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.
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.