Welcome to my data analytics project portfolio.
Each project below demonstrates my ability to:
- Clean, transform, and analyze real‑world datasets
- Build insights using Python (Pandas, NumPy, Matplotlib, Seaborn)
- Perform SQL‑based exploratory analysis
- Communicate findings clearly through visualizations and narrative
All datasets are open‑source or fictional and used strictly for educational and portfolio purposes.
Python • Pandas • Matplotlib • Seaborn
https://img.shields.io/badge/Data-Sales%20Analytics-blue?style=for-the-badge
Goal: Analyze U.S. regional sales performance to uncover trends in products, pricing, sales channels, and team performance.
🔍 Key Questions Answered
- What products sold the most by year, month, and day?
- Which sales team member sold the most?
- Average unit price per team member
- Orders by sales channel
- Min/Avg/Max order price per customer
- Average markup/profit per unit (overall + by year/month)
🧠 Skills Demonstrated
- Data cleaning
- Data manipulation
- Data processing
- Data visualization
Python • Pandas • Seaborn
https://img.shields.io/badge/Data-Humanitarian%20Analysis-red?style=for-the-badge
Goal: Analyze open‑source fatality data to understand demographic and temporal patterns.
🔍 Key Questions Answered
- Age distribution (youngest/oldest victims)
- Victims by citizenship, gender, region
- Injury types and responsible groups
- Monthly and yearly fatality trends
🧠 Skills Demonstrated
- Data cleaning
- Data manipulation
- Data visualization
Python • Pandas • Matplotlib
https://img.shields.io/badge/Data-Crime%20Analysis-darkred?style=for-the-badge
Goal: Explore crime trends in Chicago by district, year, month, and arrest status.
🔍 Key Questions Answered
- Top 10 crime types
- Arrest vs non‑arrest percentages
- Crime counts by year, month, district
- Crime type trends over time
🧠 Skills Demonstrated
- Data transformation
- Exploratory data analysis
- Visualization
SQL Server • T‑SQL
https://img.shields.io/badge/Data-Criminal%20Justice-lightgrey?style=for-the-badge
Goal: Use SQL to analyze demographic and historical patterns among Texas death row inmates.
🔍 Key Questions Answered
- Inmates by race
- Education levels
- Age statistics
- Time spent on death row
- Last statements
- Weight, birth year, execution year trends
🧠 Skills Demonstrated
- SQL data cleaning
- Data manipulation
- Aggregations & percentages
- Exploratory analysis
Python • Pandas • Seaborn
https://img.shields.io/badge/Data-Entertainment%20Analytics-purple?style=for-the-badge
Goal: Identify top games, platforms, and regional sales patterns.
🔍 Key Questions Answered
- Top 10 games
- Top platforms
- Regional sales leaders
- Global sales percentages
- Year‑over‑year platform performance
Python • Pandas • Matplotlib
https://img.shields.io/badge/Data-Education%20Analytics-green?style=for-the-badge
Goal: Explore relationships between demographics, parental education, test prep, and student scores.
🔍 Key Questions Answered
- Students by gender & race
- Parental education levels
- Lunch type & test prep effects
- Math/Reading/Writing score distributions
Python • Pandas • Seaborn
https://img.shields.io/badge/Data-Healthcare%20Analytics-teal?style=for-the-badge
Goal: Analyze fictional patient data to uncover trends in conditions, billing, insurance, and demographics.
🔍 Key Questions Answered
- Average age by gender
- Blood types
- Medical conditions
- Insurance provider trends
- Billing statistics
- Admission patterns
- Python
- SQL (T‑SQL, MSSQL Server)
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
- SQL Server Management Studio
- Git & GitHub
If you’d like to connect, collaborate, or discuss data analytics:
📧 Email: melvinjasonwallace@gmail.com
📍 Location: Naperville, IL
💼 LinkedIn: (https://www.linkedin.com/in/melvin-j-wallace-546bb6297)
🌐 Portfolio Website: (https://melvinjwallace.github.io/MelvinJW.github.io/)