Machine Learning for the Real World: Applications and Insights

In India, the increasing adoption of electric vehicles (EVs) due to environmental, energy security, and government policy factors has raised numerous research possibilities in terms of sales trajectories, infrastructure development, consumer acceptance, and sales forecasting. However, much of the existing literature remains siloed: independent studies approach the subject singularly (e.g., some examine only EV adoption barriers while others examine forecasting). This review looks to synthesise recent research in which machine learning models, time-series forecasting, new federated learning applications, and policy frameworks converge. Research gaps will be polled or inferred through an analysis of the current literature, such as limited visualisation tools developed by authors with a history in industry, a lack of comparative analysis of state-based EV infrastructure planning, and weak consumer-facing decision support. We summarise the directions offered by an example, EVision India, a smart dashboard that combines sales analytics, infrastructure layer mapping, forecasting models, and a recommendation engine to provide key stakeholders, including policymakers, manufacturers, and consumers, a roadmap to navigate EV purchases, inform government policies, and maximise EV supply chain efficiencies. The paper/authority ends with suggestions for future areas of EV research, including IoT-based, real-time data, explainable artificial intelligence (AI) to improve consumer trust, and platform scalability for use in EVs in global markets.