An end-to-end Electric Vehicle Market Analytics project built using Python, Pandas, NumPy, SQLite, SQL, Jupyter Notebook, and Power BI.
The project analyzes electric vehicle registration data from the Washington State Department of Licensing to understand EV adoption, manufacturer performance, model popularity, vehicle technology, electric range, geographic distribution, and market concentration.
The objective of this project is to analyze electric vehicle registration activity and identify meaningful patterns related to:
- EV adoption over time
- Manufacturer market leadership
- EV model popularity
- BEV vs PHEV adoption
- Electric vehicle range
- Manufacturer growth
- Market concentration
- New vs used EVs
- County-level EV adoption
- City-level EV activity
- ZIP-code level EV concentration
- Electric utility distribution
- Legislative district activity
- Primary vehicle use
Official EV Data
↓
Python Data Preparation
↓
SQLite Database
↓
SQL Analysis
↓
Power BI
↓
Interactive Dashboard
↓
EV Market Insights
The dataset was obtained from the official Washington State Open Data portal.
Electric Vehicle Title and Registration Activity
Publisher: Washington State Department of Licensing (DOL)
Washington State Electric Vehicle Title and Registration Activity
Note: The original dataset is large and is not included directly in this repository. Please download the latest dataset from the official Washington State Open Data portal using the link above.
- Vehicle Type
- DOL Vehicle ID
- Model Year
- Make
- Model
- Primary Use
- Electric Range
- Odometer Reading
- New or Used Vehicle
- Sale Price
- Sale Date
- Transaction Type
- Transaction Date
- County
- City
- State
- Postal Code
- CAFV Eligibility
- Electric Utility
- Legislative District
- Electrification Fee Information
EV-Market-Analytics/
│
├── IMAGES/
│ ├── Electric Vehicle Market Analytics Dashboard.png
│ ├── EV Trend & Technology Insights.png
│ └── Geographic Insights & Regional EV Adoption.png
│
├── POWER BI/
│ └── EV Market Analytics Dashboard.pbix
│
├── QUERIES/
│ └── EV_Analysis.ipynb
│
└── README.md
| Folder / File | Purpose |
|---|---|
IMAGES/ |
Power BI dashboard screenshots |
POWER BI/ |
Final Power BI dashboard file |
QUERIES/EV_Analysis.ipynb |
Python data preparation and SQL analysis notebook |
README.md |
Project documentation |
The original dataset contained:
Rows : 1,809,203
Columns : 33
After removing exact duplicate records:
Original Records : 1,809,203
Duplicate Rows : 63
Final Records : 1,809,140
The cleaned dataset was then loaded into SQLite for SQL analysis.
registrations
Python was used to inspect, clean, standardize, validate, and prepare the raw EV registration data before SQL analysis.
The complete workflow is available in:
QUERIES/EV_Analysis.ipynb
- Python
- Pandas
- NumPy
- SQLite3
- Matplotlib
- Seaborn
- Jupyter Notebook
The official EV dataset was loaded into Pandas and inspected to understand:
- Dataset structure
- Data types
- Missing values
- Unique categories
- Numerical fields
- Categorical fields
- Geographic fields
- Vehicle characteristics
Missing values were identified across the dataset.
Important fields requiring attention included:
- Electric Range
- County
- State
- Postal Code
- Legislative District
- Sale Date
- Fee-related fields
Missing values were handled according to the analytical purpose of each field rather than applying the same treatment to every column.
Examples of preparation included:
- Filling missing electric-range values using the median
- Handling missing county values
- Handling missing state values
- Handling missing postal-code values
This helped ensure important fields used in aggregation and visualization could be analyzed consistently.
Text fields were standardized to improve consistency during grouping and aggregation.
Important fields included:
- Make
- Model
- County
- State
For example:
TESLA
Tesla
tesla
could otherwise be treated as separate categories.
Standardization ensured consistent grouping during SQL analysis.
The dataset was checked for duplicate records.
Duplicate Rows : 63
The exact duplicate rows were removed.
1,809,140 records
Original column names containing spaces and special characters were converted into SQL/Python-friendly names.
For example:
Electric Range
↓
Electric_Range
Model Year
↓
Model_Year
New or Used Vehicle
↓
New_or_Used_Vehicle
This made the dataset easier to use with Python, SQLite, and SQL.
Important analytical fields were checked before loading the cleaned data into the database.
Validation included:
- Negative electric-range values
- Model-year validity
- Missing values
- Duplicate records
- Numerical distributions
Negative Electric Range Values : 0
Unrealistic Model Year Values : 0
After the data preparation stage, the cleaned Pandas DataFrame was loaded into a SQLite database for analytical SQL queries.
Note: The SQLite database is not included in this repository because of its large file size. It is generated locally from the cleaned dataset when the analysis notebook is executed.
registrations
### Main Table
```text
registrations
The final cleaned dataset contained:
1,809,140 records
SQLite was used as the database layer for analytical SQL queries.
The SQL analysis was performed inside:
QUERIES/EV_Analysis.ipynb
The analysis was designed around practical EV market questions.
The SQL analysis answers business questions related to:
- Total EV registrations
- Top EV manufacturers
- EV adoption by model year
- BEV vs PHEV distribution
- Top counties and geographic concentration
- Average electric range by manufacturer
- New vs used EV distribution
- Top EV models
- Manufacturer growth
- Average range by vehicle technology
- Top-3 manufacturer market share
📌 Complete SQL/Python analysis:
QUERIES/EV_Analysis.ipynb
How many cleaned EV registration records are available?
1,809,140 registrations
This represents the final analytical population used in the project.
Which manufacturers have the highest EV registration volume?
| Rank | Manufacturer | Registrations |
|---|---|---|
| 1 | Tesla | 721,710 |
| 2 | Nissan | 205,116 |
| 3 | Chevrolet | 154,036 |
| 4 | Ford | 102,216 |
| 5 | BMW | 79,422 |
Tesla has the highest registration volume in the analyzed dataset.
How has EV registration activity changed across model years?
The analysis focused on model years from 2018 to 2026.
| Model Year | Registrations |
|---|---|
| 2018 | 150,430 |
| 2019 | 103,402 |
| 2020 | 102,219 |
| 2021 | 150,233 |
| 2022 | 183,942 |
| 2023 | 302,893 |
| 2024 | 195,253 |
| 2025 | 107,860 |
| 2026 | 64,277 |
2023 → 302,893 registrations
What is the distribution between BEVs, PHEVs, and other EV technologies?
| Vehicle Type | Registrations |
|---|---|
| Battery Electric Vehicle (BEV) | 1,394,647 |
| Plug-in Hybrid Electric Vehicle (PHEV) | 414,448 |
| Hydrogen Powered Vehicle | 45 |
BEVs represent the majority of the analyzed EV registration population.
Which counties have the highest EV registration activity?
| Rank | County | Registrations |
|---|---|---|
| 1 | King | 927,297 |
| 2 | Snohomish | 214,535 |
| 3 | Pierce | 146,370 |
| 4 | Clark | 106,186 |
| 5 | Kitsap | 61,875 |
King County has the highest registration activity in the analyzed data.
Manufacturers were compared using average electric range with a minimum registration threshold to avoid very small groups dominating the comparison.
| Manufacturer | Average Range |
|---|---|
| Jaguar | 205.09 |
| Tesla | 90.18 |
| Chevrolet | 86.95 |
| Fiat | 82.50 |
| Nissan | 80.51 |
| Smart | 62.41 |
| Porsche | 47.48 |
| Audi | 46.56 |
| Kia | 44.51 |
| Land Rover | 42.04 |
Jaguar has the highest average electric range in this filtered manufacturer comparison, while Tesla has the largest registration volume.
What is the distribution between new and used EV registrations?
| Vehicle Status | Registrations |
|---|---|
| Used | 1,510,017 |
| New | 299,123 |
Used EV registrations represent a substantially larger portion of the analyzed dataset.
Which EV models have the highest registration volume?
| Rank | Model | Registrations |
|---|---|---|
| 1 | Model Y | 297,368 |
| 2 | Model 3 | 268,871 |
| 3 | Leaf | 195,252 |
| 4 | Model S | 93,815 |
| 5 | Volt | 68,570 |
| 6 | Model X | 54,096 |
| 7 | Bolt EV | 53,998 |
| 8 | Prius Prime (PHEV) | 29,633 |
| 9 | Mustang Mach-E | 29,450 |
| 10 | ID.4 | 29,391 |
Model Y is the highest-volume EV model in the analyzed dataset.
Which manufacturers changed the fastest between 2023 and 2024?
A SQL CTE was used to compare manufacturer registration activity between model years 2023 and 2024.
The analysis calculated percentage growth using registration counts for both years.
This provides a view of manufacturer momentum, rather than simply ranking manufacturers by total registrations.
How does average electric range differ between EV technologies?
| Vehicle Type | Average Range |
|---|---|
| Battery Electric Vehicle (BEV) | 72.84 |
| Plug-in Hybrid Electric Vehicle (PHEV) | 31.57 |
| Hydrogen Powered Vehicle | 0.00 |
BEVs have a substantially higher average recorded electric range than PHEVs in this dataset.
How concentrated is the EV market among the leading manufacturers?
| Manufacturer | Registrations | Market Share |
|---|---|---|
| Tesla | 721,710 | 39.89% |
| Nissan | 205,116 | 11.34% |
| Chevrolet | 154,036 | 8.51% |
59.74%
The top three manufacturers account for 59.74% of the analyzed registration population, indicating significant market concentration.
The cleaned data and analytical results were used to build a three-page Power BI dashboard.
The dashboard was designed as an analytical story:
Page 1
What does the EV market look like?
↓
Page 2
Where is EV adoption concentrated?
↓
Page 3
How is the EV market and technology evolving?
The first page provides a high-level overview of the EV market.
- Total Registrations
- Top Manufacturer
- Top Manufacturer Share
- YoY Growth
- Average Electric Range
- EV Adoption Trend
- Vehicle Type Mix
- Top EV Manufacturers
- EV Models by Range
- New vs Used EV Distribution
This page provides a quick understanding of the overall EV market before moving into detailed geographic and technology analysis.
The second page focuses on where EV registrations are concentrated.
- County-Level EV Adoption
- Top Counties by EV Registrations
- Average Electric Range by Manufacturer
- Electric Utility Distribution
- Top Cities by EV Registrations
- Top ZIP Codes by EV Registrations
- Which counties have the highest EV registrations?
- Which cities have the highest EV activity?
- Which ZIP codes show higher EV concentration?
- How does EV adoption vary geographically?
- Which electric utility areas have higher EV activity?
The third page focuses on EV technology and market evolution.
- BEV vs PHEV Adoption Over Time
- EV Market Leadership Over Time
- Monthly EV Registration Trend
- Fastest-Growing EV Models
- Primary Use Distribution
- How are BEVs and PHEVs changing over time?
- How has manufacturer leadership changed?
- Which EV models show growth?
- What are the major vehicle-use categories?
- How has EV activity changed over time?
The Power BI report includes synchronized slicers for:
- Model Year
- Vehicle Type
- County
The slicers are synchronized across the three dashboard pages so users can maintain the same filtering context while moving through the report.
Tesla recorded:
721,710 registrations
making it the leading manufacturer in the analyzed dataset.
The top three manufacturers account for:
59.74%
of the analyzed registration population.
BEVs account for:
1,394,647 registrations
compared with:
414,448 PHEV registrations
King County recorded:
927,297 registrations
making it the largest county in the analyzed dataset.
Used : 1,510,017
New : 299,123
Model Y : 297,368
Model 3 : 268,871
BEV : 72.84
PHEV : 31.57
Among the analyzed model years from 2018 to 2026:
2023 → 302,893 registrations
was the highest.
- Python 3
- Pandas
- NumPy
- Matplotlib
- Seaborn
- SQLite
- SQL
- Jupyter Notebook
- Power BI
- Power Query
- GitHub
- Data loading
- Data cleaning
- Data transformation
- Missing-value handling
- Text standardization
- Duplicate removal
- Data validation
- Pandas
- SQLite integration
SELECTWHEREGROUP BYORDER BYCOUNTAVGROUNDCASEHAVING- CTEs
- Ranking
- Percentage calculations
- Growth analysis
- Market-share analysis
- Dashboard development
- KPI cards
- Trend analysis
- Market analysis
- Geographic analysis
- Bar charts
- Column charts
- Line charts
- Area charts
- Ribbon charts
- Donut charts
- Maps
- Slicers
- Slicer synchronization
- Interactive filtering
- Data storytelling
Washington State Open Data
↓
Python
↓
Data Cleaning & Preparation
↓
Cleaned Dataset
↓
SQLite Database
↓
SQL Analysis
↓
Analytical Results
↓
Power BI
↓
3-Page Dashboard
↓
EV Insights
git clone https://github.com/Shrey2503/EV-Market-Analytics.git
cd EV-Market-Analyticspip install pandas numpy matplotlib seaborn jupyterNavigate to:
QUERIES/EV_Analysis.ipynb
jupyter notebookRun the notebook cells sequentially to:
- Load the dataset
- Inspect the data
- Analyze missing values
- Clean the data
- Standardize fields
- Remove duplicates
- Validate important fields
- Create the SQLite database
- Run SQL analysis
Open the .pbix file from:
POWER BI/
Then explore all three dashboard pages and use the synchronized slicers.
This project demonstrates practical experience in:
- Working with real-world public datasets
- Python-based data preparation
- Pandas data manipulation
- Data cleaning
- Missing-value handling
- Duplicate detection
- Data validation
- SQLite database management
- SQL analytical querying
- Market analysis
- Geographic analysis
- EV technology analysis
- Trend analysis
- Power BI dashboard development
- Interactive reporting
- Business insight generation
- Data storytelling
Potential extensions include:
- EV charging-station analysis
- EV-to-charging-station ratio
- Charging infrastructure gap analysis
- EV adoption forecasting
- Advanced DAX measures
- Power BI drill-through pages
- Power BI tooltip pages
- Power BI Service deployment
- Automated data refresh
- Integration with population data
- Integration with charging infrastructure data
- Regional EV adoption forecasting
Shreyash Vats
B.Tech CSE | Aspiring Data Analyst
Python SQL Pandas SQLite Power BI Data Analytics Data Visualization
Electric Vehicle Market Analytics
Python • SQL • SQLite • Power BI • Data Analytics • Business Intelligence • Data Visualization