Launching new products in the consumer electronics market is challenging. Developing and marketing the same in limited time affect the sustainability of such companies. This research work introduces a model that can predict the success of a product. A Feature Information Gain (FIG) measure is used for significant feature identification and Distributed Memory-based Resilient Dataset Filter (DMRDF) is used to eliminate duplicate reviews, which in turn improves the reliability of the product reviews. The pre-processed dataset is used for prediction of product pre-launch in the market using classifiers such as Logistic regression and Support vector machine. DMRDF method is fault-tolerant because of its resilience property and also reduces the dataset redundancy; hence, it increases the prediction accuracy of the model. The proposed model works in a distributed environment to handle a massive volume of the dataset and therefore, it is scalable. The output of this feature modelling and prediction allows the manufacturer to optimize the design of his new product.

在消费电子市场推出新产品具有挑战性。在有限的时间内开发和销售此类产品会影响此类公司的可持续性。这项研究工作引入了可以预测产品成功的模型。特征信息增益（FIG）度量用于显着特征识别，而基于分布式内存的弹性数据集筛选器（DMRDF）用于消除重复的评论，从而提高了产品评论的可靠性。使用分类器（例如Logistic回归和Support Vector Machine）将预处理后的数据集用于预测市场上的产品预发布。DMRDF方法具有弹性，因此具有容错能力，并且还减少了数据集的冗余性。因此，它提高了模型的预测准确性。所提出的模型在分布式环境中工作以处理海量数据集，因此具有可伸缩性。此功能建模和预测的输出使制造商可以优化其新产品的设计。