Prediction of customer recommendations on e-commerce using machine learning algorithms

Companies are embracing e-commerce, or electronic commerce, as a common way to reach clients and grow their market share. Several benefits, such as lower administrative expenses, broader coverage, and 24/7 availability, are offered by it in comparison to conventional methods. Regardless of their location or time of day, customers may place orders and have them delivered straight to their door. Buying and selling goods and services using the Internet is called online commerce or e-commerce. There are many different kinds of internet transactions that take place, including those between individuals, between companies, and between customers and businesses. In order to personalize recommendations based on customers' previous purchases and browsing habits, e-commerce companies use machine learning algorithms to examine customer data. In command to predict the recommendation factor, the research makes use of a dataset including women's clothing. Using various machine learning techniques like Support Vector Machine, Logistic Regression, Random Forest, and Naïve Bayes allows for this to be achieved. The analysis makes use of a number of indicators, including recall, accuracy, precision, and f1-sum.