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.
In this work, we would like to explore the characteristics of this bike share system and try to come up with recommendations to provide potential solutions for the questions above based on data analysis wtih the Citi Bike system 2019 data that is publicly available.
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.