BrightSarpong/skywise-weather-prediction-model

A local weather prediction model that provides accurate weather conditions in any given location in Ghana # Weather Prediction Website A simple web application that uses a trained machine learning model to predict weather conditions based on location input. ## Features - **Location Input**: Enter any city, state, or country name - **Geocoding**: Automatically converts location names to coordinates - **Weather Prediction**: Uses a trained joblib model to predict weather conditions - **Modern UI**: Clean, responsive design with real-time feedback - **Error Handling**: Comprehensive error handling for invalid locations and server issues ## Files Structure ``` My weather/ ├── app.py # Flask backend application ├── templates/ │ └── index.html # Frontend HTML template ├── combined_weather_models_geo.joblib # The trained ML model ├── requirements.txt # Python dependencies └── README.md # This file ``` ## Installation 1. Install the required dependencies: ```bash pip install -r requirements.txt ``` 2. Run the application: ```bash python app.py ``` 3. Open your web browser and navigate to: ``` localhost ``` ## Usage 1. Enter a location in the input field (e.g., "New York", "London", "Tokyo") 2. Click "Predict Weather" button 3. View the prediction results including: - Location name - Coordinates (latitude, longitude) - Predicted weather condition ## API Endpoints - `GET /` - Main page with the web interface - `POST /predict` - Weather prediction endpoint - `GET /health` - Health check endpoint ## Model Requirements The application expects your model (`combined_weather_models_geo.joblib`) to: - Accept input features as [latitude, longitude] - Return a weather condition prediction If your model requires different input features, you may need to modify the `input_features` preparation in `app.py`. ## Troubleshooting - **Model not loading**: Ensure `combined_weather_models_geo.joblib` is in the same directory as `app.py` - **Location not found**: Try using more specific …