The objectives of this thesis were to develop a machine learning tool-chain and to investigate the relationship between heart rate and trigger squeeze and shooting accuracy when firing a handgun in a simulated environment. There are several aspects that affects the accuracy of a shooter. To accelerate the learning process and to complement the instructors, different sensors can be used by the shooter. By extracting sensor data and presenting this to the shooter in real-time the rate of improvement can potentially be accelerated. An experiment which replicated precision shooting was conducted at SAAB AB using their GC-IDT simulator. 14 participants with experience ranging from zero to over 30 years participated. The participants were randomly divided into two groups where one group started the experiment with a heart rate of at least 150 beats per minute. The iTouchGlove2.3 was used to measure trigger squeeze and Polar H10 heart rate belt was used to measure heart rate. Random forest regression was then used to predict accuracy on the data collected from the experiment. A machine learning tool-chain was successfully developed to process raw sensor data which was then used by a random forest regression algorithm to form a prediction. This thesis provides insights and guidance for further experimental explorations of handgun exercises and shooting performance.