📧 Spam Email Classifier – A Machine Learning Project in Python

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📌 Project Overview

This project demonstrates a simple yet effective spam email classifier using Python and Scikit-learn. It uses natural language processing with TF-IDF vectorization and a Naive Bayes algorithm to detect whether an email message is spam or not.

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🎯 Purpose

To build a lightweight text classification model that identifies spam messages from legitimate ones using machine learning — suitable for learning basic NLP and model deployment.

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🧠 Features

✅ Detects spam vs non-spam emails
✅ Uses TF-IDF to vectorize message text
✅ Built using Scikit-learn’s pipeline (clean and efficient)
✅ Evaluates performance using accuracy & classification report
✅ Saves the trained model for reuse with joblib

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⚙️ Technologies Used

• Python 3.6+
• pandas – for dataset handling
• scikit-learn – for ML pipeline, vectorization, and model
• joblib – for saving the trained model

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🗂️ Dataset

A sample dataset (spam_email_dataset.csv) is automatically created if not already present. It includes labeled messages (spam = 1, non-spam = 0) for training/testing the model.

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🚀 How to Run

1. Clone the repository

git clone https://github.com/yourusername/spam-email-classifier.git
cd spam-email-classifier

2. Run the script

python spam_email_classifier.py

The model will train, evaluate accuracy, print a classification report, and save the trained model to spam_classifier_model.pkl.

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📊 Sample Output

    Accuracy: 1.0

    Classification Report:
                   precision    recall  f1-score   support

             0       1.00      1.00      1.00         1
             1       1.00      1.00      1.00         0

     accuracy                             1.00          1
    macro avg        1.00      1.00       1.00          1
  weighted avg       1.00      1.00       1.00          1

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📄 License

MIT License — open for use, learning, and modification.

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👨‍💻 Developed By

Shitanshu

🔗 GitHub: https://github.com/shitanshu15