To bridge the gap between available ML frameworks and their application in the broader chemistry community, we have developed PythiaCHEM (PYthon Toolkit for macHIne leArning in CHEMistry). This toolkit leverages simple statistical and ML techniques to facilitate the development of data-driven prediction models for chemistry, enhancing the efficiency and accuracy of such predictions. In this article, we illustrate the capabilities and versatility of PythiaCHEM on two distinct chemical tasks, providing step-by-step guidance on the best practices for ML. These tasks include the assessment of the activity of synthetic chloride anion transporters across membranes and predicting enantioselectivity in the Strecker synthesis of ↵-amino acids. Our results demonstrate that the right choice of ML methods and feature representation can lead to accurate and interpretable predictions at a much lower cost than fully atomistic modeling approaches would require.