HARSHITHA994862/test-for-algeria-forest-fire-

# Algerian Forest Fire Prediction using Machine Learning A Machine Learning based web application that predicts the **Fire Weather Index (FWI)** using meteorological and environmental features from the Algerian Forest Fires dataset. The application uses a trained **Ridge Regression** model to estimate the fire weather index based on user-provided weather conditions. --- ## Features - Predicts Fire Weather Index (FWI) - Data Cleaning and Preprocessing - Feature Scaling using StandardScaler - Ridge Regression Model - Interactive Flask Web Application - Real-time Prediction - User-Friendly Interface --- ## Technologies Used ### Programming Language - Python ### Machine Learning - Scikit-learn - Ridge Regression ### Backend - Flask ### Frontend - HTML - CSS ### Libraries - Pandas - NumPy - Matplotlib - Seaborn - Joblib --- ## Project Structure ``` Algerian-Forest-Fire-Prediction/ │ ├── .ebextensions/ │ └── python.config ├── templates/ │ ├── home.html │ └── index.html ├── Algerian_forest_fires_dataset.csv ├── Algerian_forest_fires_cleaned_dataset.csv ├── app.py ├── data cleaning.ipynb ├── model_training.ipynb ├── ridge.pkl ├── scaler.pkl ├── require.txt └── README.md ``` --- ## Workflow ``` Weather Parameters │ ▼ Data Cleaning │ ▼ Feature Scaling │ ▼ Ridge Regression Model │ ▼ FWI Prediction ``` --- ## Dataset The project uses the **Algerian Forest Fires Dataset**, which contains weather observations collected from two regions of Algeria. ### Input Features - Temperature - Relative Humidity (RH) - Wind Speed (Ws) - Rain - FFMC - DMC - ISI - Classes - Region ### Target Variable - Fire Weather Index (FWI) --- ## Machine Learning Pipeline 1. Load the dataset 2. Data Cleaning 3. Handle Missing Values 4. Feature Selection 5. Feature Scaling 6. Train-Test Split 7. Train Ridge Regression Model 8. Evaluate Model Performance 9. Sav …