See References for more details about preprocessing. We’re asking you to look at the steps a new user takes on Fiverr before and during registration, and predict which of them will become spammers. These techniques included cleaning of contractions and special characters, simpler spelling correction, lowercasing of words whose uppercased versions didnt exist in embeddings and/or usage of stemmers and lemmatizer. If you know the difference between machine and deep learning, then this one’s for you. Anyone who tends to make errors similar to those the model has been trained on could therefore benefit from passing their messages through this model. Stay tuned for more data competitions to come.įiverr is teaming up with Kaggle to bring you a contest as you’ve never analyzed before. This tells us that applying the Deep Text Corrector model to a potentially errant writing sample would, on average, result in a more grammatically correct writing sample. It was great having so many great participants, and we would like to thank you all for your participation. For the challenge Home Depot provided a dataset, that contains pairs of user search queries and products, that might be relevant to these queries. He reached the highest score and got some big prizes. The Home Depot Product Search Relevance Kaggle competition challenged participants to build such a model to predict the relevance of products returned in a response to a user query.
Minixhofer alerted Koh, who then contacted Kaggle to report the fraud.
The solution is quite straightforward: we just scraped the answers, he brazenly boasted. Our data experts collaborate and compete to predict new spammers on the Fiverr platform, and we have a winner! Chumajin is a Kaggle Competition Master and Notebook Grandmaster, and has participated in many competitions on the platform. Over a year ago, in a Kaggle competition to detect and flag bogus questions posted on Quora, a Q&A website, he described how he and his team achieved such a good score.
0 kommentar(er)
