Machine Learning Effects on Consumer Decision-Making and Purchase Behavior in the Fashion Industry of India

In this study, we investigate how Machine Learning (ML) is changing the way Indian consumers shop for and make fashion-related decisions. This research uses a systematic approach that includes data collecting, feature engineering, ML modelling, and statistical analysis to reveal the critical function of ML-powered recommendation systems in influencing consumer decisions and behavior. While the logistic regression study revealed the outsized impact of demographic variables and customer interaction with ML-generated suggestions, the collaborative filtering recommendation model showed impressive performance. The necessity for openness and equity in ML algorithms is emphasized by ethical and legal concerns. The results provide light on the future of fashion retail in India, which will be enhanced by technology but will also adhere to ethical standards, and give practical insights for fashion entrepreneurs, governments, and academics. Research in the future should focus on comprehending this ever-changing environment by probing its intersections with new technologies, sustainability, and other fields.