Praveen23-kk/Algerian_Fire_Prediction_Flask_app-End-to-End-ML-

# 🔥 Algerian Forest Fire Predictor (FWI Prediction) ## 📌 Project Overview This project is a machine learning web application designed to predict the **Fire Weather Index (FWI)** based on meteorological data collected from the Algerian forest regions. The application utilizes a **Ridge Regression** model trained on the *Algerian Forest Fires Dataset*. It provides a user-friendly interface built with **Flask**, allowing users to input environmental parameters (such as temperature, humidity, and wind speed) to estimate the potential fire danger intensity. --- ## 📸 Sample Result *Above: A preview of the web interface where users input weather data to receive an FWI prediction.* --- ## 📂 About the Dataset The model is trained on the **Algerian Forest Fires Dataset**, which includes data from two regions in Algeria: the **Bejaia** region and the **Sidi Bel-Abbes** region. Dataset link: Algerian Forest Fires Datas… ### Features Used for Prediction The model requires **9 input features** to generate a prediction, as defined in the training pipeline: | Feature | Description | | :--- | :--- | | **Temperature** | Maximum temperature in noon (°C) | | **RH** | Relative Humidity in % | | **Ws** | Wind Speed in km/h | | **Rain** | Total day rain in mm | | **FFMC** | Fine Fuel Moisture Code (indicates ignition potential) | | **DMC** | Duff Moisture Code (indicates fuel consumption in moderate soil layers) | | **DC** | Drought Code (indicates fuel consumption in deep soil layers) | | **ISI** | Initial Spread Index (velocity of fire spread) | | **Region** | Binary Class (0 for Bejaia, 1 for Sidi Bel-Abbes) | ### Target Variable * **FWI (Fire Weather Index):** A numerical rating of fire intensity. [cite_start]The dataset also contains a `Classes` column (fire/not fire), but this specific app predicts the continuous `FWI` value using Regression[cite: 2]. --- ## 🛠️ Technical Architecture ### Tech Stack * **Frontend:* …