Intrusion Detection Systems (IDS) are essential components for ensuring security in IoT networks. The effectiveness of IDS heavily depends on thorough exploratory data analysis (EDA) and preprocessing. This study provides an in-depth exploratory analysis of the ToN-IoT data set, with the aim of enhancing the precision and efficiency of machine learning-based IDS implementations. The ToN-IoT dataset, known for its heterogeneous composition and realistic representation of IoT environments, contains a wide range of network traffic attributes. This paper systematically addresses key preprocessing aspects, including missing data analysis, handling constant and quasi-constant features, categorical feature processing, and feature selection based on multicollinearity. Moreover, advanced feature generation methods are employed to significantly improve model prediction by capturing complex, nonlinear relationships among features. By rigorously evaluating correlations and feature distributions, we identify critical features while removing redundant and less informative ones, thus enhancing model clarity and predictive accuracy. This paper serves as a structured resource for researchers, detailing a robust data preprocessing pipeline crucial for developing accurate and reliable IDS models.