-VEH-Portfolio/
│
├── README.md # Portfolio overview (this file)
├── LICENSE
├── requirements.txt
│
├── project1_vehicle_telemetry_analytics/
│ ├── README.md # Project-specific documentation
│ ├── notebooks/ # Jupyter notebooks (pipeline + data generation)
│ ├── docs/ # Architecture, data model, assumptions
│ ├── sql/ # Schema and table documentation
│ ├── powerbi/ # BI visuals and descriptions
│ ├── data/ # Folder structure only (no raw data)
│ └── outputs/ # Generated analytics (ignored in Git)
│
└── (future projects)

#Overview

This repository is a curated portfolio of Data Science, Analytics, and AI engineering projects focused on end-to-end analytical systems rather than isolated models.
Each project demonstrates how raw data is:
*Ingested and validated
*Transformed into analytics-ready datasets
*Persisted in relational databases
*Consumed by downstream visualization and decision-support tools
The emphasis is on real-world engineering patterns: modular pipelines, reproducibility, scalability, and clear separation between data ingestion, analytics logic, and reporting layers.

#What this portfolio demonstrates
*Time-series data processing and analytics
*Feature engineering from high-frequency telemetry
*Modular analytics design (multiple analytical views from the same data)
*SQL-based data persistence for BI tools
*Integration with visualization platforms (Power BI)
*Clear documentation of assumptions, data models, and limitations

This portfolio intentionally avoids domain-specific or proprietary details while preserving the technical structure and rigor of production analytics systems.

#Projects
Project Description Key Skills

#Project 01 – Vehicle Telemetry Analytics Modular analytics pipeline that processes raw telemetry data into multiple analytical datasets, stores them in SQL, and enables interactive BI dashboards Python, Pandas, Time-Series Analysis, SQL, Power BI, FFT, Data Modeling

#Each project is self-contained and includes:
*Its own README
*Architecture and data-model documentation
*Analytics definitions
*Sample outputs (non-sensitive)

#How to use this repository
*Browse the project READMEs to understand objectives and architecture
*Review the docs/ folders for detailed technical explanations
*Inspect notebooks for implementation details
*Use sample outputs to understand analytical results without running the full pipeline
*This repository is designed for code review and architectural understanding, not for distributing raw datasets.

#Disclaimer
All data used in this repository is synthetic or anonymized.
Project structures and analytical workflows are generic representations intended for learning and demonstration purposes only.
No proprietary algorithms, datasets, or confidential information are included.

#License
This repository is released under the MIT License.
You are free to use, modify, and adapt the code with attribution.

#About the author
This portfolio reflects professional experience in:
*Data science and analytics
*Time-series and signal-based analytics
*End-to-end analytical system design
*Bridging engineering, analytics, and decision-making layers

For questions or discussions, feel free to connect via GitHub.