A simple machine learning API built with Flask that serves predictions from a Linear Regression model.

This project demonstrates a basic machine learning pipeline with model training and serving via REST API. It uses scikit-learn to train a Linear Regression model on synthetic data and Flask to create a web API for making predictions.

• Model Training: Train a Linear Regression model on synthetic data
• REST API: Flask-based API for serving predictions
• Error Handling: Comprehensive error handling for API requests
• Model Persistence: Save and load trained models using joblib

simple_ml_api/
├── app.py              # Flask API server
├── train.py            # Model training script
├── requirements.txt    # Python dependencies
├── model.joblib        # Trained model (generated after running train.py)
└── README.md          # This file

• Python 3.7 or higher
• pip package manager

1. Clone or download the project

   cd simple_ml_api

2. Install dependencies

   pip install -r requirements.txt

3. Train the model

   python train.py

   This will:

   • Generate synthetic training data
   • Train a Linear Regression model
   • Save the model as model.joblib

4. Start the API server

   python app.py

   The API will be available at http://localhost:5000

Welcome endpoint that returns a greeting message.

Response:

Welcome to the Simple ML API! Use /predict to get predictions.

Make predictions using the trained model.

Request Format:

{
    "features": [value]
}

Example Request:

curl -X POST http://localhost:5000/predict \
  -H "Content-Type: application/json" \
  -d '{"features": [5]}'

Example Response:

{
    "prediction": [6.0]
}

import requests
import json

# Make a prediction
url = "http://localhost:5000/predict"
data = {"features": [7]}
response = requests.post(url, json=data)
result = response.json()
print(f"Prediction: {result['prediction'][0]}")

• Algorithm: Linear Regression
• Training Data: Synthetic data with 10 samples
• Features: Single feature (X values from 1 to 10)
• Target: Approximately linear relationship with some noise
• Model File: Saved as model.joblib using joblib

The trained model learns a linear relationship: y ≈ 1.0 * x + 1.0

The API includes comprehensive error handling for:

• Model not found: Returns 500 error if model.joblib doesn't exist
• Invalid JSON format: Returns 400 error for malformed requests
• Missing features: Returns 400 error if 'features' key is missing
• General exceptions: Returns 500 error with descriptive message

The app runs in debug mode by default when using python app.py. This enables:

• Auto-reload on code changes
• Detailed error messages
• Debug toolbar

For production deployment, consider using a WSGI server like Gunicorn:

pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 app:app

• Flask: Web framework for the API
• scikit-learn: Machine learning library
• joblib: Model serialization
• numpy: Numerical computing
• gunicorn: WSGI server (for production)

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

This project is open source and available under the MIT License.

• Add data validation and preprocessing
• Implement multiple model support
• Add model versioning
• Include unit tests
• Add logging and monitoring
• Support batch predictions
• Add model retraining endpoints
• Implement authentication and rate limiting