Enhancing Employee Promotion Prediction with a Novel Hybrid Model Integrating Convolutional Neural Networks and Random Forest

In the ever-evolving landscape of human resources, the critical task of identifying employees ready for promotion remains a complex challenge. To address this issue, we propose a novel hybrid model that seamlessly integrates Convolutional Neural Networks (CNNs) with Random Forest. Through a two-step process, we initially train the CNN comprising Conv1D and Dense layers. Subsequently, we harness the extracted features from the Nth layer, merging them with the original dataset. These augmented features are then input into the Random Forest algorithm. This innovative approach has yielded remarkable results, achieving an astounding accuracy rate of 99%. This surpasses the performance of both standalone Random Forest and CNN models, as well as various other machine learning methods. The presented model not only enhances the prediction accuracy for employee promotions but also offers a powerful tool for HR managers seeking to make informed and data-driven decisions in workforce advancement, ultimately contributing to more effective and efficient talent management.