Email Spam Detection API

Use Case

The Email Spam Detection system classifies emails as Spam or Not Spam using Natural Language Processing (NLP) techniques. This helps in filtering unwanted emails and improving inbox organization by leveraging Machine Learning models trained on a labeled spam dataset.

Dataset

The model is trained using the Spam Dataset, which consists of labeled email samples categorized as:

• Spam (1)
• Not Spam (0) The dataset is preprocessed using NLP techniques such as tokenization, stop-word removal, and TF-IDF vectorization.

📁 Directory Structure

spam-classifier-api/
│── hyperparameter_tuning/           # Work done by team to optimize various hyperparameters 
│   ├── abhishek/                    # Contributions by Abhishek
│   ├── archita/                      # Contributions by Archita
│   ├── narayan/                      # Contributions by Narayan
│   │   ├── hyperparameter_logging.png      # Logging of hyperparameter tuning
│   │   ├── model_accuracy.png              # Accuracy results of tuning
│   │   ├── overall_tuning_mlflow_setup.png # MLflow setup visualization
│   │   ├── response.json                   # JSON response of tuning results
│   ├── shivraj/                      # Contributions by Shivraj
│
│── model/                             # Directory for trained model artifacts
│── spam-classifier-fe/                 # Front-end application for spam classification
│── app.py                               # Root file of the classifier
│── requirements.txt                      # Dependencies and package requirements
│── train_model.py                        # Model training file

Getting Started

Prerequisites

Make sure you have Docker installed on your machine.

Build and Start the Application

Run the following command to build and start the application using Docker:

docker-compose up --build --remove-orphans

Application UI

Once the application is running, access the Spam Classifier UI at:
🔗 http://localhost:3000/

🔗 API Endpoints & Usage

Health Check

• Endpoint: /health
• Method: GET
• Response:

  {
    "status": "Healthy"
  }

• Description: Checks if the API is running.

Predict Email Spam

• Endpoint: /predict
• Method: POST
• Response:

 {
    "email": "Congratulations! You have won a free gift card."
}

• Response:

{
  "result": "Spam"
}

• Description: Takes an email as input and predicts whether it is Spam or Not Spam.

Train the Model

• Endpoint: /train
• Method: GET
• Response:

  {
  "message": "Model training completed!"
}

• Description: Triggers model training using the dataset.

Get Best Hyperparameters

• Endpoint: /best-params
• Method: GET
• Response:

  {
  "best_params": {
    "C": 1.0,
    "solver": "lbfgs"
  }
}

• Description: Returns the best hyperparameters for the model after hyperparameter tuning.

Curl Commands for each APIs

curl -X GET http://localhost:9000/health


curl -X POST http://localhost:9000/predict \
  -H "Content-Type: application/json" \
  -d '{"email": "Congratulations! You have won a free gift card."}'

curl -X GET http://localhost:9000/train


curl -X GET http://localhost:9000/best-params

Technologies Used

• Python - Core programming language used for model development.
• Flask - Lightweight web framework for building the API.
• Docker & Docker Compose - Containerization for easy deployment and scalability.
• Scikit-learn - Machine learning library used for training and classification.
• NLTK & TF-IDF - Natural language processing tools for text preprocessing.
• MLflow - Experiment tracking and hyperparameter tuning for model optimization.

Contributors

• Narayan Khanna
• Abhishek
• Archita
• Shivraj

Contact

For any queries, reach out via email or GitHub Issues.