Eye-tracking data provides valuable insights into human behavior, but its noisy and unstable nature necessitates robust preprocessing for accurate analysis. This study evaluates a tailored preprocessing pipeline designed to enhance machine learning classifier performance. Unlike prior research focusing on isolated preprocessing steps, this work systematically combines and compares techniques, including missing value imputation, outlier handling, and normalization, specifically optimized for eye-tracking data. The pipeline's impact is tested on classification accuracy, particularly in detecting academic dishonesty. By experimenting with diverse methods for handling missing data, outliers, and feature scaling, we assess their combined effects on classifier performance. A Random Forest classifier is utilized due to its proven effectiveness in prior studies {nurwulan_random_2020}. This research not only builds on earlier findings but extends them by optimizing each preprocessing step. Results show a well-designed pipeline significantly enhances classification accuracy, offering insights into optimal preprocessing techniques for behavioral prediction tasks.