There are no reviews yet. Be the first to send feedback to the community and the maintainers!
Knowledge-Graph
Creating Knowledge using NLP libraries NLTK, Spacy, textacy and graph library NetworkXDisease-Predictor
SMSSpamClassifier-from-NLP-using-RandomForest-and-GradientBoosting-Classifier
Here i simply took SMSSpamClassifier data and using NLTK library along with RandomForest and GradientBoosting Classifier from sklearn.esemble, i classified if that SMS is spam or ham. Firstly, i cleaned raw data removing punctuations, stopwords and tokenizing along with stemming and lemmatizing. Then, moving forward i test vectoring with CountVectorize, N-grams and Tf-Idf vectorizer. And then, moving forward to feature enginnering i add two new features i.e. text length and percentage of punctuations on text.Then evaluate them if it was useful for detecting spam and if transformation was required. Finally it was applied to Gradient Boosting and Random forest classifier along with GridSearch and their performance was evaluated for selecting better hyperparameters using GridSearchCv.Language-Translation-Using-Transformer
Here, due to inavailability of data of my native language. I have used English to Spanish Translation.Love Open Source and this site? Check out how you can help us