abhimanyu726/Algerian_forest_fire_pred

# Algerian Forest Fire Prediction ## Overview This project aims to predict forest fires in Algeria using machine learning models. By analyzing environmental factors and historical data, this tool helps identify potential fire hazards and provides insights for effective fire management. ## Features - Data preprocessing and feature engineering for forest fire prediction - Machine learning models to predict fire occurrence based on environmental features - Visualization of model performance - User-friendly interface with Streamlit for interactive predictions ## Dataset The dataset used is the **Algerian Forest Fires dataset**, containing information on weather conditions and fire incidents across different regions in Algeria. ## Installation 1. **Clone the Repository** ```bash git clone github.com cd Algerian_forest_fire_pred 2. **Install Required Packages** Install the dependencies using pip: ```bash pip install -r requirements.txt 3. **Set Up Environment Variables** If using external APIs or storing sensitive data, create a .env file with required API keys or configurations. ## Usage 1. Run the Jupyter Notebook Open and run the Jupyter notebook forest_fire_prediction.ipynb for data exploration, model training, and evaluation. ```bash Copy code jupyter notebook forest_fire_prediction.ipynb 2. Streamlit Interface For an interactive web-based prediction tool, use the Streamlit app: ```bash Copy code streamlit run app.py 3. Input Features Region: The specific region in Algeria Temperature: Temperature (in °C) Humidity: Relative humidity (percentage) Wind Speed: Speed of wind (km/h) Rainfall: Rainfall (mm) The model will predict the likelihood of fire occurrence based on these inputs. ## Model The project employs several machine learning algorithms to achieve optimal predictions. Models used include: Logistic Regression Decision Tree Classifier Random Forest Classifier Each model’s performance is evalua …