Spam Email Classifier

A Machine Learning project for detecting spam emails using Natural Language Processing (NLP).

The project preprocesses email text, extracts features using TF-IDF Vectorization, trains a Naive Bayes classifier, and predicts whether an email is Spam or Ham through a simple deployment pipeline.

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Features

• Email text preprocessing
• Text cleaning
• TF-IDF Vectorization
• Naive Bayes Classification
• Model serialization using Pickle
• Spam/Ham prediction
• Ready for deployment

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Dataset

The dataset contains labeled email messages.

Target classes:

• Spam
• Ham

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Technologies

• Python
• Pandas
• NumPy
• Scikit-learn
• NLTK
• Pickle
• Jupyter Notebook

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Machine Learning Pipeline

1. Load Dataset
2. Clean Text
3. Remove Stopwords
4. TF-IDF Vectorization
5. Train/Test Split
6. Train Naive Bayes Model
7. Evaluate Model
8. Save Model
9. Predict New Emails

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Project Structure

spam-email-classifier
│
├── dataset
├── models
├── notebooks
├── README.md
├── requirements.txt
└── .gitignore

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Installation

pip install -r requirements.txt

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Run

Open the notebook:

notebooks/project_deployment.ipynb

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Author

Basmala Khaled