With such a peerless rush in data, and with applications across an ocean of varieties, it is inevitable that many different opportunities will arise. But the analysis and interpretation of IoT data is often blocked by its great dimensionality, noise, and complexity. In the case of IOT datasets, machine learning efficacy models critically rely on feature engineering. In this paper, we look more closely at the CIC-IOT-2023 Dataset and use clever feature engineering to squeeze out all its predictive power. The 2023 CIC IoT Dataset is comprised of network traffic by many different types of IoT devices. It contains a variety communication protocols, device categories and traffic frequency patterns. This dataset presents such challenges as highly skewed classes, missing values and the need to distill significant features from raw network traffic logs. Our aim is to provide a comprehensive framework for feature engineering on the CIC IOT Dataset 2023. As such, it can be considered as an intellectual property that will be useful for researchers and practitioners in organizations involved with IOT security, network monitoring or anomaly detection. We hope to unearth hidden insights, increase predictive power and enhance IOT ecosystem security with the use of advanced feature engineering methods.