Email/SMS Spam Classification

This project implements an Email/SMS Spam Classification application using Streamlit and machine learning models trained with scikit-learn. The app can distinguish between spam and ham (legitimate) messages.

Features

• Classify SMS messages as spam or ham
• Interactive web interface built with Streamlit
• Preprocessing with NLTK (stopwords, tokenization)
• Utilizes machine learning models like Random Forest and Naive Bayes
• Final model trained with Multinomial Naive Bayes for better accuracy
• Applied knowledge of Natural Language Processing (NLP) and deep learning
• Used matplotlib.pyplot and seaborn for data visualization
• Evaluated model performance with a confusion matrix to analyze accuracy and precision
• Pickle used for model serialization

Installation

1. Clone the repository:

git clone https://github.com/AbhishekYadav915/Email-SMS_Spam_Classification.git

2. Install the required dependencies:

pip install -r requirements.txt

3. Download necessary NLTK resources:

import nltk
nltk.download('stopwords')
nltk.download('punkt')

Running the App

streamlit run index.py

File Structure

• index.py: Main application file
• requirements.txt: Dependencies for deployment

Deployment on Streamlit Cloud

1. Push the code to a GitHub repository.
2. Go to Streamlit Community Cloud.
3. Connect your repository and deploy the application.

Testing

For testing, you can use the following spam message:

Congratulations! 🎉 You’ve won a $1,000 gift card. Click here: http://fake-link.com to claim your prize now! 🚨

Live App

Access the live application here: SMS Spam Classifier

GitHub Repository

Access the code here: GitHub Repo

License

This project is licensed under the MIT License.