The rapid growth of e-commerce and consumers familiarity with multichannel buying have made omnichannel business a popular topic. Numerous commercial entities have begun to focus on omnichannel business issues in an effort to meet the emerging trend of consumer demand and often focus their energies on both offline and internet company. Therefore, it is undeniable that an awareness of online shoppers’ purchasing habits is essential for omnichannel businesses. Customer satisfaction and product diversification become critical. Because there is so much data, it can be difficult to match the proper clients with appropriate items. Our objective is to ensure business success while improving customer happiness by pairing them with the right items. SVM, logistic regression, K-means, decision trees, random forests, and gradient boosting are the seven methods that we have used. We evaluate each method to see if it is appropriate for our goal. The objective of this study is to provide a streamlined approach for customized product recommendations, which will improve the entire shopping experience.