Naive Bayes is a supervised classification technique based on Bayes' Theorem with an assumption of independence among predictors. That is, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
It is a popular technique for text categorization, judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as features.
Goal: Previously unseen records should be assigned a class as accurately as possible
- We have a bunch of emails classified as 'spam' and a bunch of emails classified as 'ham' (not spam)
- The emails are first read and stored in a dataframe. They are then parsed using CountVectorizer
- This information is used to train the model and its prediction is then tested with a sample input
Python Libraries used: pandas, numpy, io, os, CountVectorizer and MultinomialNB from sklearn
The Spam classifier classifies the given input as a spam/ham.
https://spam-ham-classifier.streamlit.app/
- Direct Marketing
- Fraud Detection
- Text Classification
- Spam Filtering
- Categorizing News
- Medical Diagnosis
- Face Recognition