Exploratory data analysis and visualization across business performance, public health, and environmental datasets. Each project follows a consistent analytical workflow: data acquisition and cleaning, exploratory analysis, visualization, and interpretation of findings.
| Project | Domain | Description | Tools |
|---|---|---|---|
| Marketing & Product Performance | Business | End-to-end business analysis - ROI by channel, product segmentation, correlation heatmaps, and spend optimization recommendations | Pandas · Seaborn · Matplotlib |
| Child Mortality Trends 1900–2018 | Public Health | 119-year longitudinal analysis across four pediatric age groups, isolating the 1918 influenza anomaly against the long structural decline | Seaborn · Matplotlib |
| Car Dealership Inventory Analysis | Automotive | Pricing distributions, body type and engine sizing trends, and curb weight analysis across dealership inventory | Pandas · Matplotlib |
| Forest Fires by State 1992–2015 | Environmental | Decade-averaged fire counts, acres burned, and burn duration by state, structured for long-range disaster planning insight | Pandas |
Data-Visualization/
├── Leah_Nicholson_Business_Case_Study.ipynb # Marketing analysis
├── MortalityAnalysis/ # Child mortality project
├── CarInventory_DataAnalysis/ # Dealership inventory project
└── ForestFires_DataPreparation/ # Wildfire trends project