This repository contains Python-based projects focused on data analysis, lightweight SQL usage, and introductory machine learning concepts.

The primary goal of this repo is to demonstrate practical, hands-on Python work using realistic datasets and workflows rather than toy examples.

• Practice Python for data analysis and automation
• Use SQL (primarily SQLite) as a lightweight data backend
• Explore data cleaning, aggregation, and visualization
• Gradually introduce machine learning concepts in a practical way
• Build a clean, well-documented portfolio of work

• Python 3
• SQLite (local, file-based databases)
• pandas
• NumPy
• scikit-learn (as projects progress)
• matplotlib / seaborn (as needed)

Note: Databases, raw data, and generated outputs are intentionally excluded from version control.

1. Clone the repository
2. Create a Python virtual environment
3. Install required dependencies
4. Run scripts or open notebooks to explore the analyses

SQLite databases are created locally by the scripts as needed.

• Warehouse / inventory-style data analysis
• Tracking incoming shipments and processing times
• Aggregation and trend analysis using SQL and pandas

Future work may include:

• Predicting processing times
• Basic classification and regression models
• Moving from SQLite to PostgreSQL or cloud databases for practice

If you're using this project, I'd love to hear from you! Whether you've:

• Found it helpful for learning
• Used it in your own work
• Have suggestions for improvements
• Found bugs or issues

Feel free to reach out by opening an issue or contacting me at [rteague111@gmail.com].

This repository is a work in progress and reflects ongoing learning and experimentation.