The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails.
This is a csv file containing related information of 5172 randomly picked email files and their respective labels for spam or not-spam classification.
Following models have been employed along with TfidfVectorizer:
- Logistic Regression-95.61%
- SVC-98.03%
- KNN-94.35%
- Random Forest Classifier-97.04%
