📧 Email & SMS Spam Classifier ✉️

A Machine Learning project that helps classify emails and SMS messages into Spam or Non-Spam using NLP techniques.

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📖 Introduction

Welcome to the Email & SMS Spam Classifier project! This project leverages Natural Language Processing (NLP) techniques and Machine Learning to accurately classify messages as Spam or Non-Spam (Ham). Whether you want to filter out annoying spam messages or identify legitimate communications, this tool is designed to help you.

With an intuitive user interface built using Streamlit, users can easily input emails or SMS messages and receive real-time classification results.

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🚀 Key Features

• 💡 Advanced NLP Techniques: The classifier employs cutting-edge NLP algorithms like Naive Bayes and Support Vector Machines (SVM) to ensure high accuracy.
• 📲 Multi-Platform: Supports classification for both Emails and SMS messages.
• 👨‍💻 User-Friendly Interface: Built using Streamlit, providing a simple and clean UI for users to interact with the model easily.
• 📈 High Accuracy: Achieved a 95% accuracy rate with significant reduction in false positives, ensuring reliable classifications.
• ⚡ Real-Time Results: Quickly processes inputs and delivers instant predictions, making it useful for fast-paced environments.

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🛠️ Technologies Used

This project integrates various tools and libraries to achieve high performance and ease of use:

• Python: The primary language for building the model and backend logic.
• Scikit-learn: For building, training, and validating the machine learning models.
• Natural Language Toolkit (NLTK): Used for text preprocessing, tokenization, and feature extraction.
• Streamlit: Framework used for creating the web interface for easy interaction with the classifier.
• Pandas: For efficient data handling and manipulation.
• Google Colab: Used for faster model training and experimentation in the cloud.

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🎯 How the Model Works

The spam classifier analyzes the textual content of emails and SMS messages to predict whether they are spam or ham. Here’s how it works:

1. Data Collection: A dataset of labeled email and SMS messages is used to train the model.
2. Preprocessing: The textual data is preprocessed using techniques such as tokenization, removing stop words, and lemmatization.
3. Feature Extraction: Relevant features are extracted using TF-IDF (Term Frequency-Inverse Document Frequency) to convert text into numerical vectors.
4. Model Training: Using algorithms like Naive Bayes and SVM, the classifier is trained on this preprocessed and feature-extracted data.
5. Prediction: The trained model is used to predict whether a new email or SMS message is spam or non-spam.

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🔍 Model Performance

• Accuracy: Achieved 95% accuracy in distinguishing between spam and ham.
• Error Reduction: False positives were reduced by 80%, ensuring reliable spam filtering.
• Cross-Validation: 5-fold cross-validation was performed to ensure model robustness.

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💻 Installation Guide

Step-by-Step Instructions

1. Clone the Repository

   git clone https://github.com/AshimaSingh0610/email-sms-spam-classifier.git
   

2. Navigate to the Project Directory
   Change your directory to the cloned project folder:

   cd email-sms-spam-classifier
   

3. Create a Virtual Environment
   It's a good practice to create a virtual environment for Python projects to manage dependencies:

   python3 -m venv venv
   

4. Install Required Dependencies
   Install the necessary Python libraries using the requirements file included in the project:

   pip install -r requirements.txt
   

5. Run the Application
   Start the Streamlit web app with the following command:

   streamlit run app.py
   

6. Access the App
   Open your web browser and go to http://localhost:8501/ to access the Email & SMS Spam Classifier interface.

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🖼️ Software Design

Below is the software design for the Email & SMS Spam Classifier project:

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🚀 Features to be Added

Planned Enhancements

• 📈 Model Improvement: Continuously refine the spam classification model using more advanced machine learning algorithms and techniques.

• 🌍 Multi-Language Support: Expand the model to classify spam messages in multiple languages, enhancing usability for a global audience.

• 📊 Analytics Dashboard: Integrate a dashboard that provides insights into spam detection rates, types of spam messages, and user interactions.

• 🔔 Real-time Notifications: Implement a notification system that alerts users when spam messages are detected, allowing for immediate action.

• 🛠️ User Customization: Allow users to customize spam filters based on their preferences, improving the accuracy of the classifier.

• 🔒 Enhanced Security Features: Incorporate measures to ensure user data privacy and security, especially when processing sensitive information.

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🙌 Contributing

We love contributions! Help us make this repository even better by contributing to the project. Your input is highly valued. 🤗

1. Fork the Project: Click the "Fork" button at the top right of the repository page.
2. Create your Feature Branch:

   git checkout -b feature/AmazingFeature

3. Commit your Changes:

   git commit -m 'Add some AmazingFeature'

4. Push to the Branch:

   git push origin feature/AmazingFeature

5. Open a Pull Request:
   1. Navigate to the "Pull Requests" tab in the repository.
   2. Click "New Pull Request".
   3. Select your feature branch and compare it with the main branch.
   4. Add a descriptive title and comments about your changes.
   5. Click "Create Pull Request".

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✍️ Authors

• @AshimaSingh0610 - Creator & Maintainer