Spam Email Classification📧

Coded a simple spam email classification model for ML subject in college.

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%

Thus, SVM with hyperparameter tuning gives the best accuracy(99.4%) for this dataset.