SMS Spam Classifier

This project is an SMS Spam Classifier built using a Naive Bayes classifier. The application is developed with Streamlit, allowing users to easily interact with the model and classify SMS messages as spam or not spam.

Table of Contents

• Features
• Technologies Used
• Installation
• How It Works

Features

• User-friendly interface to input SMS messages.
• Real-time classification of messages as spam or ham (not spam).
• Visualizations of model performance metrics.
• Built with a Naive Bayes classifier for efficient spam detection.

Technologies Used

• Python
• Streamlit
• Scikit-learn
• Pandas
• NLTK (Natural Language Toolkit)
• NumPy

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/chauhan-pratham/SMS_Spam_Classifier
   

2. Open Your Command Line Interface (CLI):

   • On Windows, you can use Command Prompt or PowerShell.
   • On macOS or Linux, you can use the Terminal.

3. Change Directory to the Repository: After cloning, you need to navigate into the repository's directory. Use the cd (change directory) command:

   cd SMS_Spam_Classifier

   Additional Tips

• List Files: Once you are in the repository, you can list the files and directories using:

  ls  # On macOS/Linux
  dir # On Windows

• Check Current Directory: If you want to confirm that you are in the correct directory, you can use:

  pwd  # On macOS/Linux
  cd   # On Windows

• Return to Previous Directory: If you need to go back to the previous directory, you can use:

  cd ..

4. Install the required packages:

   pip install -r requirements.txt

5. Run the Streamlit application:

   streamlit run app.py

Note: When you type this web-browser will open and will load you app.

6. Enter an SMS message in the input box and click the "Predict" button to see if the message is spam or not.

How It Works

1. Data Collection: The model is trained on a dataset of SMS messages labeled as spam or ham.
2. Preprocessing: The text data is cleaned and preprocessed using techniques such as tokenization, stopword removal, and vectorization.
3. Model Training: A Naive Bayes classifier is trained on the preprocessed data.
4. Prediction: The trained model predicts whether a new SMS message is spam or not based on user input.