Somrat390/Algerian-Forest-Weather-Index-prediction

# 🌲 Algerian Forest Fire Weather Index Prediction This project predicts the **Fire Weather Index (FWI)** for the Algerian Forest dataset using a **Ridge Regression Model**. It is deployed on **Render** as a Flask web application. --- ## 📊 Dataset - **Source:** Kaggle - Algerian Forest Fires Dataset - **Features include:** - 🌡️ Temperature (°C) - 💧 Relative Humidity (%) - 🌬️ Wind Speed (km/h) - 🌧️ Rain (mm) - 🔥 Fire Weather Codes → FFMC, DMC, ISI - 🏷️ Classes (0 = No Fire, 1 = Fire) - 🗺️ Region (Bejaia / Sidi-Bel Abbes) --- ## ⚙️ Tech Stack - **Python 3** - **Flask** (Web Framework) - **Scikit-Learn** (ML Model) - **NumPy, Pandas** (Data Processing) - **Render** (Deployment) --- ## 🚀 Deployment The project is deployed on **Render** and can be accessed here: 🔗 **Live Demo:** algerian-forest-weather-ind… --- ## 🖥️ Local Setup Follow these steps to run the project locally: ### 1️⃣ Clone the Repository ```bash git clone github.com cd Algerian-Forest-Weather-Index-prediction ```` ### 2️⃣ Create Virtual Environment & Install Dependencies ```bash python -m venv venv source venv/bin/activate # For Mac/Linux venv\Scripts\activate # For Windows pip install -r requirements.txt ``` ### 3️⃣ Run the Flask App ```bash python application.py ``` Your app will start at **127.0.0.1 --- ## 📌 Example Prediction **Input Example:** | Feature | Value | | ----------- | ---------- | | Temperature | 25°C | | RH | 40% | | Ws | 15 km/h | | Rain | 0 mm | | FFMC | 85 | | DMC | 120 | | ISI | 10 | | Classes | 1 (Fire) | | Region | 1 (Bejaia) | **Output Example:** 🔥 **Predicted Fire Weather Index:** `42.31` --- ## 📂 Project Structure ``` ├── Model/ │ ├── ridge.pkl │ ├── scaler.pkl ├── templates/ │ ├── index.html │ ├── home.ht …