You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Through Python 3.6 and scikit-learn, the model will predict the language of new data. Steps include data preprocessing, feature extraction, model training, and evaluation. Techniques like tokenization, stopwords removal, and normalization will enhance model performance. Classification algorithms such as Logistic Regression will be explored. The project culminates in a language detection pipeline, evaluated for accuracy, precision, and recall.
The text was updated successfully, but these errors were encountered:
Through Python 3.6 and scikit-learn, the model will predict the language of new data. Steps include data preprocessing, feature extraction, model training, and evaluation. Techniques like tokenization, stopwords removal, and normalization will enhance model performance. Classification algorithms such as Logistic Regression will be explored. The project culminates in a language detection pipeline, evaluated for accuracy, precision, and recall.
The text was updated successfully, but these errors were encountered: