Nowadays, Machine Learning (ML) plays the important role in the E-commerce industry and its customer relations to perform different kinds of tasks such as prediction of purchases, segmentation of customers according to their reviews/sentiments, recommendation of products to the active users etc... various ML algorithms are implemented to get trained with data patterns to perform the above-mentioned tasks. In this paper, the customer segmentation and recommendation of women’s clothing based on the reviews are presented. The comparative study is done using five different ML algorithms namely Regression analysis, Naïve Bayes, Decision Trees, Support Vector Machines, and Clustering analysis. The results show that the Naïve Bayes algorithm has better performance when compared to other algorithms by showing better accuracy.