Email_Spam_Detection

Email Spam Detection - Random Forest

Overview

This project is an Email Spam Detection system that uses machine learning to classify emails as either “spam” or “ham” (not spam). The system is built using a Random Forest classifier and provides a web-based interface for users to input email details and get predictions.

Features

• Dataset: A curated dataset of emails labeled as spam or ham.
• Machine Learning Model: Random Forest classifier for accurate predictions.
• Web Interface: User-friendly web app to input email details and get predictions.
• API Integration: Backend API to handle predictions.

Project Structure

Email_Spam_Detection/
├── backend/
│   ├── dataset/                                # Contains the email dataset
│   │   ├── email_dataset_full_with_email.csv   # Full dataset with labeled emails
│   ├── model/                                  # Machine learning model files
│   |   ├──spam_classifier.pkl                  # Pre-trained Random Forest model
│   ├── app.py                                  # Backend API implementation
│   ├── requirements.txt                        # Package requirements for this project
│   ├── train_model.py                          # Script to train the model
├── frontend/
│   ├── index.html                              # HTML templates for the web app
│   ├── style.css                               # CSS styles for the web app
├── README.md                                   # Project documentation

Installation

1. Clone the repository:

   git clone https://github.com/amudhan-mohan/Email_Spam_Detection.git
   cd Email_Spam_Detection
   

2. Install dependencies:

   pip install -r requirements.txt
   

Usage

1. Train the Model: Run the following command to train the Random Forest model. This will generate the spam_classifier.pkl file in the backend/model/ directory.

    python backend/train_model.py
   

2. Start the Backend Server: Launch the Flask backend server to handle API requests.

    python backend/app.py
   

3. Access the Web App: Open your browser and navigate to http://localhost:5000 to access the web interface.
4. Make Predictions: Enter the email details (email ID, subject, and body) in the web app and click “Submit” to classify the email as spam or ham.
5. Public Web App: Alternatively, access the public version of the web app at: Public Web App Link

Dataset

The dataset is located in backend/dataset/email_dataset_full_with_email.csv. It contains labeled examples of spam and ham emails.

Model

The Random Forest model is trained on the dataset and saved in the backend/model/ directory. The model is loaded during runtime to make predictions.

Model Performance

📊 Model Accuracy: 93.18%

📋 Classification Report:

              precision    recall  f1-score   support

         Ham       0.95      0.91      0.93        23
        Spam       0.91      0.95      0.93        21

    accuracy                           0.93        44
   macro avg       0.93      0.93      0.93        44
weighted avg       0.93      0.93      0.93        44

Contributing

Contributions are welcome! Feel free to submit issues or pull requests to improve the project.

License

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