UTA Datathon 2023 Innovative Data Intelligence Research Lab Machine Learning Text Classification Challenge - DO NOT EDIT

 

Use provided starting files to train a machine learning model that, given an input sentence, determines whether it is a factual statement that is worth fact-checking, i.e., whether the general public would be interested in knowing whether it is true or not. Your submissions should include a CSV file with some of the following columns: Id: id for a given sentence; Text: sentence's text; Category: the column has two possible values: Yes (indicating a check-worthy sentence) or No (indicating a sentence that is not check-worthy).

Recent Submissions

  • Machine Learning Text Dataset 

    Jadhav, Mayur (2023-05-05)
    Use predictive modelling to generate Models which were later used for predicting the new dataset. Achieved accuracy of 91.23%.

  • A Naive-Bayes Text Classifier using Laplace smoothing 

    Pudu, Prithvidhar (2023-05-03)
    The text classifier was built using kaggle.com, a website that provides GPU resources to train large amounts of data. Using this website, I created a Naive-Bayes Text Classifier in Python to classify articles on whether ...