<![CDATA[The purpose of this study is to improve gender categorization by examining the usage of keyboard dynamics, with enhanced model performance through data standardization and appropriate feature selection. Features including gender, age, handedness, language, education, and metrics measuring typing behavior like mean_latency, std_latency, and frequency are all included in the dataset. Correlation analysis served as the foundation for the feature selection procedure, and data normalization was performed to guarantee consistency among the characteristics that were chosen. Because of its stability and capacity to handle complicated data, the Random Forest classifier was selected. The findings demonstrate that the Random Forest model performed better than benchmark models, such as SVM, in terms of F1-score, recall, accuracy, and precision. The results emphasize how important it is to choose the appropriate characteristics and standardize the data in order to increase predictive accuracy. By showcasing keystroke dynamics' capacity for gender categorization, this study advances the area and creates opportunities for further research in user experience improvement, digital service customisation, and online behavioral analysis. All things considered, the study highlights how crucial feature engineering and model tuning are to getting better categorization outcomes.]]>
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