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.
We aim to train a classifier to accurately classify a person whether or not has a heart disease problem. The dataset is provided by UCI Machine Learning Repository.
This work provides insights on how to design a neural network to work with spam filtering problem.