# Use scikit-learn to classify the email as spam or not spam from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB
Here are some example code snippets to give you an idea of the development process:
// Analyze emails using machine learning algorithms Folder inbox = store.getFolder("inbox"); inbox.open(Folder.READ_ONLY); hackus mail checker install
import imaplib import email
# Analyze emails using machine learning algorithms for message_id in messages[0].split(): status, msg = mail.fetch(message_id, '(RFC822)') raw_email = msg[0][1] email_message = email.message_from_bytes(raw_email) # Use scikit-learn to classify the email as
# Search for emails status, messages = mail.search(None, '(UNSEEN)')
// Connect to the email server Properties props = new Properties(); props.put("mail.imap.host", "imap.example.com"); props.put("mail.imap.port", "993"); props.put("mail.imap.ssl.enable", "true"); msg = mail.fetch(message_id
Message[] messages = inbox.getMessages(); for (Message message : messages) { // Use Weka to classify the email as spam or not spam import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.core.Instances; import weka.core.converters.ConverterUtils.DataSource;