Abstract
The application of neural networks in predicting consumer behavior and market trends has gained significant traction in recent years. This article explores how advanced neural network models are being employed to analyze and predict consumer purchasing patterns, brand preferences, and market shifts. It examines various neural network architectures, including feedforward, recurrent, and convolutional networks, and their effectiveness in capturing complex, non-linear relationships within consumer data. Furthermore, the article discusses the integration of neural networks with big data analytics, the role of artificial intelligence in market prediction, and the challenges of implementing these technologies in real-world scenarios.
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