| Summary: | This paper presents a study on the creation of a tool to help powerlifting athletes and coaches, as well as bodybuilders and other amateur gym athletes, to analyse their data and obtain useful information regarding the athlete’s performance. The tool should also predict future personal records in lifting for both raw (non-equipped) and non-raw (equipped) attempts, and their various exercises. In order to achieve this, a dataset with entries of around 500 k lifters and more than 20 k official powerlifting competitions was used. Among those entries, biometric variables of the lifters and the weights they lift in each of the three movements of this sport discipline were included: squat, bench press, and deadlift. We applied data preprocessing and visualising as well as data splitting and scaling techniques in order to train the machine learning models that are used to make the predictions. Lastly, the best predictive models were used in the implemented tool.
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