Models implemented using Scikit learn - SVM(along with hyperparameter tuning), Naive Bayes, Decision Tree classifier, Random Forest Classifier, Gradiant Boosting.
Additional libraries used - NLTK, Seaborn, Pandas, Matplotlib, Numpy.
F1-score and accuracy obtained -
- SVM with HP tuning - 98.88% and 99.4%
- SVM - 97.4% and 98.8%
- Naive Bayes - 75.1% and 90.9%
- Decision Tree classifier - 90.26% and 95.4%
- Random Forest classifier - 95.33% and 97.89%
- Gradiant Boost - 93.25% and 96.9%