In the project, the algorithm encodes the category labels using label encoding and prepares the data for training a machine learning model. It uses the TF-IDF vectorizer to convert the clean text into numerical features and splits the data into training and testing sets. Then, it trains a K-nearest neighbors classifier using the One-vs-Rest strategy and evaluates the model's performance using accuracy scores and a classification report.
Google colab file can be found here