AI-Powered Career Guidance A Scalable Model for Personalized Recommendations

AI Career Guidance Framework predicts student employment post-graduation (1,000 records, 20 features). Three architectures: ACFE-Net (Autoencoder-Convolutional Feature Extraction Network) , combining autoencoder with ensemble classifiers and attention mechanism; DTFE-Net (Dual-Transformer Feature Extraction Network), utilizing dual transformer towers; and HRAF-Net (Hybrid Residual-Attention-BiGRU Fusion Network), fusing residual blocks, attention mechanisms, and bidirectional GRU layers. The framework delivers: correlation analysis, balanced classification, and deployment-ready inference with actionable career suggestions (academic improvement, internships, mentoring, upskilling). Impact includes personalized recommendations, at-risk student identification, and data-driven career counseling.