🚀 Mini-Projects: Technical Sandbox

A Collection of Rapid Prototypes & Experimental Implementations

📌 Overview

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.

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📂 Project Directory

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.

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🛠 Tech Stack Overview

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

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🧪 Structural Standards

To maintain clarity, every project within this collection follows a standardized structure:

1. Source Code: Well-documented .py or .ipynb files.
2. Data: A data/ folder containing sample datasets (or links to sources).
3. Outputs: An images/ or results/ folder showcasing the project's success.

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🚀 How to Navigate

1. Clone the repo: git clone https://github.com/DHARKIVE-STUDIO/Mini-Projects.git
2. Browse Folders: Each folder is named after the project it contains.
3. Read Internal Docs: Check the local README inside each folder for specific installation and execution instructions.

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Maintained by DHARKIVE-STUDIO | Continuous learning and prototyping.