Mini-Projects Collection

This repository contains a collection of small, focused machine learning and data science mini-projects.
Each mini-project demonstrates a specific technique, workflow, or concept without the scale or depth of a full case study or flagship project.

Purpose

• Showcase technical breadth across different areas of data science
• Practice implementing clean, reproducible workflows
• Maintain modular, skill-focused notebooks that complement larger portfolio projects

Project Organization

Mini-projects are grouped by skill or technique category:

• machine_learning/ — Projects involving supervised learning models like classification and regression.
• data_cleaning/ — Projects focused on data wrangling, missing value handling, and data preparation techniques.
• visualization/ — Projects centered on data exploration and visualization.
• geospatial_projects/ — Projects involving spatial datasets, mapping, and geographic analysis.
• automation_scripts/ — Small scripts automating repetitive data tasks or workflow processes.

Each mini-project subfolder typically includes:

• A Jupyter Notebook
• A README.md file summarizing the project
• A requirements.txt listing necessary libraries

Notes

• Mini-projects are intended to be lightweight and self-contained.
• These notebooks prioritize practical application over exhaustive deep dives.
• More complex, domain-specific projects are housed in the Real-World Data Case Studies repository.

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This project is part of a broader portfolio showcasing practical applications of data science across analytics, visualization, and machine learning.
For more projects, visit My GitHub Portfolio.