(untitled) (Global Journal of Computer Science and Technology, 2024)
This research delves into the multifaceted implications of customer feedback within the e-commerce landscape focusing on product reviews on Amazon The study meticulously examines over 1 400 unique product reviews to decipher patterns extrapolate trends and offer actionable recommendations for the evolv- ing e-commerce paradigm The dataset comprises 16 distinct features including product ratings textual re- views prices and discounts Preliminary data explo- ration reveals a prevalence of high ratings indicative of an overarching positive sentiment among Amazon s clientele Furthermore features related to pricing and discounts hint at the intricate interplay between economic factors and customer feedback Through data prepa- ration techniques including numeric extraction and missing data handling the research ensures the dataset s readiness for advanced statistical and machine learning analyses Leveraging the CRISP-DM methodology the study uncovers insights into customer satisfaction the impact of pricing strategies and the significance of in- depth reviews These findings provide actionable insights for e-commerce platforms and vendors underscoring the importance of understanding customer sentiments for informed decision-making and cultivating positive customer relationships