PREDICTING AND IMPROVING CUSTOMER SATISFACTION ON E-COMMERCE PLATFORMS USING MACHINE LEARNING
Abstract
This study explores the application of machine learning to predict and improve customer satisfaction on e-commerce platforms. Utilizing a dataset of customer reviews and ratings, the research employs Natural Language Processing (NLP) and supervised learning algorithms—specifically Logistic Regression and Decision Tree. The results demonstrate that these models can effectively categorize customer sentiment and satisfaction levels. By integrating these predictive models into a Streamlit web application, businesses can gain real-time insights into consumer feedback, allowing for proactive improvements in service delivery and product quality.
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