Baseball is a popular sport played in the United States of America. Performing analytics on base ball games gives ideas for improving a teams performance. It is possible to predict pitch type based on parameters associated with pitching. Input data was collected by scraping from various sources at University of Illinois, Urbana-Champaign. Extreme gradient descent algorithm was chosen for predicting pitch type. The model achieved accuracy of 85.9% with minimum F1 score of 0.7 for each class.

Credit cards are used for a significant portion of payment system all across the world. There is a risk of fraud with credit cards if credit card credentials are stolen by someone. With machine learning algorithms, we can detect if a transaction is genuine or fraudulant. The data for building the models is taken from a Kaggle Competition. After trying variouos models, model having XGBoost algorithm performed best, giving the ROC area under the curve of 0.9973. The model also achieved the mean sensitivity of 0.9964.