Skip to content

Latest commit

 

History

5 Commits

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 

Repository files navigation

sms-spam-classification

In this project various classifiers are been used followed by accuracy,
They are:-
(1) Logistic Regression: 95.33%

(2) Multinomial Naive Bayes: 97.60%

(3) Decision Tree Classifier: 95.81%

(4) Support Vector Machine: 97.12%

(5) Random FOrest Classifier: 97.54%

The output will be classified as a Spam or Ham.

About

In this project, concepts of Natural Language Processing were used with the help of various Classification algorithms. The output will be classified as Spam or Ham.

Topics

Resources

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Contributors

Languages