This project focuses on building a machine learning model to classify emails as either spam or ham (non-spam). The model utilizes text data from emails and applies various machine learning algorithms to accurately categorize them.
Spam emails are a significant issue in today's digital world. They clog inboxes, waste time, and can even pose security threats through phishing attacks. An effective spam classification system can automatically filter out unwanted emails, improving user experience and protecting against potential risks.
Efficiency: Automating the spam detection process saves time and effort for users, allowing them to focus on important emails.
Identifying and filtering out malicious emails can prevent phishing and other cyber threats. Productivity: Reducing the number of spam emails in an inbox helps users manage their communications more effectively.
A robust spam detection system can be scaled to handle large volumes of email traffic, making it suitable for both individual users and large organizations.
Data Preprocessing: Clean and prepare email text data for analysis. Feature Extraction: Extract relevant features from email content using techniques like TF-IDF and word embeddings. Model Training: Train various machine learning models (e.g., Naive Bayes, SVM, Neural Networks) on labeled email data.
Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score. Deployment: Deploy the model using a web-based application with a Flask backend for real-time spam classification. Installation and Usage Prerequisites Python 3.x Flask Scikit-learn NLTK NumPy Pandas
Clone the repository: bash Copy code git clone https://github.com/AyambaSumaila/spam-email-classification.git Navigate to the project directory: bash Copy code cd spam-email-classification
bash Copy code pip install -r requirements.txt Usage Run the Flask application: bash Copy code python app.py Open your web browser and go to http://localhost:5000. Upload an email file or paste the email content to classify it as spam or ham.
Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure your code follows the project's coding standards and include appropriate tests.