EV Exploratory Data Analysis (EDA)

This is my executive report and presentation for my Computing & Modeling course (INDE 6605-01) at my university. I analyzed a dataset of over 232,000 electric vehicle records from Washington state to understand EV adoption patterns. I used Python (pandas, numpy, matplotlib) to clean and preprocess the data. I handled missing values, structured the dataset, and ran statistical analysis. Then I used Tableau to create visualizations showing things like which states have the most EVs, which brands dominate the market (Tesla is huge at 48%), and how EV adoption has grown rapidly from 2020 to 2025. Key findings from my analysis: Battery Electric Vehicles (BEVs) make up 79% of the market compared to Plug-in Hybrids. Tesla alone holds almost half the market. The state of Washington has the most registered EVs in my dataset. Most vehicles produced are from recent years (2020-2025) showing how new this market really is. This work shows my skills in data preprocessing, statistical modeling, and data visualization. It is relevant to industrial engineering because it applies data analysis methods to understand real market trends and consumer behavior. Published on Zenodo with DOI 10.5281/zenodo.19589458.