This project uses machine learning techniques to classify emails as either spam or ham (non-spam). It provides an easy-to-use interface powered by Streamlit, where users can input email content and instantly receive predictions.
The goal is to detect spam emails with high accuracy using TF-IDF vectorization and logistic regression. This tool helps users quickly determine if an email is spam, improving productivity and reducing the risk of phishing attacks.
- 📝 TF-IDF Vectorization: Converts email text into numerical features suitable for machine learning.
- 📊 Logistic Regression: A classification algorithm used to distinguish between spam and ham emails.
- 🌐 Streamlit App: Provides a web interface for users to input text and view predictions in real-time.
- app.py: Contains the code for the Streamlit web application.
- ML.ipynb: Jupyter notebook with the machine learning logic, including preprocessing, model training, and evaluation.
- ML.py: Script version of the Jupyter notebook for direct import into the Streamlit app.
- requirements.txt: Lists all the Python modules required to run the project.
- summary.txt: Provides a step-by-step summary of how the application works.
https://spam-ham-classifier-akash.streamlit.app/
Created with ❤️ by Akash Anandani @[email protected]