Using Random Forest to predict rare events (imbalanced) Credit Card Fraud data, and outperforming a Deep Learning Neural Network algorithm

A Deep Learning model for prediction of SMS Text Spam is created using R and H2O. The modeling process includes canonical NLP techniques. The model has an overall accuracy of 97.2%.

Based on human sentiment, does TF-IDF do a better job at extracting the most relevant words of a user-input Rotten Tomatoes review than XGboost's Feature Importance functionality?

Choosing the best 'xgboost' parameters and stop word list for the most accurate NLP multinomial response prediction.

Exploring the prediction accuracy of a decision-tree based model versus that of a generalized linear model, applied to a NLP multinomial response.

Using shopper-provided clothing reviews to predict the department name that the clothing item came from.

Teaching computers to taste wine - an NLP exercise in predicting multinomial response of wine taste score based on a description provided by sommeliers

Using NLP techniques, comparing respective accuracy of three lasso-regularized models, using n-gram max lengths of 1, 2, and 3

Extracting feature importance from the Wisconsin Breast Cancer dataset using Random Forest