Predictive Analysis of Employee Turnover in IT Using a Hybrid CRF-BiLSTM and CNN Model

Many people in the field of information systems are still devoting a lot of time and effort to researching and analyzing the correlation between IT spending and financial returns. More study is needed to establish the particular mechanisms or intermediate processes through which IT investments deliver value to firms, but there is growing consensus that such investments are beneficial. The proposed methodology rests on three primary pillars: data preparation, feature selection, and model training. As part of the data preparation process, discordant dimensions, redundant data, Qualitative features are useless, useless, and underutilized because they lack utility, value, and data. Extensive Optimized PCA was utilized for feature selection. CRF-BiLSTM-CNN is then used to train the models. When compared to the two most common alternatives, CNN and BiLSTM, the proposed technique performs significantly better. The success rate for the proposed method was 98.87%.