📧 Email Spam Classifier

A machine learning project to classify emails as spam or ham (not spam) using natural language processing (NLP). The model uses techniques like TF-IDF vectorization and compares multiple classification algorithms including Logistic Regression, Naive Bayes, and Support Vector Machines (SVM).

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🚀 Features

• Text preprocessing with TF-IDF
• Trained and validated on the completespamassasin dataset
• Compared multiple models: Logistic Regression, Naive Bayes, and SVM
• Best performance with SVM (98%+ accuracy)
• Save and reuse model for prediction
• CLI script to test your own email

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🗂 Dataset

• Source: Kaggle - arXiv Spam Dataset
• File used: completespamassasin.csv
• Columns:
  • Body: The content of the email
  • Label: 0 = Ham, 1 = Spam

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🛠️ Setup

📦 Prerequisites

• Python 3.8+
• Virtual environment (optional but recommended)

🔧 Install dependencies

pip install -r requirements.txt

If requirements.txt is not provided, you can install manually:

pip install pandas numpy scikit-learn matplotlib seaborn

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🧪 How to Train the Model

1. Open the Jupyter notebook spam_classifier.ipynb or your Python script.
2. Load and preprocess the dataset.
3. Vectorize text using TF-IDF.
4. Train using the best model (e.g., SVM).
5. Save the model:

import joblib
joblib.dump(svm_model, 'spam_classifier_model.pkl')
joblib.dump(vectorizer, 'tfidf_vectorizer.pkl')

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🤖 Predict with Your Own Email (CLI)

Run the classify_email.py script to test any email string:

python classify_email.py "Win a brand new iPhone now! Click here to claim."

Sample Output:

Prediction: SPAM

This uses the saved model and vectorizer (.pkl files).

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📊 Model Comparison Summary

Model	Accuracy
Logistic Regression	96.6%
Naive Bayes	89.7%
SVM (Best)	98.6%

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🤝 Contributing

Feel free to fork this repo and improve on:

• Text preprocessing (lemmatization, stemming)
• Adding Flask or Streamlit UI
• Deploying to web

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📬 Contact

Author: [Ndongmo Christian] Email: [christianhonore2003@gmail.com] GitHub: @ndongchrist