🌧️ Rainfall Prediction API

A machine learning-powered REST API that predicts whether it will rain based on weather parameters. Built with FastAPI and a trained ML model, this API can be easily integrated with any frontend application (Streamlit, React, Vue, mobile apps, etc.).

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🚀 Features

• 🔮 Intelligent Rain Prediction – Binary classification (Rain / No Rain)
• ⚡ FastAPI Backend – High-performance async API with automatic Swagger UI
• 🧠 Server-Side Feature Engineering – Automatically computes derived features (e.g., temp_range)
• 🧪 Interactive Testing – Built-in Swagger documentation at /docs
• 🌐 Cloud-Ready – Deploy to Render, Railway, AWS, or any cloud platform
• 📊 Production-Ready – Optimized model loading and efficient inference

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🧠 Model Inputs

The API expects the following weather parameters:

Parameter	Type	Description	Example
pressure	float	Atmospheric pressure (hPa)	1012.5
temperature	float	Average temperature (°C)	26.0
humidity	float	Relative humidity (%)	70.0
cloud	float	Cloud cover (%)	60.0
sunshine	float	Hours of sunshine	5.0
winddirection	float	Wind direction (0–360°, 0 = North)	180.0
windspeed	float	Wind speed (km/h)	10.0
max_temp	float	Maximum temperature of the day (°C)	30.0
min_temp	float	Minimum temperature of the day (°C)	22.0

ℹ️ Note: temp_range = max_temp - min_temp is computed automatically by the API during inference.

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📦 Project Structure

rainfall-api/
│
├── main.py                  # FastAPI application
├── rainfall_model.pkl       # Trained ML model
├── requirements.txt         # Python dependencies
├── README.md               # Project documentation
├── .gitignore              # Git ignore rules
└── Dockerfile              # (Optional) Docker configuration

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🛠️ Setup & Run Locally

1️⃣ Clone the Repository

git clone https://github.com/your-username/rainfall-api.git
cd rainfall-api

2️⃣ Create a Virtual Environment (Recommended)

# On Windows
python -m venv venv
venv\Scripts\activate

# On macOS/Linux
python3 -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the API

uvicorn main:app --reload

The API will start at http://127.0.0.1:8000

5️⃣ Open Swagger UI

Visit the interactive API documentation:

👉 http://127.0.0.1:8000/docs

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🔌 API Endpoints

POST /predict

Predicts whether it will rain based on input weather parameters.

Request Body (JSON)

{
  "pressure": 1012.5,
  "temperature": 26.0,
  "humidity": 70.0,
  "cloud": 60.0,
  "sunshine": 5.0,
  "winddirection": 180.0,
  "windspeed": 10.0,
  "max_temp": 30.0,
  "min_temp": 22.0
}

Response

{
  "prediction": "Rain"
}

or

{
  "prediction": "No Rain"
}

GET /

Health check endpoint.

Response

{
  "message": "Rainfall Prediction API is running!"
}

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🧪 Testing the API

Using cURL

curl -X POST http://127.0.0.1:8000/predict \
  -H "Content-Type: application/json" \
  -d '{
    "pressure": 1012.5,
    "temperature": 26.0,
    "humidity": 70.0,
    "cloud": 60.0,
    "sunshine": 5.0,
    "winddirection": 180.0,
    "windspeed": 10.0,
    "max_temp": 30.0,
    "min_temp": 22.0
  }'

Using Python requests

import requests

url = "http://127.0.0.1:8000/predict"
data = {
    "pressure": 1012.5,
    "temperature": 26.0,
    "humidity": 70.0,
    "cloud": 60.0,
    "sunshine": 5.0,
    "winddirection": 180.0,
    "windspeed": 10.0,
    "max_temp": 30.0,
    "min_temp": 22.0
}

response = requests.post(url, json=data)
print(response.json())

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☁️ Deployment

Deploy to Render

1. Push your code to GitHub
2. Create a new Web Service on Render
3. Connect your GitHub repository
4. Set the following:
   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT
5. Deploy! 🚀

Deploy to Railway

1. Install the Railway CLI
2. Run:

   railway login
   railway init
   railway up

Deploy with Docker

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Build and run:

docker build -t rainfall-api .
docker run -p 8000:8000 rainfall-api

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🧠 How the Model Was Trained

The rainfall prediction model was trained using historical weather data with the following approach:

1. Data Collection: Weather observations including temperature, humidity, pressure, wind metrics, and rainfall records
2. Feature Engineering:
   • Created temp_range feature (max_temp - min_temp)
   • Normalized numerical features
   • Handled missing values and outliers
3. Model Selection: Tested multiple algorithms (Logistic Regression, Random Forest, XGBoost)
4. Training: Used scikit-learn with cross-validation for robust performance
5. Evaluation: Achieved strong performance on validation set using metrics like accuracy, precision, and recall
6. Serialization: Saved the trained model using joblib for efficient loading

💡 Future Improvements: Plans to integrate time-series features and ensemble methods for better accuracy.

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🧰 Tech Stack

• FastAPI – Modern, fast web framework for building APIs
• scikit-learn – Machine learning library for model training
• pandas – Data manipulation and preprocessing
• joblib – Model serialization
• uvicorn – ASGI server for production deployment

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📝 Requirements

fastapi==0.104.1
uvicorn==0.24.0
scikit-learn==1.3.2
pandas==2.1.3
joblib==1.3.2
pydantic==2.5.0

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📌 Important Notes

• ✅ Ensure feature names and order match the training data exactly
• ✅ The model is loaded once at startup for optimal performance
• ✅ API is designed to be consumed by any frontend or mobile application
• ✅ CORS is enabled for cross-origin requests (configure as needed)

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙌 Author

Aman Rawat

• 💼 LinkedIn
• 🐙 GitHub
• 📧 Email: amansinghrawat992752@gmail.com

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🌟 Support

If you found this project helpful, please give it a ⭐️!

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Built with ❤️ using FastAPI and Machine Learning