Dataset Summary
This dataset contains electric vehicle registration records for washington States. It includes vehicle type, make, model, base MSRP, electric range, and location data (county, city, zip). This project explores patterns in EV adoption and pricing across different regions and demographics.
Project Goal
The primary goal of this project is to analyze electric vehicle trends and behaviors based on publicly available registration data. This includes identifying vehicle types by region, analyzing price vs. range, clustering EVs based on features, and predicting EV types using supervised learning. The following questions were explored:
EV Type by State
Price vs. Range Relationship
Top Utility Companies
Growth Trends by Year
County Correlation with Demographics
These insights can help inform government incentives, charging infrastructure planning, and consumer behavior analysis.
Machine Learning Summary
This project used both supervised (classification & regression) and unsupervised (clustering) learning methods.
Classification: Predicting EV type using DecisionTreeClassifier
Regression: Predicting MSRP based on range
Clustering: Grouping vehicles using KMeans. GridSearchCV was used for hyperparameter tuning. Evaluation metrics included accuracy, confusion matrix, R² score, and visual comparison.