This repository showcases a collection of Exploratory Data Analysis (EDA) projects developed using Python and modern data analytics tools.
The projects focus on data cleaning, preprocessing, statistical analysis, visualization, and dashboard development using real-world datasets from healthcare, public health, and global data sources.
Each project demonstrates the complete EDA workflow, from raw data preparation to insight generation and visual storytelling.
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
- Statistical Analysis
- Data Visualization
- Dashboard Development
- Insight Generation
- Trend and Pattern Identification
- Python
- Pandas
- NumPy
- Plotly
- Matplotlib
- Seaborn
- Jupyter Notebook
- Dash
data/
- raw/
- processed/
notebooks/
dashboards/
results/
- figures/
docs/
src/
This project analyzes obesity, nutrition, and physical activity trends across U.S. states using public health datasets.
The analysis focuses on identifying behavioral risk factors, obesity patterns, physical activity levels, and state-level health differences through exploratory data analysis, statistical techniques, and data visualization.
Key topics include:
- Obesity trend analysis
- Physical activity patterns
- State-level comparisons
- Distribution analysis
- Correlation analysis
- Public health insights
- Statistical visualizations
This project analyzes global COVID-19 confirmed cases using time-series data, country-level aggregation, and geospatial visualization techniques.
The analysis focuses on global trends, case distribution, and interactive mapping of pandemic data.
Key topics include:
- Data cleaning and reshaping
- Country-level aggregation
- Time-series analysis
- Global case distribution
- Geospatial visualization
- Interactive mapping
- Pandemic trend analysis
This project analyzes food inspection records from the City of Chicago using public health and geospatial datasets.
The analysis focuses on restaurant inspection outcomes, compliance patterns, risk classifications, and geographic distribution across Chicago neighborhoods through exploratory data analysis, interactive mapping, and spatial visualization techniques.
Key topics include:
- Food inspection analysis
- Restaurant compliance patterns
- Risk level assessment
- Geographic distribution analysis
- Interactive Folium maps
- Restaurant cluster visualization
- ZIP code density analysis
- Public health insights
Mario Jakupas
MS Computer Science – Data Analysis
Montclair State University
This project explores obesity, nutrition, and physical activity indicators across the United States using public health datasets.
The goal is to identify trends, behavioral patterns, and state-level differences through statistical analysis and data visualization techniques.
This project analyzes global COVID-19 confirmed cases using time-series data, country-level aggregation, geospatial visualization, and interactive mapping techniques.
- Data Cleaning and Reshaping
- Country-Level Aggregation
- Global Case Distribution
- Interactive Geospatial Mapping
- Time-Series Visualization
This project analyzes food inspection records from the City of Chicago to identify restaurant compliance patterns, inspection outcomes, and geographic distributions using interactive mapping techniques.
The project combines public health inspection data with geospatial visualization to explore restaurant inspection trends across Chicago neighborhoods.
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Data Cleaning and Preparation
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Restaurant Inspection Analysis
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Risk Level Assessment
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Geographic Distribution Analysis
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Interactive Mapping with Folium
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ZIP Code Density Analysis
