The automotive industry is undergoing a critical transition from Internal Combustion Engine vehicles to Electric Vehicles (EVs), yet marketing strategies continue to rely on broad demographic profiles that fail to identify the pragmatic "Early Majority" consumers essential for mass adoption. This study addresses this gap by developing a predictive machine learning model that integrates demographic, behavioural, and psychographic data to identify potential EV buyers. Using survey data from 1,050 respondents across India, the research employed three classification models—Logistic Regression, Decision Tree, and Random Forest—with the Random Forest classifier emerging as the best performing model, achieving a 10-fold cross-validation accuracy of 79.7% and an AUC-ROC of 0.886. Feature importance analysis revealed that Climate Concern (0.142), EV Perception (0.116), Age (0.102), Tech Comfort Level (0.100), and Income (0.090) are the strongest predictors of EV purchase intention, demonstrating that attitudinal and psychological factors outweigh traditional demographic characteristics. Notably, the analysis identified a critical distinction in barriers between buyer segments: Cost dominates as the primary barrier for unlikely buyers (45.5%), while likely buyers show balanced concerns across charging infrastructure (29.0%), range anxiety (28.4%), and cost (27.5%). The dataset revealed that 45.0% of respondents are likely buyers, 44.4% are neutral, and only 10.7% are unlikely buyers, indicating substantial market opportunity. The study concludes that cost reduction strategies, targeted marketing to the 26-35 age group, emphasis on range and charging speed in product development, and validation of the predictive model on holdout data before deployment represent the most effective pathways for accelerating EV adoption. This research contributes a data-driven framework that enables precise customer targeting, reduces marketing inefficiencies, and provides actionable insights for automakers, policymakers, and marketers navigating the transition to sustainable transportation.

Keywords: Electric Vehicle adoption, predictive modelling, Random Forest, consumer behaviour, Early Majority, purchase intention, machine learning, India