Smartphones play a dynamic role in our daily lives, enriching with the tools of Knowledge Data Discovery (KDD) and data mining as a mobile device. This assists in acquiring and deriving better contextual data about users who are utilizing their mobile phones. Data mining is used to search and analyze the data to recognize the potential information. In contrast, data mining has several computational patterns, models, and algorithms to analyze the customer feedback of the smartphone and its features. The Apriori algorithm is one of the standard algorithms for Association Rule Mining (ARM) that can be used to mine frequent item sets and their associate rules.To classify sentiments into positive and negative polarity, the supervised ML is applied. By training data sets to train a model, and then testing data sets to validate the model works as expected. The fields of ML have several challenges namely text mining, associative mining,image processing or classification problems, and, sentiment classification which endure to resolve in this area. Significant experiments utilizing the methods on PAUL, PID, and GitHub datasets demonstrate that the methods significantly evaluated utilizing the Product Identification data set are involved for the analysis of the precision, recall, f-measure as well as accuracy metrics. This research focuses on improving the efficiency of the various mobile brand patterns using the customer Review process method to discover a suitable mobile review analysis.