This repository showcases my practical experience across key data domains: Data Engineering: building robust data pipelines, managing ETL processes, and working with diverse storage solutions. Data Science: Applications of machine learning, statistical modeling, and predictive analytics to solve real-world problems.

• Tech Stack: React, FastAPI, MySQL, Tailwind CSS
• Features: Real-time analytics dashboard, player statistics, team performance metrics
• Status: Production Ready ✅

• Tech Stack: Python, Scikit-learn, Pandas, Jupyter
• Features: Machine learning model for diabetes risk assessment
• Status: Complete ✅

• Tech Stack: Python, Machine Learning, Data Analysis
• Features: Advanced fraud detection algorithms
• Status: Complete ✅

• Tech Stack: Python, Data Processing, Database Management
• Features: Automated data extraction, transformation, and loading
• Status: Complete ✅

• Tech Stack: Python, Data Visualization, Statistical Analysis
• Features: Gender representation analysis in Olympic sports
• Status: Complete ✅

• Tech Stack: Python, Data Science, Environmental Analytics
• Features: Renewable energy trends and sustainability metrics
• Status: Complete ✅

• Tech Stack: Python, Financial Analysis, Time Series
• Features: Stock price prediction and market analysis
• Status: Complete ✅

• Tech Stack: Python, NLP, Machine Learning
• Features: Automated text categorization and sentiment analysis
• Status: Complete ✅

• Tech Stack: Python, Statistical Analysis, Data Visualization
• Features: Global happiness trends and correlation analysis
• Status: Complete ✅

Languages: Python, JavaScript, SQL, HTML/CSS
Frameworks: React, FastAPI, Flask, Pandas, Scikit-learn
Databases: MySQL, SQLite, PostgreSQL
Tools: Jupyter, Git, Docker, VS Code
Cloud: Azure Cloud Platform

Each project contains its own README with specific setup instructions. General requirements:

# Python projects
pip install -r requirements.txt

# JavaScript projects
npm install

• Email: onuhj47@gmail.com
• GitHub: @JohnEdohOnuh
• LinkedIn: John Edoh Onuh

This portfolio is open source and available under the MIT License.

⭐ If you find these projects helpful, please star this repository!