🏭 Production Ready Spam Detection

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📌 Project Overview

This project demonstrates an industrial-style Machine Learning pipeline for detecting spam emails.
It follows real-world ML engineering practices, covering data loading, preprocessing, model training, inference, and deployment using a Flask web application.

The system allows users to input text and receive a Spam / Not Spam prediction along with confidence insights.

📂 Project Structure

Industrial-Style-ML-Model-Demo-main/
├── Spam Detection App/
│   ├── app/                 # Flask web application (routes, controllers, UI)
│   ├── src/                 # Core ML pipeline (preprocessing, training, inference)
│   ├── data/                # Datasets and data ingestion scripts
│   ├── artifacts/           # Trained model and vectorizer artifacts
│   ├── tests/               # Unit and integration tests
│   ├── Dockerfile           # Docker configuration for containerized deployment
│   ├── requirements.txt     # Python project dependencies
│   └── README.md            # Application-level documentation

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🚀 Features

• End-to-end ML workflow (data → training → inference)
• Spam email classification
• Flask-based web UI
• Modular, production-style codebase
• Dockerized for easy deployment
• Reusable preprocessing and vectorization pipeline

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⚙️ Installation

1️⃣ Clone the Repository

git clone <repository-url>
cd Industrial-Style-ML-Model-Demo-main

2️⃣ Create a Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r "Spam Detection App/requirements.txt"

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▶️ Usage

Run the Flask Application

cd "Spam Detection App"
python app/app.py

Open your browser and navigate to:

http://127.0.0.1:5000/

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🧠 Machine Learning Pipeline

• Data Loading: data/load_data.py
• Preprocessing: src/preprocessing.py
• Vectorization: src/vectorizer.py
• Training: src/train.py
• Prediction: src/predict.py

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🐳 Docker Support

Build and run the application using Docker:

docker build -t spam-detection-app .
docker run -p 5000:5000 spam-detection-app

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🧪 Testing

Run tests from the project root:

pytest

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📦 Dependencies

Key libraries used:

• Flask
• scikit-learn
• pandas
• numpy
• joblib

See requirements.txt for the full list.

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🔧 Configuration

Model artifacts are loaded from:

Spam Detection App/artifacts/

Ensure model.pkl and vectorizer.pkl are present before running the app.

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📸 Example

Input:

Congratulations! You've won a free prize.

Output:

Spam (High Confidence)

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🛠 Troubleshooting

• Model not found: Ensure the artifacts directory contains trained files.
• Module errors: Verify your virtual environment is activated.
• Port issues: Make sure port 5000 is free.

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👨‍💻 Contributors

• @ash-iiiiish

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