Enhancing family education pattern recognition with a random forest algorithm

Abstract

It is essential to understand family educational patterns to develop effective educational interventions and policies. Academic success, socioemotional development, and the general well-being of adolescents are all significantly affected by family dynamics and practices. Due to the wide range and variety of familial conditions, it cannot be easy to analyze and recognize these patterns. This paper offers an intelligent strategy for effectively identifying and analyzing family education patterns using the random forest (RF) algorithm. We begin by collecting an extensive data set that includes family factors, educational practices, and student outcomes. The raw data is first preprocessed using min-max normalization. Furthermore, we employ principal component analysis (PCA) to extract the pertinent attributes from the preprocessed data. The best features are chosen using the reptile search optimization (RSO) algorithm to increase the accuracy of the random forest. Comparing the empirical results to state-of-the-art methods demonstrates the suggested RF technique’s higher effectiveness in identifying family education patterns. The value of performance metrics such as accuracy (94.7%), precision (95.2%), recall (95.7%), and F1 score (96.1%). This strategy uses the RF to deliver insightful data that may guide targeted interventions, regulations, and individualized procedures to help students and their families succeed in education.