Spam SMS Detection Model

Overview

Our Spam SMS Detection model is a powerful solution built to identify and classify spam messages using the Naive Bayes algorithm. The accompanying Flask interface provides users with a seamless experience for submitting SMS entries, tracking usage, and receiving real-time classification results.

Key Features

1. Naive Bayes Algorithm:

   • Leveraging probabilistic classification for accurate spam detection.

2. Flask User Interface:

   • A user-friendly interface for easy interaction with the model.

3. Entry Tracking:

   • Keep track of the number of SMS entries made by the user.

4. Spam and Non-Spam Classification:

   • Provide a breakdown of entries into spam and non-spam categories.

How It Works

1. User Submission:

   • Users submit SMS messages through the Flask interface.

2. Naive Bayes Analysis:

   • The model processes the message using the Naive Bayes algorithm.

3. Real-time Classification:

   • Users receive instant feedback on the classification results.

4. Entry Tracking and Summary:

   • Track the total number of entries and provide a classification summary.

Usage

1. Clone the repository:

   git clone https://github.com/your-username/spam-sms-detection.git
   

2. Install dependencies:

   cd spam-sms-detection pip install -r requirements.txt

3. Run the flask app:

   python app.py

User Interface

Sample Prediction