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Cloud computing offers a wide range of services, but it comes with some challenges. One of these challenges is to predict the resource utilization of the nodes that run applications and services. This is especially relevant for container-based platforms such as Kubernetes. Predicting the resource utilization of a Kubernetes cluster can help optimize the performance, reliability, and cost-effectiveness of the platform. This paper focuses on how well different resources in a cluster can be predicted using machine learning techniques. The approach consists of three main steps: data collection and extraction, data pre-processing and analysis, and resource prediction. The data collection step involves stressing the system with a load-generator (called Locust) and collecting data from Locust and Kubernetes with the use of Prometheus. The data pre-processing and extraction step involves extracting relevant data and transforming it into a suitable format for the machine learning models. The final step involves applying different machine learning models to the data and evaluating their accuracy. The results illustrate that different machine learning techniques can predict resources accurately.