In regions where access to professional tennis coaching is limited, aspiring athletes often find their potential capped by a lack of resources. SwingScale steps in as a game-changing mobile application, harnessing the precision of a custom-trained machine learning model to analyze tennis form right from the palm of the user’s hand [14]. The SwingScale mobile application was developed utilizing the Flutter framework, enabling one cross-compatible source for both Android and iOS devices. The custom-trained machine learning, which serves as the key component for the SwingScale project, is a GradientBoostingClassifier model trained on a variety of different professional and amateur tennis form samples. The model boasts an average accuracy rate of 96% and consistently returns processed video analysis in no more than 12.5 times the video's duration. Generally, throughout the project there were a few challenges encountered considering the advanced nature of the machine learning model and associated libraries and frameworks [1]. However, these challenges provided opportunities for reflection on the development process, and eventually were all overcome, and a final product able to be produced. Reflecting on the challenges posed during development, inspired proper experiments to be conducted to analyze the performance of the challenge features. Multiple experiments were carried out and documented in this paper. The results demonstrate a consistent accuracy rate provided testing data, as well as a consistent analysis and response time back to the user. Ultimately, the SwingScale mobile application offers a free, personalized tennis resource for individuals seeking to improve their game [3]. While other tools fall short in delivering tailored feedback, SwingScale empowers users with precise, consistent analysis of their recorded performance, thus providing classification and feedback customized to their unique skill level.