Swapnaneel1616/Forest_Fire_Detection

This is a machine learning web application built with Flask and Scikit-learn that predicts the Fire Weather Index (FWI) based on meteorological data from the Algerian Forest Fires dataset. # Forest Fire FWI Prediction This is a machine learning web application built with **Flask** and **Scikit-learn** that predicts the **Fire Weather Index (FWI)** based on meteorological data. ## 📋 About The Project This project deploys a trained **Ridge Regression** model as a web service. A user can input weather observations, and the model will predict the FWI, which is a key indicator of fire intensity and danger. The model was trained on the **Algerian Forest Fires Dataset**, which includes data from two regions: Bejaia and Sidi Bel-abbes. ### Key Features (Inputs) The model uses 9 features to make a prediction: * **Temperature**: Temperature in Celsius * **RH**: Relative Humidity (%) * **Ws**: Wind Speed (km/h) * **Rain**: Rain (mm) * **FFMC**: Fine Fuel Moisture Code * **DMC**: Duff Moisture Code * **ISI**: Initial Spread Index * **Classes**: A binary (1 for fire, 0 for no fire) used as an input feature * **Region**: A binary (1 for Sidi-Bel Abbes, 0 for Bejaia) --- ## 🚀 How It Works The application logic is handled by `application.py`: 1. **User Interface**: A user accesses the root route (`/`) and is shown `index.html`, which contains a form. 2. **Data Submission**: The user fills in the 9 features and submits the form, which sends a `POST` request to the `/predictdata` endpoint. 3. **Data Preprocessing**: The app loads the saved `StandardScaler` (`scaler.pkl`) and uses it to scale the user's input data. This is crucial as the model was trained on scaled data. 4. **Prediction**: The scaled data is fed into the pre-trained `Ridge` regression model (`ridge.pkl`). 5. **Display Result**: The model's prediction (a single FWI value) is returned and rendered on the `home.html` page. --- ## 🛠️ Technology Stack * **Backend**: Flask * **Machine Learning**: Scikit-learn * **Data Handling**: NumPy, Pandas * **Models**: Linear Regression , Ridge Regression , Elasticnet Regularisation * **Model Deployment**: `pickle` --- ## 📂 Project Structure