A Collection of Rapid Prototypes & Experimental Implementations
This repository is a central hub for my smaller-scale technical explorations. It serves as a testing ground for new libraries, data processing techniques, and automation workflows. Each project is self-contained and demonstrates a specific application of Python and Data Science.
| Project | Domain | Tech Stack | Highlights |
|---|---|---|---|
| Data Cleaning Toolkit | Automation | Pandas, NumPy | Automated handling of missing values and outliers in large datasets. |
| Web Scraper Studio | Data Sourcing | BeautifulSoup, Selenium | Custom scrapers for extracting structured data from dynamic websites. |
| EDA Dashboard | Visualization | Matplotlib, Seaborn | Comprehensive exploratory analysis with automated plot generation. |
| Predictive Sandbox | Machine Learning | Scikit-Learn | Implementation of regression and classification models on small datasets. |
Across these projects, I leverage the following ecosystem:
- Languages: Python (Primary), SQL
- Data Processing: Pandas, NumPy, Scipy
- Visuals: Matplotlib, Seaborn, Plotly
- Machine Learning: Scikit-Learn (Classification, Clustering, Regression)
- Automation: OS, Request, Selenium
To maintain clarity, every project within this collection follows a standardized structure:
- Source Code: Well-documented
.pyor.ipynbfiles. - Data: A
data/folder containing sample datasets (or links to sources). - Outputs: An
images/orresults/folder showcasing the project's success.
- Clone the repo:
git clone https://github.com/DHARKIVE-STUDIO/Mini-Projects.git - Browse Folders: Each folder is named after the project it contains.
- Read Internal Docs: Check the local README inside each folder for specific installation and execution instructions.
Maintained by DHARKIVE-STUDIO | Continuous learning and prototyping.