📧 Spam Email Classifier

A machine learning web application that classifies whether an email subject line is Spam or Not Spam using a Naive Bayes model. Built using Python, Flask, and scikit-learn.

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🧩 Problem Statement

Spam emails have become a significant threat, cluttering user inboxes and often containing phishing links or harmful content. Efficient and real-time detection of spam can:

• Enhance user experience
• Improve security
• Reduce time spent on managing emails

While major email providers use large-scale filters, this project demonstrates how machine learning can be used to build a simple and effective spam classifier for email subject lines using traditional NLP techniques.

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🚀 Project Overview

This project allows users to enter an email subject line through a web interface. Upon submission, the subject is processed using a pre-trained Multinomial Naive Bayes model. The result — Spam or Not Spam — is instantly displayed to the user.

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🛠️ Tech Stack

Technology	Purpose
Python	Backend logic and model training
Flask	Web framework for frontend-backend integration
HTML/CSS	UI for input and output
scikit-learn	Machine Learning - model training and prediction
Pandas	Data handling and preprocessing

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✨ Features

• 🔍 Real-time spam detection using email subject line
• 📦 Model is trained at runtime from spam.csv
• 🧠 ML pipeline using CountVectorizer + MultinomialNB
• 🎨 Clean, responsive, glassy-themed user interface
• 🔁 Post/Redirect/Get pattern to avoid result repetition on refresh
• 💡 Lightweight and easy to deploy

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🧠 Future Scope

• ✅ Use full email content (body, metadata) for classification
• ✅ Replace CountVectorizer with TF-IDF or word embeddings
• ✅ Add user login and spam filtering history
• ✅ Deploy using Docker or host on platforms like Heroku or Render
• ✅ Improve UI/UX with animations and mobile responsiveness

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🖥️ Setup Instructions

1. Clone the Repository

git clone https://github.com/SrujanPR/Spam-Email-Classifier.git
cd spam-classifier-webapp

2. Install required dependencies

pip install requirements.txt

3. Run the model

Go to the model.ipynb file and execute all the cells and click on save.

4. Run the App

Now go the app.py file and run it by tying the following code in the terminal

python app.py

Then open your browser and go to: http://127.0.0.1:5000

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📁 Project Structure

Spam-Email-Classifier/
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├── spam.csv                  # Dataset (SMS Spam Collection Dataset)
├── app.py                    # Flask web server and ML pipeline
├── templates/
│   └── index.html            # Frontend HTML page
├── README.md 
├── model.ipynb               # Python notebook where the model is present
├── Sample.png
└── requirements.txt

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📸 Preview

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📞 Contact

Built by SRUJAN P R
Feel free to reach out for collaborations, ideas, or improvements. Pull requests are welcome! If you’d like to improve the assistant or contribute new agents or features, feel free to fork the repo and submit a PR.

LinkedIn • Email

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📜 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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