Clinical-Text-Classification

The goal of this NLP project is to develop a model that can accurately classify medical transcripts into their respective medical specialties. The target variable is the medical specialty and the features are the text data extracted from the medical transcripts. The project will involve several key steps. The text data will need to be preprocessed, which may involve tasks such as tokenization, stemming, and stop word removal. Once the data has been preprocessed, various machine learning algorithms are trained and evaluated on the dataset, such as Logistic regression, Support Vector Machines, or Categorical Boosting. The performance of each model can be evaluated using metrics such as accuracy, precision, recall, and F1-score. Finally, the model with the best performance can be selected and deployed to classify new medical transcripts into their respective medical specialties.