SMS Spam Detection

Kaggle

Find my work on kaggle : Kaggle link

Overview

This application leverages multiple machine learning models to accurately classify SMS messages as either spam or ham (non-spam). The application provides an interactive interface for users to input SMS text to receive instant predictions. It also includes a detailed analysis section showcasing the performance metrics of each deployed model.

Features

• Spam Prediction: Users can input an SMS text and get predictions on whether the message is spam or ham.
• Model Performance: Displays detailed performance metrics for each model, including accuracy, classification reports, and confusion matrices.

Models Used

• Logistic Regression
• Support Vector Machine (SVM)
• Random Forest Classifier
• Gradient Boosting Classifier
• Multinomial Naive Bayes

Installation

To set up and run this application locally, follow the steps below:

1. Clone the Repository

git clone https://github.com/junioralive/sms-spam-detection.git
cd sms-spam-detection

2. Create and Activate a Virtual Environment

For Windows:

python -m venv venv
venv\Scripts\activate

For macOS and Linux:

python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Download NLTK Resources

Before running the application, download the required NLTK resources by executing the following Python commands:

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

Running the App

To run the app, use the following command in the project directory:

streamlit run app.py

Visit http://localhost:8501 in your web browser to interact with the application.

Contributing

Contributions are welcome! Here are a few ways you can help improve the project:

• Report bugs.
• Propose new features.
• Submit pull requests for bug fixes or new functionalities.
• Improve documentation.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.