Assessing online products using NLTK based machine learning model

The paper analyses the experiences and expectations of the consumer about the product during online purchases, the supply and demand of the online market, and the promises made by the company. Differentiate e-commerce products based on similar features, price, or quality. The tools used on the dataset are Pandas, NumPy, NLTK, and SkLearn. A better product is distinguished based on the ratio of positive to negative edges. Customer satisfaction, sales volume, and rankings were also determined. These factors were the main reason for customer satisfaction. The research looks at customer expectations and experiences with online buying, including product preferences, usability, and product promises. The competitive market environment, which makes it challenging to identify desired items from identical ones in terms of features, price, or quality, is said to be the reason why buyers are cautious about various products.