Factors Affecting Customer Decision Making for Electric Vehicles in India Using Machine Learning

Abstract

These days, everyone is worried about the weather changing because of global warming. India now ranks among the world's top 10 polluters due to its steadily rising rate of greenhouse gas emissions. One of the major causes of the greenhouse effect is air pollution. Ten percent of India's air pollution is caused by vehicles. In an effort to lessen the country's air pollution, the government of India is promoting the use of electric cars. However, how people feel, think, and comprehend electric vehicles (EVs) will determine how well they do. The goal of this case study was to get a sense of how electric car buyers in India feel. The primary goal of this study was to utilize Deep Learning techniques, such as the Doc2Vec Algorithm, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), to extract opinions that would be useful for marketers, manufacturers, and prospective buyers. We choose to use a big data platform to examine EV sentiment since that's how social media data is naturally structured. The better text mining capabilities of Deep Learning based approaches made them the favored choice over more conventional machine learning algorithms like Support Vector Machine, Logistic regression, Decision trees, etc.