As an individual who has collected training activity data through Strava and Garmin APIs over the years, a substantial database has been amassed containing valuable information that has the potential to enhance performance and achieve athletic goals. Through personal observation, it has been recognized that optimizing training is not solely about exerting maximum effort daily or pushing the body to its limits. Instead, a balanced routine encompassing various training types and appropriate rest periods is crucial. While these observations are based on personal intuition, it is reasonable to assume that patterns exist within the aforementioned database, enabling the inference of current physical condition and the development of an optimal training routine geared towards personal performance improvement. Furthermore, like any extensive database, this dataset contains latent information that can be further explored and comprehended through visualizations and analytical techniques. Leveraging the power of data science, it is possible to delve deeper into the data, uncovering trends, patterns, and correlations that may not be immediately apparent. Such insights can then be employed to adjust training regimens and fine-tune approaches to cater to individual needs more effectively. The main objective of the project is to develop a software system application that allows any user to upload the data of their training activities and use it for inferring their fitness status. Thus, it allows any athlete who has registered their training activities to upload it and see the consequences of their weekly workouts on the performance for the following week, along with other relevant results.
