yashh-alpha/predictforestfires

Algerian forest fires prediction using ML model, made with Flask and deployed on Render. # Forest Fire Prediction Web App ## Overview This project is a machine learning-based web application that predicts the likelihood of forest fires using environmental and meteorological inputs. The model is trained on historical data and deployed using a Flask backend. The application allows users to input parameters such as temperature, humidity, wind speed, and rainfall, and returns a prediction indicating the fire risk. --- ## Live Demo predictforestfires-ga2w.onr… Note: The application may take a few seconds to load initially due to free hosting limitations. --- ## Features * Predict forest fire risk based on user inputs * End-to-end machine learning pipeline * Flask-based web deployment * Simple and interactive user interface * Deployed on cloud (Render) --- ## Tech Stack * Python * NumPy * Pandas * Scikit-learn * Flask * Gunicorn --- ## Project Structure ```id="r8lbw6" predictforestfires/ │ ├── app.py # Flask application ├── model.pkl # Trained machine learning model ├── requirements.txt # Dependencies ├── Procfile # Deployment configuration ├── templates/ │ └── index.html # Frontend UI └── README.md ``` --- ## Installation 1. Clone the repository ```id="ocd6bs" git clone github.com cd predictforestfires ``` 2. Create a virtual environment (optional) ```id="e5p4rs" python -m venv venv venv\Scripts\activate ``` 3. Install dependencies ```id="y9n9pv" pip install -r requirements.txt ``` --- ## Running the Application Start the Flask server locally: ```id="3th1ut" python app.py ``` Open in browser: ```id="c5cy0s" 127.0.0.1 ``` --- ## Usage 1. Enter input values: * Temperature * Relative Humidity * Wind Speed * Rain 2. Click **Predict** 3. The model will output the predicted forest fire risk. --- ## Deployment This application is deployed using Render. To deploy: * Add `gunicorn` to requirements.txt * …