🚫 Detecting Email Spam with Amazon SageMaker – A Hands-on ML Project

Hey readers!
Today, I’m excited to share a project that combines the power of Machine Learning with Amazon SageMaker – an end-to-end platform that lets you build, train, and deploy models at scale.

The goal of this project is to build an Email Spam Detection system that can classify whether a given message is spam or not spam using natural language processing and a machine learning model trained on AWS.

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🎯 Why Email Spam Detection?

Email spam is a common annoyance, and in some cases, a security threat. Identifying and filtering spam effectively is critical for maintaining secure and clean inboxes. While tools like Gmail already do this, understanding how such a system works under the hood can be a fantastic learning opportunity for anyone getting into machine learning or cloud deployment.

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🛠️ Tools & Technologies Used

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🧠 How the Project Works – Step by Step

1. 📁 Dataset Collection

We use the SMS Spam Collection dataset which contains labeled messages:

• ham: Not spam

• spam: Spam email or SMS

Each line contains the label and the message.

2. 📊 Data Preprocessing

Text data is messy, so we:

• Convert to lowercase

• Remove punctuation and stopwords

• Tokenize and lemmatize words

• Vectorize using TF-IDF

pythonCopyEditfrom sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(messages)

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3. 🤖 Model Training on SageMaker

We use Amazon SageMaker’s built-in XGBoost container or train a custom Naive Bayes model using a Jupyter Notebook.

Steps:

• Upload dataset to S3

• Create a SageMaker training job

• Choose an algorithm (XGBoost / custom script)

• Monitor training metrics

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4. 🚀 Deploying as a SageMaker Endpoint

After training:

pythonCopyEditpredictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m5.large')

You can now make real-time predictions using the endpoint by sending a string message.

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5. 🧪 Testing the Model

pythonCopyEditresponse = predictor.predict("You’ve won a free vacation to the Bahamas!")
print(response)  # Output: spam

Test various messages to validate model accuracy.

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📊 Results & Accuracy

The trained model achieved:

• Accuracy: 98.1%

• Precision (spam): 97.5%

• Recall (spam): 96.9%

The model can be further improved with hyperparameter tuning or using BERT-based embeddings.

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🔐 Why SageMaker?

Amazon SageMaker provides:

✅ Scalable infrastructure
✅ Pre-built containers for training & inference
✅ One-click deployment
✅ Easy integration with S3, Lambda, and other AWS services

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💡 Future Enhancements

• Use deep learning with AWS SageMaker JumpStart models

• Add real-time email ingestion via AWS SES or Lambda

• Auto-retrain using Amazon SageMaker Pipelines

• Build a web interface to test your spam detector

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📚 What I Learned

• How to preprocess NLP data efficiently

• Building and training ML models in SageMaker

• Hosting endpoints and deploying at scale

• Interpreting model evaluation metrics

• Automating workflows in AWS

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🔗 Conclusion

This project helped me gain hands-on experience in both machine learning and cloud deployment. If you're interested in NLP, security, or just want to explore AWS SageMaker – this is a perfect starter project.

Want to build this yourself?
👉 [GitHub Repo (https://github.com/MadanMohan-M/email-spam-detection-using-AWS-Sage-Maker)]

Let me know if you'd like a tutorial version or want to collaborate on the next phase!