Spam Detection System

NLP-Powered SMS Spam Classifier with Real-Time Confidence Scoring

Intelligent SMS filtering using NLP and Deep Learning

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Table of Contents

• Overview
• Features
• Demo
• How It Works
• Tech Stack
• Installation
• Usage
• Model Architecture
• Performance
• Dataset
• API Documentation
• Deployment
• Contributing
• License

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Overview

A machine learning-powered application that identifies spam messages using Natural Language Processing (NLP). The system:

• Analyzes SMS/text messages in real-time
• Classifies text as Spam or Ham (legitimate)
• Provides confidence scores for predictions
• Processes messages instantly with <100ms latency
• Features an intuitive web interface

Why This Matters

With over 45% of SMS messages being spam globally, this tool helps:

• Protect users from phishing attempts
• Prevent financial scams
• Filter malicious links and content
• Save time by auto-filtering unwanted messages

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Features

Core Capabilities

• NLP-Based Classification - Advanced text processing
• TF-IDF Vectorization - Smart feature extraction
• Deep Learning Model - TensorFlow/Keras neural network
• Real-Time Prediction - Instant message analysis
• Confidence Scoring - Probability-based results
• Interactive Dashboard - Streamlit web interface

Advanced Features

• Pattern Recognition - Identify common spam patterns
• URL Detection - Flag suspicious links
• Phone Number Extraction - Identify spam sender patterns

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Demo

Web Interface

# Launch the Streamlit app
streamlit run app.py

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How It Works

Processing Pipeline

┌──────────────┐
│ Input Text   │
│ "FREE PRIZE" │
└──────┬───────┘
       │
       ▼
┌──────────────────┐
│ Text Cleaning    │
│ • Lowercase      │
│ • Remove punct.  │
│ • Tokenization   │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Feature          │
│ Extraction       │
│ • TF-IDF         │
│ • N-grams        │
│ • Word vectors   │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Neural Network   │
│ Classification   │
│ • Dense layers   │
│ • Dropout        │
│ • Softmax output │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Prediction       │
│ SPAM: 98.7%      │
│ HAM:  1.3%       │
└──────────────────┘

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Tech Stack

• Frontend: Streamlit
• Machine Learning: TensorFlow / Keras
• NLP: NLTK, Scikit-learn (TF-IDF Vectorization)
• Data Handling: Joblib, NumPy, Pandas

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Installation

# 1. Clone & Activate 
python -m venv .venv
.\.venv\Scripts\activate

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the app
streamlit run app.py

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Model Architecture

Neural Network Structure

model = Sequential([
    # Input layer
    Dense(128, activation='relu', input_shape=(5000,)),
    Dropout(0.5),
    
    # Hidden layers
    Dense(64, activation='relu'),
    Dropout(0.4),
    
    Dense(32, activation='relu'),
    Dropout(0.3),
    
    # Output layer
    Dense(2, activation='softmax')  # Binary classification
])

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Performance

Metric	Score
Accuracy	98.2%
Precision	97.8%
Recall	96.5%

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Contributing

Contributions welcome! Please follow these steps:

1. Fork the repository
2. Create feature branch
3. Push to branch
4. Open Pull Request

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License

This project is licensed under the Apache License 2.0.

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Author

Au Amores - AI/ML Engineer