In this competition, all of the students received a historical data set of NBA players and statistics associated with them (such as games played, minutes played, points, etc.). Using this data set, we had to output the most profitable investment strategy to create the optimal portfolio of player investments. The investments that we were making were based on the predictions of a classification model predicting whether or not a given player will be inducted into the Hall-of-fame (and the probability thereof). These players were profitable investments.

Throughout this project, I utilized various types of Machine Learning models in Python such as Decision Trees, Bagging Models, Random Forests, Multinomial Naive Bayes, and K Nearest Neighbors. I iteratively constructed each of these models using feature engineering, grid search, comparing ROC AUC scores, correlation matrices, variable filtering, and train test splitting. Ultimately, the model that produced the highest profit of $3,000,000+ was a Random Forest model.