Search Results for author:
Found 5 papers, 0 papers with code
Modelling customer churn for the retail industry in a deep learning based sequential framework
As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits.
Sequence-aware item recommendations for multiply repeated user-item interactions
Recommender systems are one of the most successful applications of machine learning and data science.
Automatic Detection of Industry Sectors in Legal Articles Using Machine Learning Approaches
The experimental results show that the suggested automated industry analysis which employs ML techniques allows the processing of large collections of text data in an easy, efficient, and scalable way.
Optimal trees selection for classification via out-of-bag assessment and sub-bagging
The predictive performance of tree based machine learning methods, in general, improves with a decreasing rate as the size of training data increases.
Optimal survival trees ensemble
The top ranked survival trees are then assessed for their collective performance as an ensemble.
