Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and difficulty with balance and coordination. It is highly important for early detection of Parkinson’s Disease for patients to receive proper treatment. This paper aims to present a preliminary data mining procedure that help Parkinson’s Disease patients slow down their progression of the disease while helping early detection of the disease. For early non-invasive treatment, our research first analyses the early symptoms of Parkinson’s Disease, designs/selects a proper demo video, let the user follow the demo to exercise and upload his exercise video to our deep learning APP: LaBelle. LaBelle utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both demo and the user, calculates the angles created between different joints and major body parts. LaBelle’s AI model uses a K-means clustering algorithm to create a group of clusters for both demo and the user dataset. Using the two sets of clusters, LaBelle identifies the key frames in the user video and searches the demo cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. Meanwhile, if the user is willing to donate their exercise data, he can simply input his age, whether he is a PD patient (maybe for how long) anonymously. Then his data can be stored into our customized dataset, used in data mining for Parkinson’s Disease prediction, which involves building/training our deep learning CNN model and help early detection of Parkinson’s Disease.