This project is a Spam Mail Detector that classifies emails as Spam or Not Spam using Machine Learning and Natural Language Processing (NLP). It is implemented in a Jupyter Notebook using Google Colab.
✅ Preprocesses email text (removing stopwords, punctuation, etc.)
✅ Extracts important text features using TF-IDF or CountVectorizer
✅ Trains a machine learning model to classify emails as spam or not
✅ Evaluates the model using accuracy, precision, recall, and F1-score
SpamMailDetection.ipynb→ Main Jupyter Notebook with all code (preprocessing, training, evaluation)requirements.txt→ List of Python libraries required to run the project
This project is trained on a labeled dataset containing email messages marked as spam or non-spam. The dataset undergoes preprocessing for better accuracy.
- Python
- Pandas, NumPy (Data Processing)
- NLTK, Scikit-learn (Text Processing & Machine Learning)
- Jupyter Notebook / Google Colab
The model is evaluated using accuracy, precision, recall, and F1-score to measure classification effectiveness.
🔹 Improve accuracy with deep learning models (LSTMs, Transformers, etc.)
🔹 Deploy the model as a web-based spam detector
🔹 Integrate with an email API for real-time classification