Exploring Electric Vehicle Price Prediction and Characteristics

Predicting energy consumption accurately is crucial for maximizing efficiency, the introduction of electric Vehicle (EVs) has presented new issues and chances for car pricing. The exact issue discussed in this research is identifying key technical factors and market scenario that impacts the pricing of electric vehicles. This work use sophisticated machine learning algorithms (ML) to assess several elements, including battery capacity, range, charging time, brand reputation, and energy efficiency, thereby helping to discover the major forces behind the pricing of electric vehicles. By means of an analysis of the relationships between technical specifications and market dynamics, the study aims to provide important insights to consumers, governments, and manufacturers. These disclosures can help businesses maximize their production and marketing strategies, help governments create data-driven regulations for sustainable transportation, and let consumers make ecologically responsible and financial wise purchases. By examining the factors affecting the price of EVs helps the study contribute to the continuous debate on sustainable transportation. It highlights the need of considering the economic feasibility and commercial opportunities of EVs. The findings not only provide an explanation for existing patterns in the EVs market, but also offer a prediction framework for future patterns. This framework assists the automotive sector in transitioning to more environmentally friendly options. This study helps us better understand electric car price dynamics overall, therefore supporting a more sustainable and financially realistic future for transportation. Incorporating ML, the study shows the promise of data-driven methods in solving present challenges in the automotive sector. The work identifies cost, battery capacity, and range as the key drivers of EV pricing and develops a high-accuracy predictive framework for market segmentation and emerging patterns.