Revolutionizing Business with AI: Unlocking Customer Insights Through Unsupervised and Supervised Learning for Behavior Prediction

This study navigates the intricate landscape of understanding and classifying customer behavior amid dynamic consumer preferences and vast data. Conventional segmentation methods often fall short, resulting in suboptimal marketing strategies and reduced satisfaction. Our research advocates for an advanced approach integrating unsupervised and supervised learning techniques to enhance accuracy in customer behavior classification. By synergizing these methodologies, businesses can precisely tailor products and services, fostering engagement and success in the marketplace. The study emphasizes the critical need for a data-driven, machine learning-based solution to unlock the full potential of customer behavior analysis. We present a framework advancing customer behavior classification through unsupervised learning, encompassing data exploration, clustering, and dimensionality reduction. The research extends its impact by enhancing application security for sensitive data. Evaluations demonstrate outstanding accuracy levels for each algorithm: Logistic Regression (95%), Random Forest (99%), SVM (99%), CNN (99.3%), and ANN (99.4%). These results underscore the efficacy of our proposed methodology, representing a significant advancement in precision and depth of customer behavior classification.