Resource Attrition Prediction Model Using Digital Feature Analysis

The patent replaces manual mechanical analysis with an automated machine learning system. The server automatically collects employee data, selects relevant features using algorithms, trains prediction models, and generates attrition risk assessments without human intervention in the analysis process, thereby reducing time loss while maintaining or improving identification accuracy.

The system enables self-service automated analysis where the machine learning model independently performs data collection, feature selection, model training, and prediction generation. The server autonomously identifies attrition factors and provides recommendations without requiring human analysts to manually process each case, significantly reducing analysis time while preserving comprehensive factor identification.

The patent extracts only the most relevant features from comprehensive employee data using automated feature selection algorithms. Instead of analyzing all available data, the system identifies and selects only those features that have the strongest predictive power for attrition, thereby maintaining high prediction accuracy while significantly reducing computing resource consumption and processing time.

The system applies partial action by focusing computational resources on analyzing only the critical subset of features that drive attrition predictions, rather than processing the entire dataset. This selective approach achieves sufficient prediction accuracy with reduced energy consumption and faster processing, balancing comprehensiveness with efficiency.