Exploratory Data Analysis (EDA) on Washington State Electric Vehicle registration data covering 112,634 EV records across 17 features, using Python, Seaborn, Matplotlib, and Plotly.
- Source: Kaggle - Electric Vehicle Population Data
- Records: 112,634 rows × 17 columns
- Coverage: Washington State EV registrations (BEV & PHEV)
| Feature | Description |
|---|---|
| Make / Model | Vehicle manufacturer and model |
| Model Year | Year of manufacture (1997–2023) |
| Electric Vehicle Type | BEV (Battery) vs PHEV (Plug-in Hybrid) |
| Electric Range | Range in miles on electric charge |
| Base MSRP | Manufacturer suggested retail price |
| State / County / City | Geographic location of registration |
- Distribution of Model Year, Electric Range, Base MSRP
- EV Type vs Electric Range (boxplot)
- Top 10 Makes vs Base MSRP
- EV Type vs Base MSRP (barplot)
- Choropleth map — number of EVs by US state
- Scatter plot — Electric Range vs Base MSRP coloured by EV type
- Box plot — Base MSRP by EV type
- Pie chart — Distribution of BEV vs PHEV
- Animated bar chart showing EV Make count growth year by year (2000–2023)
- Tesla MODEL3 is the most registered EV with 23,135 units, followed by MODEL Y (17,142) and Nissan LEAF (12,880)
- Battery Electric Vehicles (BEV) have significantly higher electric range than PHEVs
- EV registrations grew exponentially post-2015, with the sharpest spike in 2022
- Washington State dominates registrations; Tesla commands the highest Base MSRP among top manufacturers
- After cleaning, all 17 columns had zero missing values
- Python 3.x
- Pandas
- Matplotlib
- Seaborn
- Plotly Express
pip install pandas matplotlib seaborn plotly
python ev_analysis.pyPlace dataset.csv in the same folder before running. Dataset available on Kaggle.