The goal of this project is to construct a model for a given sentence and the label sentiment to predict what phrases in the sentence that best support the given sentiment.
We explore the potential of creating new recipes via text data. Our goal has two folds. First, we aim to classify the cuisine based on ingredients. Second, we want to predict an ingredient that is missing from a given list of ingredients and a cuisine name.
It classifies fraudulent credit card transactions based on the result of a PCA transformation. We explore various classic classification algorithms in this work.
This work provides insights on how to design a neural network to work with spam filtering problem.
We estimated probabilities for tetrahedral and triangular dice using the sphere projection method and multivariate calculus. To test our results, we printed several dice of varying sizes. After a few thousand initial rolls, we realized that our calculations weren’t accurately describing the situation and that bias could have been introduced due to rolling the dice by hand. Therefore, we built a machine to roll the dice.