Machine Learning project using 15 seasons of NBA data (2005-2020) to predict player position. Decision Trees, Random Forests, Support Vector Machines (SVMs) and Gradient Boosted Trees (GBTs) utilized. Example PCA transformation of X-data included as well. Specific predictions made at the end, leading to interesting insights into what players are out-of-position.
