The Impact of Data Preprocessing on Machine Learning Model Performance: A Comprehensive Examination

Machine Learning (ML) models have been extensively applied in various fields to enhance prediction. For instance, in cybersecurity, they examine large amounts of data, establish trends in the data, and draw insights from previous events, to enhance detection and respond to cyber threats. Random Forest, Logistic Regression, K-Nearest Neighbor and LSTM are some of the popular ML models vastly used for anomaly detection. The accuracy of these models is therefore the cornerstone of the organization’s information systems security since wrong predictions result to false positives and negatives which significantly reduce employees’ output and may result into workers’ frustrations when interacting with the information systems. Among the many factors that affect ML model performance, data pre-processing has been underscored. Using the various publicly available datasets, this paper examines the impact of data preprocessing techniques on selected ML model architectures’ performance. Training time, Accuracy, Precision, Recall and F1 scores are used for evaluating the ML models’ performance.