Nitesh15180/Algerian-Forest-Fires

This Streamlit app predicts forest fire risk in Algeria based on factors like temperature, humidity, wind speed, and rainfall. It uses a machine learning model and a heuristic function for real-time predictions. Users can visualize historical fire data and input parameters for time-based risk evaluation. # Algerian-Forest-Fires This Streamlit app predicts forest fire risk in Algeria based on factors like temperature, humidity, wind speed, and rainfall. It uses a machine learning model and a heuristic function for real-time predictions. Users can visualize historical fire data and input parameters for time-based risk evaluation. - **Prediction:** Predict fire risk using both a trained machine learning model and a simple heuristic function. - **Historical Data Visualization:** Visualize past fire risk trends. - **Interactive UI:** Use sliders to input environmental factors and get real-time predictions. - **Time-Based Adjustment:** Adjust fire risk predictions based on the time of day (lower risk at night). ## The app will launch in your default web browser where you can: - Input various weather parameters (temperature, humidity, wind speed, rainfall, etc.). - View a historical fire risk trend visualization. - Get a prediction of fire risk based on the input data using the heuristic or ML model. ## How It Works The app predicts fire risk using both a **machine learning model** and a **heuristic approach** based on environmental factors. ### 1. **Model Prediction** The app loads a pre-trained machine learning model (`elasticcv_model.pkl`) and uses environmental data to predict the fire risk. Users input features such as temperature, humidity, wind speed, and rainfall, and the model calculates the likelihood of a fire. ## Libraries Used - Numpy - Pandas - Matplotlib & Seaborn - Scikit-learn - Streamlit