🌦️ Weather Prediction using Machine Learning & FastAPI

A production-ready Weather Prediction API built using Python, Scikit-learn, and FastAPI. The project predicts the average temperature based on historical weather and geographical features using a machine learning pipeline.

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📌 Features

• Predicts Average Temperature
• End-to-End Machine Learning Pipeline
• Automatic Data Preprocessing
• One-Hot Encoding using ColumnTransformer
• Linear Regression Model
• FastAPI REST API
• Interactive Swagger Documentation
• Production-ready Model Serialization with Joblib

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🛠️ Tech Stack

Category	Technologies
Language	Python 3.x
Machine Learning	Scikit-learn
Data Processing	Pandas, NumPy
API Framework	FastAPI
Model Storage	Joblib
Server	Uvicorn
Validation	Pydantic

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📂 Project Structure

weather-prediction-utils/
│
├── api/
│   ├── main.py
│   ├── predictor.py
│   └── schema.py
│
├── data/
│
├── models/
│   └── weather_prediction_model.pkl
│
├── src/
│   └── weather_ai/
│
├── train_model.py
├── README.md
└── LICENSE

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📊 Dataset Features

The model is trained using the following features:

Feature
Minimum Temperature
Maximum Temperature
Wind Speed
Air Pressure
Elevation
Latitude
Longitude
Rainfall
Season
Station Name
State
District
Year
Month Number
Day

Target Variable:

• Average Temperature

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⚙️ Machine Learning Pipeline

The project uses a Scikit-learn Pipeline.

Raw Data
     │
     ▼
Feature Engineering
     │
     ▼
ColumnTransformer
     │
     ├── Numerical Features
     └── OneHotEncoder
     │
     ▼
Linear Regression
     │
     ▼
Model Serialization (.pkl)

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📈 Model Performance

Metric	Value
MAE	0.8788
MSE	2.0237
RMSE	1.4226
R² Score	0.9315

The model explains approximately 93.15% of the variance in the dataset.

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🚀 Installation

Clone the repository

git clone https://github.com/your-username/weather-prediction-utils.git

Move into the project directory

cd weather-prediction-utils

Create a virtual environment

python -m venv .env

Activate the virtual environment

Windows

.env\Scripts\activate

Linux / macOS

source .env/bin/activate

Install dependencies

pip install -r requirements.txt

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▶️ Train the Model

python train_model.py

The trained model will be saved in:

models/weather_prediction_model.pkl

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▶️ Run the FastAPI Server

python -m uvicorn api.main:app --reload

Open your browser:

http://127.0.0.1:8000/docs

Swagger UI will open automatically.

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📩 Example API Request

{
  "min_temp": 25.5,
  "max_temp": 34.2,
  "wind_speed": 8.5,
  "air_pressure": 1012.3,
  "elevation": 45,
  "latitude": 20.2961,
  "longitude": 85.8245,
  "rainfall": 2.5,
  "season": "Summer",
  "station_name": "Bhubaneswar",
  "state": "Odisha",
  "district": "Khordha",
  "year": 2025,
  "month_number": 7,
  "day": 27
}

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📤 Example Response

{
  "Predicted Average Temperature": 29.49
}

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🧠 Future Improvements

• Random Forest Regressor
• XGBoost Regressor
• Feature Importance Analysis
• Model Versioning
• Docker Support
• CI/CD Pipeline
• Cloud Deployment
• Real-Time Weather Data Integration
• Logging & Monitoring
• Automated Retraining Pipeline

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👨‍💻 Author

Devidutta Das

Founder – CodeUdaan

• AI & Machine Learning Enthusiast
• FastAPI Developer
• Machine Learning Engineer

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📜 License

This project is licensed under the MIT License.

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⭐ Support

If you found this project useful:

• ⭐ Star the repository
• 🍴 Fork the project
• 🛠️ Contribute improvements
• 📢 Share it with the community

Happy Coding! 🚀