analyzing customer decision-making in indian e-commerce: a study using decision tree and k-nearest neighbor

The advent of technology has brought about a major shift in the way businesses operate. electronic commerce has revolutionized the way consumers purchase goods and services. with the increasing popularity of online shopping in india, it is essential to understand the factors that influence customer decision-making processes. this thesis aims to study e-commerce and examine the decision-making processes of customers in india. the study is based on data collected through a questionnaire designed under the title customer survey of e-commerce sales in india, which was published by kaggle. the survey aims to evaluate the various factors that influence e-commerce in india. customers responded to multiple-choice questions about various factors related to e-commerce and evaluated different criteria. to analyze the collected data, data visualization techniques using tableau and google data studio were employed. additionally, machine learning techniques, including decision trees and k-nearest neighbors, were applied. the findings of this research will help e-commerce businesses in india to better understand the decision-making processes of their customers. the study s results can also help businesses to optimize their marketing strategies and improve their services based on the needs and preferences of their customers. overall, this paper will contribute to the existing body of knowledge on e-commerce and customer decision-making processes in india.