Building a Spam Email Classifier Using AI: A Basic Application
Spam Email Classifier with Node.js
This project uses Node.js and the Natural library to create an AI-based application that classifies emails as spam or not spam. The application uses a Naive Bayes classifier for spam detection, which is a common algorithm for text classification tasks.
Prerequisites
Before you begin, make sure you have the following installed:
- Node.js: Download Node.js
- npm (Node Package Manager): npm comes with Node.js installation.
Steps to Set Up the Project
Step 1: Set Up Your Project
- Create a Project Folder: Open your terminal or command prompt and create a new folder for your project.
mkdir spam-email-classifier cd spam-email-classifier
- Initialize a Node.js Project: Inside the folder, run the following command to create a package.json file.
npm init -y
Step 2: Install Dependencies
Run the following command to install the required dependencies:
npm install natural
- natural: A library that provides various NLP (Natural Language Processing) tools including classification using Naive Bayes.
Step 3: Create the Spam Classifier
Create a new JavaScript file (e.g., spamClassifier.js) and add the following code:
const natural = require('natural');
// Create a new Naive Bayes classifier
const classifier = new natural.BayesClassifier();
// Sample spam and non-spam data
const spamData = [
{ text: "Congratulations, you've won a 00 gift card!", label: 'spam' },
{ text: "You are eligible for a free trial, click here to sign up.", label: 'spam' },
{ text: "Important meeting tomorrow at 10 AM", label: 'not_spam' },
{ text: "Let's grab lunch this weekend!", label: 'not_spam' }
];
// Add documents to the classifier (training data)
spamData.forEach(item => {
classifier.addDocument(item.text, item.label);
});
// Train the classifier
classifier.train();
// Function to classify an email
function classifyEmail(emailContent) {
const result = classifier.classify(emailContent);
return result === 'spam' ? "This is a spam email" : "This is not a spam email";
}
// Example of using the classifier to detect spam
const testEmail = "Congratulations! You have won a 00 gift card.";
console.log(classifyEmail(testEmail)); // Output: "This is a spam email"
// Save the trained model to a file (optional)
classifier.save('spamClassifier.json', function(err, classifier) {
if (err) {
console.log('Error saving classifier:', err);
} else {
console.log('Classifier saved successfully!');
}
});
Step 4: Run the Classifier
To run the classifier, open a terminal and navigate to the project folder. Then, run the following command:
node spamClassifier.js
You should see an output similar to this:
This is a spam email Classifier saved successfully!
Step 5: Load the Saved Classifier (Optional)
You can load the classifier model later to classify new emails. Here’s how to load the model and classify new emails:
const natural = require('natural');
// Load the saved classifier
natural.BayesClassifier.load('spamClassifier.json', null, function(err, classifier) {
if (err) {
console.log('Error loading classifier:', err);
} else {
// Classify a new email
const testEmail = "You have won a free iPhone!";
console.log(classifier.classify(testEmail)); // Output: 'spam' or 'not_spam'
}
});
Step 6: Improve the Model (Optional)
To improve the accuracy of the spam classifier, you can:
- Add more training data: Include more samples of spam and non-spam emails.
- Experiment with different algorithms: Try other classification algorithms or models if Naive Bayes is not sufficient for your needs.
- Use advanced techniques: Implement deep learning or neural networks for more complex classification tasks.
Step 7: (Optional) Integrate with Email System
If you want to send or receive emails from the app, you can use the Nodemailer library to send emails.
- Install Nodemailer:
mkdir spam-email-classifier cd spam-email-classifier
- Send an Email (Example):
npm init -y
Conclusion
This guide walked you through setting up an AI app using Node.js and Naive Bayes to classify emails as spam or not spam. You can expand this app by:
- Adding more training data for better accuracy.
- Using more advanced machine learning techniques.
- Integrating the classifier into a web application or email system.
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