| Abstract |
The globe is currently extremely concerned about the impending threat of climate
change, which is being exacerbated by rising global temperatures. Regrettably, India’s greenhouse
gas emissions are rising substantially, placing it in the top ten global emitters. Air pollution plays
a crucial part in this environmental issue, acting as a significant accelerator for the greenhouse
effect. Specifically, 10% of India’s air pollution is caused by the transportation industry. Recognising
the gravity of the problem, the Indian government has moved decisively to reduce air pollution,
putting particular focus on promoting the use of electric vehicles. Nevertheless, customer attitudes,
views, and knowledge about electric vehicles determine how successful these programmes are. In
a diverse market, finding the proper customers is a challenging issue for marketers in the electric
vehicle space. Recent research endeavours to develop a machine learning model that can forecast
sales of electric vehicles (EVs) in India, taking into account the country’s dynamic topography. In
order to construct the model, OLA conducted a thorough investigation of the EV landscape, which
included a textual analysis of social media comments pertaining to EVs. The primary objective is to
identify frequently used terms that reveal important details about consumer concerns and interests
for OLA electric cars. In this research with the help of web scraping and natural language processing
we found the sentiments of EV users. Also linear regression was used for determining the EV future
demands. |
