📧 Email / SMS Spam Classifier

🚀 Project Overview

• This project is a Machine Learning based web application that classifies Email or SMS messages as Spam or Not Spam.
• It uses Natural Language Processing (NLP) techniques to preprocess the text and a Multinomial Naive Bayes model to detect spam messages.
• The application is deployed using Streamlit, making it easy for anyone to use without coding.

📝 Problem Statement

• Spam messages are unwanted and can cause security risks. This project aims to automatically classify messages as Spam or Not Spam, helping users filter unwanted messages and improve communication efficiency.

🛠 Technologies Used

• Python – Programming language
• Pandas – Data manipulation
• NumPy – Numerical operations
• NLTK – Natural Language Processing (tokenization, stopword removal, stemming)
• Scikit-learn – Machine Learning library
• TF-IDF Vectorizer – Feature extraction from text
• Multinomial Naive Bayes – Machine Learning model for classification
• Streamlit – Web app deployment

📊 Model Details

Text Preprocessing:

• Convert text to lowercase
• Tokenization (splitting text into words)
• Stopword removal (removing common words like “is”, “the”)
• Stemming (reducing words to their root form)

Feature Extraction:

• Using TF-IDF Vectorizer to convert text into numerical features

Model Training:

• Tested multiple models

Best Model:

• Multinomial Naive Bayes

Evaluation Metrics:

• Accuracy: Measures overall correctness
• Precision: Measures how many predicted spam messages are actually spam

⚡ How to Run the Code

• 1. Clone the repository or download the ZIP file
• 2. Install required packages:
     • pip install pandas numpy nltk scikit-learn streamlit
• 3. Run the Streamlit app:
     • streamlit run app.py
• 4. Open the web app in your browser
• 5. Enter any Email or SMS text and click Predict to see if it’s Spam or Not Spam