Kushagra976/Algerian_forest_fires_dataset_model

# Algerian Forest Fires – FWI Prediction Model This project predicts the **Fire Weather Index (FWI)** using meteorological and fire-related features from the **Algerian Forest Fires dataset**. It implements **regularized regression models** and serves predictions through a **Flask web application**. --- ## Project Overview Forest fires depend on several environmental factors such as temperature, humidity, wind speed, and fire indices. This project focuses on: - Building a regression model to predict **FWI** - Applying **feature scaling and regularization** - Deploying the model using **Flask** --- ## Machine Learning Details - **Models used:** - Ridge Regression - Lasso Regression - ElasticNet Regression - **Final selected model:** Ridge Regression - **Preprocessing:** StandardScaler - **Target variable:** Fire Weather Index (FWI) --- ## 📊 Input Features The model uses the following inputs: - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - ISI - Classes (Fire / No Fire) - Region (Bejaia / Sidi-Bel Abbes) --- ## 📁 Project Structure ``` Algerian_fires_dataset_model/ │── application.py │── requirements.txt │── .gitignore │── README.md │ ├── models/ │ ├── ridge.pkl │ └── scaler.pkl │ ├── templates/ │ ├── home.html │ └── index.html │ └── notebooks/ ├── Algerian_forest_fires_dataset_UPDATE.csv └── Algerian_forest_fires_cleaned.csv ```