Machine Learning

The metal wings of aircraft and the feathered wings of nature often compete for the same airspace. At times, this leads to collisions and may result in damage to aircraft and sometimes injuries or even death to passengers, crew, and wildlife too. Wildlife strikes are the consequence of various factors. These factors include migration of species, weather, time-of-day, phase of flight and the region of the flight’s route. However, some of the questions remain to be addressed with solid observational data.

This dataset contains a Magazine_Subscriptions.csv and a Gift_Card.csv, both of which contains a column of item id, a column of user(reviewer) id, a column of user rating, and a column of reviewed time. This data is originally collected by Ni, Jianmo, Jiacheng Li, and Julian McAuley in “Justifying Recommendations Using Distantly-Labeled Reviews and Fine-Grained Aspects,” proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.

Crowdfunding campaigns frequently fail to reach their funding goals, posing a significant challenge for project creators. To address this issue and empower future crowdfunding stakeholders, accurate prediction models are essential. This study evaluates the relative significance of diverse modalities (visual, audio, and text) in predicting campaign success.

Mulan , a sourceforge net multi-target dataset available in www.openml.org. Despite the numerous interesting applications of MTR, there are only few publicly available datasets of this kind - perhaps because most applications are industrial - and most experimental evaluations of MTR methods are based on a limited amount of datasets. For this study, much effort was made for the composition of a large and diverse collection of benchmark MTR datasets.